--- Input for META at 20250925_035322 ---
================================== Ai Message ==================================

- META THOUGHTS:
  - The recent experiment shows a severe regression: FooPlayer now loses heavily (8 wins vs 22). We previously added selective 2-ply and then later added _evaluate_action; the crash earlier (AttributeError) was fixed, but performance dropped. That suggests the new 2-ply pipeline, sampling biases, or evaluation fallbacks are causing the agent to select poor actions (or to frequently fall back to unsafe defaults).
  - Fast priorities: (1) stabilize decision pipeline so it reliably uses the 1-ply + spectrum evaluator (which gave good results earlier), (2) widen the 1-ply search to recover missed actions (roads/expansion), (3) add diagnostics to locate where the deepening pipeline fails (frequent fallbacks, spectrum failure, etc.), and (4) only re-enable selective 2-ply after we validate the building blocks.
  - I will propose a conservative, targeted plan that (A) temporarily disables/limits 2-ply, (B) expands 1-ply sampling budget and biases sampling to include roads (early/mid game), (C) adds detailed debug counters/logging for adapter usage and fallback frequency, and (D) includes a short debug experiment plan so we can diagnose the remaining issues before reintroducing full 2-ply.

- META GOAL:
  - Restore and improve win rate quickly by making the agent rely on a stronger, broader 1-ply evaluation (with spectrum) while gathering diagnostic traces to find why the 2-ply path underperformed. Only re-enable selective 2-ply after we verify the building blocks are correct.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Give CODER a precise, minimal set of code changes to implement now to stabilize foo_player.py, recover performance, and produce diagnostics. This must be adapter-only, deterministic, and reversible. Apply only these changes this iteration.

1) Immediate safety change — disable selective 2-ply by default
   - Set TOP_K_1PLY = 0 (effectively disables 2-ply branching until we validate components).
   - Rationale: prevent any buggy 2-ply path from dominating decisions and causing regressions.

2) Expand 1-ply search budget and sampling
   - Increase:
     - MAX_ACTIONS_TO_EVAL = 80
     - SAMPLE_PER_ACTION_TYPE = 4
   - Rationale: earlier regressions looked like important actions (roads/expansion) were pruned. Larger budget improves coverage while staying 1-ply deterministic.

3) Improve sampling to better include roads and expansion
   - Modify _sample_actions to bias inclusion of road-building actions in early/mid game (not just builds vs VP).
   - Implementation (precise):
     - Compute game phase:
       - current_turn = getattr(game, "current_turn", getattr(game, "tick", 0))
       - early_game = current_turn <= EARLY_TURN_THRESHOLD
       - mid_game = EARLY_TURN_THRESHOLD < current_turn <= 2 * EARLY_TURN_THRESHOLD
     - When determining sample_count for each group:
       - base = SAMPLE_PER_ACTION_TYPE
       - If early_game and group contains build/upgrade actions -> sample_count = base + 1
       - If mid_game and group contains build_road actions -> sample_count = base + 1
       - If late_game and group contains VP-generating actions -> sample_count = base + 1
     - Use same deterministic RNG as before for shuffling.
   - NOTE: This is still phase-aware sampling (allowed), not a hand-tuned scoring function.

4) Add robust wrapper fallback to avoid missing method problems
   - In decide(), where you call the evaluator, replace direct call self._evaluate_action(...) with:
     - eval_fn = getattr(self, "_evaluate_action", None) or getattr(self, "_simulate_and_evaluate", None)
     - if eval_fn is None: log/warn and fall back to deterministic single simulation using execute_deterministic
     - Then call eval_fn(game, action, self.color)
   - Rationale: protects against refactor/name mismatch and avoids AttributeError.

5) Add diagnostic counters and logging (debug only)
   - Add counters in the player instance and reset per decide call:
     - self._diag = {
         "n_candidates": 0,
         "n_eval_attempts": 0,
         "n_eval_success": 0,
         "n_spectrum_calls": 0,
         "n_spectrum_success": 0,
         "n_det_calls": 0,
         "n_det_success": 0,
         "n_skipped": 0,
         "n_fallbacks_to_first_action": 0
       }
   - Increment appropriately inside _evaluate_action and decide when you:
     - call execute_spectrum/expand_spectrum -> n_spectrum_calls +=1; on success n_spectrum_success +=1
     - call execute_deterministic -> n_det_calls +=1; on success n_det_success +=1
     - when _evaluate_action returns None -> n_skipped +=1
   - At the end of decide (when debug True) print a concise diagnostic summary:
     - Phase, n_candidates, n_eval_attempts, n_eval_success, spectrum success rate, det success rate, any fallbacks used.
   - Rationale: we need to see whether the 2-ply path (when enabled) or the spectrum path fails often.

6) More conservative default for spectrum usage
   - If spectrum usage is causing noisy evaluations, keep SPECTRUM_MAX_OUTCOMES small (8) — keep as-is — but log when spec returns > SPECTRUM_MAX_OUTCOMES; consider sampling the highest-probability outcomes first if spec provides probabilities.
   - Implementation hint: when normalizing spectrum, sort by probability descending before capping: spec_list = sorted(spec, key=lambda x: x[1], reverse=True)[:cap]

7) Short debug experiment plan
   - Enable debug True, run 3 games and capture logs (per-game diag).
   - Inspect:
     - How often _evaluate_action returned None for candidates (high skip rate means evaluation failed often).
     - Spectrum call counts and success rates.
     - Whether many actions are evaluated deterministically or skipped.
   - If _evaluate_action failing frequently, capture stack traces printed by debug for the first few failures and send them back. If so, call RESEARCHER to confirm adapters' exact signatures or to ascertain why execute_deterministic/execute_spectrum failures occur.

8) If diagnostics show evaluation is healthy but win rate still low
   - Re-enable selective 2-ply but constrained:
     - TOP_K_1PLY = 6
     - OP_MAX_ACTIONS = 10
   - Add simulation node cap (SIMULATION_HARD_LIMIT) and decrement/stop deepening when reached.

9) Prioritize fixes order for CODER (minimal change set)
   1. Set TOP_K_1PLY = 0.
   2. Set MAX_ACTIONS_TO_EVAL = 80, SAMPLE_PER_ACTION_TYPE = 4.
   3. Modify _sample_actions to add road bias for mid_game (as described).
   4. Add eval_fn fallback in decide() (use getattr).
   5. Instrument diagnostic counters and per-decision debug print.
   6. Run 3 debug games; collect traces; share results.
   7. If debug shows adapter signature errors -> call RESEARCHER with full trace.
   8. If debug shows evaluation healthy but low performance -> enable 2-ply conservatively (TOP_K_1PLY=6, OP_MAX_ACTIONS=10) and run 30 games.

10) Success criteria for this iteration
   - After the 3 debug games: no AttributeError, and per-decision diagnostics show low skip rates (n_eval_success / n_eval_attempts > 0.9).
   - After re-running 30 games with TOP_K_1PLY=0 and expanded sampling, expect win rate to return at or above previous best (>= 50% wins or avg VP near opponent). If it does, reintroduce 2-ply carefully.

Deliverable for CODER (exact)
- Implement the 6 minimal code changes listed in step 9.
- Run 3 games with player.debug=True and return:
  - The per-decision diagnostic logs for each game.
  - The three game output traces (only the agent's debug prints are required).
- If diagnostics show frequent failures, return the first 5 full stack traces from _evaluate_action or adapter calls.

If you want, I can write the exact patch snippets for _sample_actions, the eval_fn fallback in decide, and the diagnostic counter updates so you can paste them directly. Which do you prefer — the patch snippets or have you already made the changes and want me to analyze the debug output?
================================ Human Message =================================

STRATEGIZER
**Stabilization Plan for `foo_player.py`**

### **Key Changes to Implement**
1. **Disable 2-Ply Lookahead**:
   - Set `TOP_K_1PLY = 0` to disable 2-ply adversarial lookahead and rely on 1-ply evaluation.

2. **Expand 1-Ply Search Budget**:
   - Increase `MAX_ACTIONS_TO_EVAL = 80` and `SAMPLE_PER_ACTION_TYPE = 4` to ensure broader coverage of actions.

3. **Improve Phase-Aware Sampling**:
   - Modify `_sample_actions` to prioritize **road-building actions** in mid-game and **build/upgrade actions** in early game.

4. **Add Diagnostic Counters**:
   - Instrument `_evaluate_action` and `decide` with counters to track:
     - Number of candidates evaluated.
     - Success/failure rates for `execute_spectrum` and `execute_deterministic`.
     - Fallbacks to default actions.

5. **Robust Fallback for Evaluation**:
   - Use `getattr` to dynamically resolve `_evaluate_action` or `_simulate_and_evaluate` to avoid `AttributeError`.

6. **Debug Experiment Plan**:
   - Run 3 games with `debug=True` to capture logs and validate the changes.

---

### **Code Changes**

#### **1. Update Class Parameters**
```python
# Tunable class defaults (updated per STRATEGIZER recommendations)
MAX_ACTIONS_TO_EVAL: int = 80  # Increased from 60
SAMPLE_PER_ACTION_TYPE: int = 4  # Increased from 3
SPECTRUM_MAX_OUTCOMES: int = 8
EARLY_TURN_THRESHOLD: int = 30
TOP_K_1PLY: int = 0  # Disable 2-ply lookahead
```

#### **2. Modify `_sample_actions` for Phase-Aware Sampling**
```python
def _sample_actions(self, playable_actions: Iterable, game: Game) -> List:
    """Phase-aware sampling: prioritize builds early, roads mid-game, VP late."""
    actions = list(playable_actions)
    n = len(actions)
    if n <= self.MAX_ACTIONS_TO_EVAL:
        return actions

    # Determine game phase
    current_turn = getattr(game, "current_turn", None)
    if current_turn is None:
        current_turn = getattr(game, "tick", 0)
    early_game = (current_turn <= self.EARLY_TURN_THRESHOLD)
    mid_game = (self.EARLY_TURN_THRESHOLD < current_turn <= 2 * self.EARLY_TURN_THRESHOLD)

    # Group actions by stable key
    groups = {}
    for a in actions:
        key = self._action_type_key(a)
        groups.setdefault(key, []).append(a)

    # Deterministic RNG
    color_seed = sum(ord(c) for c in str(self.color))
    rng = random.Random(self.RNG_SEED + color_seed)

    sampled = []
    for key in sorted(groups.keys()):
        group = list(groups[key])
        sample_count = self.SAMPLE_PER_ACTION_TYPE
        try:
            if early_game and any(self._is_build_or_upgrade(a) for a in group):
                sample_count += 1
            elif mid_game and any(self._is_road_action(a) for a in group):
                sample_count += 1  # Prioritize roads in mid-game
            elif not early_game and any(
                getattr(a, "action_type", None) in {ActionType.BUILD_CITY, ActionType.BUILD_SETTLEMENT}
                for a in group
            ):
                sample_count += 1
        except Exception:
            pass

        # Deterministic shuffle and pick
        rng.shuffle(group)
        take = min(sample_count, len(group))
        sampled.extend(group[:take])
        if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
            break

    # Fill remaining slots deterministically
    if len(sampled) < self.MAX_ACTIONS_TO_EVAL:
        for a in actions:
            if a not in sampled:
                sampled.append(a)
                if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
                    break

    if self.debug:
        phase = "early" if early_game else ("mid" if mid_game else "late")
        print(f"_sample_actions: phase={phase}, pruned {n} -> {len(sampled)} actions (cap={self.MAX_ACTIONS_TO_EVAL})")
    return sampled

# Helper to detect road-building actions
def _is_road_action(self, action) -> bool:
    """Detect road-building actions."""
    at = getattr(action, "action_type", None)
    try:
        return at == ActionType.BUILD_ROAD
    except Exception:
        name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
        name_str = str(name).lower()
        return "road" in name_str
```

#### **3. Add Diagnostic Counters**
```python
def __init__(self, name: Optional[str] = None):
    super().__init__(Color.BLUE, name)
    self.debug: bool = False
    self._diag = {
        "n_candidates": 0,
        "n_eval_attempts": 0,
        "n_eval_success": 0,
        "n_spectrum_calls": 0,
        "n_spectrum_success": 0,
        "n_det_calls": 0,
        "n_det_success": 0,
        "n_skipped": 0,
        "n_fallbacks_to_first_action": 0
    }
    try:
        self._value_fn = base_fn()
    except Exception:
        self._value_fn = None
```

#### **4. Update `_evaluate_action` with Diagnostics**
```python
def _evaluate_action(self, game: Game, action, my_color: Color) -> Optional[Tuple[float, float]]:
    """Evaluate an action and return (score, vp_delta) or None on failure."""
    self._diag["n_eval_attempts"] += 1

    # Copy the game state
    try:
        game_copy = copy_game(game)
    except Exception as e:
        if self.debug:
            print("copy_game failed:", e)
            traceback.print_exc()
        self._diag["n_skipped"] += 1
        return None

    # Helper to safely compute numeric score
    def score_for(g: Game) -> Optional[float]:
        try:
            s = self._value_fn(g, my_color)
            return float(s)
        except Exception:
            if self.debug:
                print("value function failed on game state for action", repr(action))
                traceback.print_exc()
            return None

    # If this is a robber/chance-like action, try to compute expected value
    if self._is_robber_or_chance(action):
        self._diag["n_spectrum_calls"] += 1
        try:
            spectrum = None
            try:
                spectrum = execute_spectrum(game_copy, action)
            except Exception:
                try:
                    spec_map = expand_spectrum(game_copy, [action])
                    if isinstance(spec_map, dict):
                        spectrum = spec_map.get(action, [])
                except Exception:
                    spectrum = None

            if spectrum:
                spectrum_list = list(spectrum)[:self.SPECTRUM_MAX_OUTCOMES]
                weighted_score = 0.0
                weighted_vp_delta = 0.0
                base_vp = self._get_visible_vp(game, my_color)
                for entry in spectrum_list:
                    try:
                        outcome_game, prob = entry
                    except Exception:
                        continue
                    sc = score_for(outcome_game)
                    if sc is None:
                        weighted_score = None
                        break
                    weighted_score += prob * sc
                    vp_after = self._get_visible_vp(outcome_game, my_color)
                    weighted_vp_delta += prob * (vp_after - base_vp)

                if weighted_score is not None:
                    self._diag["n_spectrum_success"] += 1
                    if self.debug:
                        print(f"Spectrum eval for {repr(action)}: expected_score={weighted_score}, expected_vp_delta={weighted_vp_delta}, outcomes={len(spectrum_list)}")
                    return (float(weighted_score), float(weighted_vp_delta))
        except Exception as e:
            if self.debug:
                print("execute_spectrum/expand_spectrum failed for action", repr(action), "error:", e)
                traceback.print_exc()

    # Default deterministic evaluation
    self._diag["n_det_calls"] += 1
    try:
        outcomes = execute_deterministic(game_copy, action)
    except Exception as e:
        if self.debug:
            print("execute_deterministic failed for action:", repr(action), "error:", e)
            traceback.print_exc()
        self._diag["n_skipped"] += 1
        return None

    try:
        if not outcomes:
            if self.debug:
                print("execute_deterministic returned empty outcomes for", repr(action))
            self._diag["n_skipped"] += 1
            return None
        first = outcomes[0]
        if isinstance(first, (list, tuple)) and len(first) >= 1:
            resultant_game = first[0]
        else:
            resultant_game = first
    except Exception:
        resultant_game = game_copy

    sc = score_for(resultant_game)
    if sc is None:
        self._diag["n_skipped"] += 1
        return None
    try:
        base_vp = self._get_visible_vp(game, my_color)
        after_vp = self._get_visible_vp(resultant_game, my_color)
        vp_delta = float(after_vp - base_vp)
    except Exception:
        vp_delta = 0.0

    self._diag["n_eval_success"] += 1
    self._diag["n_det_success"] += 1
    return (float(sc), float(vp_delta))
```

#### **5. Update `decide` with Fallback and Diagnostics**
```python
def decide(self, game: Game, playable_actions: Iterable):
    """Choose an action using 1-ply lookahead with diagnostics."""
    self._diag = {
        "n_candidates": 0,
        "n_eval_attempts": 0,
        "n_eval_success": 0,
        "n_spectrum_calls": 0,
        "n_spectrum_success": 0,
        "n_det_calls": 0,
        "n_det_success": 0,
        "n_skipped": 0,
        "n_fallbacks_to_first_action": 0
    }

    actions = list(playable_actions)
    if not actions:
        if self.debug:
            print("decide: no playable_actions provided")
        return None
    if len(actions) == 1:
        if self.debug:
            print("decide: single playable action, returning it")
        return actions[0]

    # Sample/prune with phase awareness
    candidates = self._sample_actions(actions, game)
    self._diag["n_candidates"] = len(candidates)

    if self.debug:
        print(f"decide: evaluating {len(candidates)} candidate(s) out of {len(actions)} playable action(s)")

    best_action = None
    best_score = -float("inf")
    best_vp_delta = -float("inf")
    best_tie_repr = None

    # Use getattr to dynamically resolve evaluator
    eval_fn = getattr(self, "_evaluate_action", None) or getattr(self, "_simulate_and_evaluate", None)
    if eval_fn is None:
        if self.debug:
            print("decide: no evaluator method found; falling back to first action")
        self._diag["n_fallbacks_to_first_action"] += 1
        return actions[0]

    for action in candidates:
        eval_res = eval_fn(game, action, self.color)
        if self.debug:
            print(f"Evaluated action: {repr(action)} -> {eval_res}")

        if eval_res is None:
            self._diag["n_skipped"] += 1
            continue
        score, vp_delta = eval_res

        tie_repr = repr(action)
        is_better = False
        if best_action is None:
            is_better = True
        elif score > best_score:
            is_better = True
        elif score == best_score:
            if vp_delta > best_vp_delta:
                is_better = True
            elif vp_delta == best_vp_delta:
                if best_tie_repr is None or tie_repr < best_tie_repr:
                    is_better = True

        if is_better:
            best_action = action
            best_score = score
            best_vp_delta = vp_delta
            best_tie_repr = tie_repr

    if best_action is None:
        if self.debug:
            print("decide: no evaluated candidate succeeded; falling back to first playable action")
            self._diag["n_fallbacks_to_first_action"] += 1
        return actions[0]

    if self.debug:
        print("decide: chosen action:", repr(best_action), "score:", best_score, "vp_delta:", best_vp_delta)
        print("Diagnostics:", self._diag)

    return best_action
```

---
### **Debug Experiment Plan**
1. **Run 3 Games with `debug=True`**:
   - Enable `debug=True` for the player instance.
   - Capture logs for each game.

2. **Analyze Logs**:
   - Check for frequent evaluation failures or fallbacks.
   - Validate that `execute_spectrum` and `execute_deterministic` are succeeding.

3. **Adjust Parameters if Needed**:
   - If performance is still low, incrementally increase `MAX_ACTIONS_TO_EVAL` or adjust sampling biases.

---
### **Expected Outcome**
- **Stabilized Performance**: Win rate should return to ≥50% with broader 1-ply sampling.
- **Diagnostic Insights**: Logs will reveal if evaluation failures or sampling biases are causing issues.

---
### **Deliverable for CODER**
1. Implement the changes above.
2. Run 3 games with `debug=True` and share logs.
3. Re-run the 30-game experiment and report results.

END STRATEGIZER
================================== Ai Message ==================================

- META THOUGHTS:
  - Recent run regressed: selective 2-ply appears to be hurting performance (or falling back often). The immediate priority is to stabilize behavior by disabling the 2-ply path, widening 1-ply coverage (to avoid pruning key expansion/road actions), and instrumenting diagnostics so we can see why deepening fails when re-enabled.
  - Make minimal, reversible changes so we can attribute any improvement or further regression to these edits. After we collect diagnostics from a few debug games we will either re-enable a constrained 2-ply or fix the failing adapter/path.

- META GOAL:
  - Implement the stabilization plan: disable 2-ply by default (TOP_K_1PLY=0), increase 1-ply sampling (MAX_ACTIONS_TO_EVAL=80, SAMPLE_PER_ACTION_TYPE=4), bias sampling to include road-building in mid-game, add diagnostic counters and robust eval fallback in decide, run 3 debug games and then the 30-game batch if diagnostics look healthy.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the following concrete edits to foo_player.py now. Make only these changes this iteration.

1) Class defaults — update constants
- Set:
  MAX_ACTIONS_TO_EVAL = 80
  SAMPLE_PER_ACTION_TYPE = 4
  SPECTRUM_MAX_OUTCOMES = 8  (keep)
  EARLY_TURN_THRESHOLD = 30  (keep)
  TOP_K_1PLY = 0             (disable 2-ply by default)
  RNG_SEED = 0               (keep)

2) Modify / add sampling behavior
- Replace current _sample_actions with the phase-aware version below (ensure deterministic RNG seeding uses RNG_SEED + stable color hash):

def _sample_actions(self, playable_actions, game):
    actions = list(playable_actions)
    n = len(actions)
    if n <= self.MAX_ACTIONS_TO_EVAL:
        return actions

    # phase detection
    current_turn = getattr(game, "current_turn", None)
    if current_turn is None:
        current_turn = getattr(game, "tick", 0)
    early_game = (current_turn <= self.EARLY_TURN_THRESHOLD)
    mid_game = (self.EARLY_TURN_THRESHOLD < current_turn <= 2 * self.EARLY_TURN_THRESHOLD)

    # group actions by stable key
    groups = {}
    for a in actions:
        key = self._action_type_key(a)
        groups.setdefault(key, []).append(a)

    # deterministic RNG
    color_seed = sum(ord(c) for c in str(self.color))
    rng = random.Random(self.RNG_SEED + color_seed)

    sampled = []
    for key in sorted(groups.keys()):
        group = list(groups[key])
        sample_count = self.SAMPLE_PER_ACTION_TYPE
        try:
            if early_game and any(self._is_build_or_upgrade(a) for a in group):
                sample_count += 1
            elif mid_game and any(self._is_road_action(a) for a in group):
                sample_count += 1
            elif not early_game and any(getattr(a, "action_type", None) in {ActionType.BUILD_CITY, ActionType.BUILD_SETTLEMENT} for a in group):
                sample_count += 1
        except Exception:
            pass
        rng.shuffle(group)
        take = min(sample_count, len(group))
        sampled.extend(group[:take])
        if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
            break

    # fill remaining deterministically
    if len(sampled) < self.MAX_ACTIONS_TO_EVAL:
        for a in actions:
            if a not in sampled:
                sampled.append(a)
                if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
                    break

    if self.debug:
        phase = "early" if early_game else ("mid" if mid_game else "late")
        print(f"_sample_actions: phase={phase}, pruned {n} -> {len(sampled)} (cap={self.MAX_ACTIONS_TO_EVAL})")
    return sampled

- Add helper _is_road_action:

def _is_road_action(self, action):
    at = getattr(action, "action_type", None)
    try:
        return at == ActionType.BUILD_ROAD
    except Exception:
        name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
        return "road" in str(name).lower()

3) Add diagnostic counters in __init__
- Initialize self._diag dict in __init__:

self._diag = {
    "n_candidates": 0,
    "n_eval_attempts": 0,
    "n_eval_success": 0,
    "n_spectrum_calls": 0,
    "n_spectrum_success": 0,
    "n_det_calls": 0,
    "n_det_success": 0,
    "n_skipped": 0,
    "n_fallbacks_to_first_action": 0
}

- Ensure self._value_fn initialization remains (try base_fn()).

4) Robust eval function resolution in decide
- In decide, resolve evaluation function using getattr to avoid AttributeError:

eval_fn = getattr(self, "_evaluate_action", None) or getattr(self, "_simulate_and_evaluate", None)
if eval_fn is None:
    if self.debug:
        print("decide: no evaluator; falling back to first action")
    self._diag["n_fallbacks_to_first_action"] += 1
    return actions[0]

- Use eval_fn(game, action, self.color) in the decision loop. Keep the existing tie-break logic. Increment diag counters per result as described in the STRATEGIZER pseudocode.

5) Instrument _evaluate_action and / or _simulate_and_evaluate with diagnostics
- At top of _evaluate_action increment self._diag["n_eval_attempts"].
- When calling execute_spectrum/expand_spectrum increment n_spectrum_calls and on success n_spectrum_success.
- When calling execute_deterministic increment n_det_calls and on success n_det_success.
- If _evaluate_action returns None for a candidate increment n_skipped.
- On final selection or fallback, print self._diag when debug True.

6) Sorting / normalization improvement for spectrum
- When normalizing/capping spectrum outcomes, sort by probability descending before truncating:

spec_list = sorted(list(spec), key=lambda x: x[1] if len(x)>1 else 0.0, reverse=True)[:self.SPECTRUM_MAX_OUTCOMES]

7) Run debug experiment and reporting
- Set player.debug = True and run 3 games. Collect and return:
  - Per-decision diagnostic prints (the debug outputs).
  - Any stack traces printed for the first up to 5 failures (if present).
- If diagnostics show low skip rates (n_eval_success / n_eval_attempts > 0.9), set player.debug = False and run 30 games. Report performance JSON.
- If diagnostics show frequent failures or spectrum/deterministic adapters failing, capture full stack traces and return them so we call RESEARCHER.

8) Conservative re-enable path for 2-ply (only after diagnostics)
- If stable, re-enable 2-ply conservatively:
  TOP_K_1PLY = 6
  OP_MAX_ACTIONS = 10

Important constraints for CODER:
- Make only the changes listed above this iteration—don’t reintroduce broader 2-ply branching.
- Preserve determinism (use RNG seeding as shown).
- Do not add handcrafted scoring heuristics—use base_fn-based evaluations only.
- Ensure decide never raises; always fall back to a deterministic action.

Execution request:
- Apply the code edits (1..6).
- Run 3 debug games (player.debug=True).
- Return debug logs and up to 5 stack traces (if any).
- After we inspect diagnostics, we will instruct you whether to run the full 30-game batch.

If you want, I can provide exact patch snippets for insertion points (e.g., __init__, _sample_actions replacement block, _is_road_action, modifications to decide) to paste directly into foo_player.py. Do you want the patch snippets or will you implement these changes directly?
================================ Human Message =================================

CODER

I updated foo_player.py with the stabilization changes you requested:

- Disabled 2-ply by default (TOP_K_1PLY = 0).
- Increased 1-ply search budget (MAX_ACTIONS_TO_EVAL = 80, SAMPLE_PER_ACTION_TYPE = 4).
- Implemented phase-aware sampling that prioritizes build actions early and road actions mid-game.
- Added _is_road_action helper.
- Added diagnostic counters (_diag) initialized in __init__ and instrumented in _evaluate_action, _simulate_and_evaluate, and decide.
- Implemented robust eval function resolution in decide (getattr fallback), preventing AttributeError.
- Improved spectrum normalization to sort by probability descending before capping.
- Ensured all adapter calls are try/except wrapped; debug prints include full tracebacks when self.debug is True.
- Kept determinism via RNG seeded with RNG_SEED + stable color hash.

What I need from you / next steps:
- Do you want me to run the 3-game debug smoke test with player.debug = True now and return the diagnostic logs (as STRATEGIZER requested)?
- If yes, I will run the small debug experiment and report per-decision diagnostics and any stack traces (up to 5) if failures occur.
- After diagnostics look healthy, I'll run the 30-game batch and return performance JSON.

END CODER
================================ Human Message =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 18,
      "AVG VP": 8.233333333333333,
      "AVG SETTLES": 2.433333333333333,
      "AVG CITIES": 2.3,
      "AVG ROAD": 0.6,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 12,
      "AVG VP": 7.766666666666667,
      "AVG SETTLES": 3.033333333333333,
      "AVG CITIES": 1.7666666666666666,
      "AVG ROAD": 0.4,
      "AVG ARMY": 0.03333333333333333,
      "AVG DEV VP": 0.3333333333333333
    }
  },
  "Game Summary": {
    "AVG TICKS": 211.33333333333334,
    "AVG TURNS": 73.96666666666667,
    "AVG DURATION": 1.4504021803538005
  }
}
================================ Human Message =================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

---
### ERROR SUMMARY
- **First Error Line**:
  `AttributeError: 'FooPlayer' object has no attribute '_evaluate_action'`
- **Exception Type**: `AttributeError`
- **File**: `/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/agents/agentEvolver_v2/foo_player.py`
- **Line Number**: 505
- **Exact Code Line**:
  `res = self._evaluate_action(game, a, self.color)`

---
### LIKELY CAUSE
- The `_evaluate_action` method is **referenced in the `decide` method** but **not implemented** in the `FooPlayer` class.
- This is likely due to a **refactoring oversight** where the method was either:
  - **Renamed** (e.g., to `_simulate_and_evaluate`).
  - **Removed** or **not implemented** during updates.

---
### QUICK FIX FOCUS
- **Inspect Line 505** in `foo_player.py`:
  - The `decide` method calls `self._evaluate_action(game, a, self.color)`, but this method is **missing**.
  - **Solution**:
    1. **Implement `_evaluate_action`** to evaluate actions and return `(score, vp_delta)` as expected by the logic.
    2. **Replace the call** with an existing method (e.g., `_simulate_and_evaluate`) or a fallback logic.

---
Let me know if you need anything else.
END ANALYZER
================================== Ai Message ==================================

- META THOUGHTS:
  - Recent fixes removed the crash and stabilized behavior, but performance still lags: FooPlayer now 12/30 wins and trails AlphaBeta in avg VP and roads. The stabilization changes (disable 2-ply, widen 1-ply sampling, phase-aware sampling, diagnostics) helped, but the agent still under-prioritizes long-term expansion (roads/placement) and city/road tradeoffs.
  - Root causes likely: (A) selective 2-ply was disabled and not yet reintroduced, removing opponent-robust reasoning; (B) 1-ply sampling, even widened, may not prioritize actions that increase future building opportunities; (C) when 2-ply was enabled earlier it may have fallen back often (many None evaluations) or used too many poor candidates; (D) evaluation (base_fn) may undervalue multi-turn positioning since it only sees immediate state. We must bring back a constrained, reliable 2-ply that explicitly favors actions that increase our future actionable space (expansion potential) and that is robust to evaluation failures.
  - We must avoid handcrafted heuristics. Use algorithmic measurements available through adapters (simulate outcomes, count future playable actions, compute base_fn) to pick candidates with good multi-turn potential. Keep computation bounded and deterministic. Collect diagnostics to verify improvements.

- META GOAL:
  - Reintroduce a conservative, reliable selective 2-ply adversarial lookahead that: (1) is only applied when the underlying evaluators succeed reliably, (2) explicitly considers “expansion potential” (number of playable actions we have next turn) as a proxy for long-term positioning, and (3) uses a tight candidate/opponent action cap to limit runtime. Aim for improved roads and win-rate recovery on the next 30-game batch.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Design a concrete, implementable selective 2-ply reintroduction and expansion-potential strategy for CODER to implement. Provide precise parameters, algorithm, tie-break rules, diagnostics to collect, and safe fallbacks so CODER can apply the change and run experiments without breaking determinism or runtime bounds.

1) High-level change summary to implement now
   - Re-enable selective 2-ply but conservatively and only when evaluators are healthy:
     - TOP_K_1PLY = 6  # only deepen top 6 1-ply candidates
     - OP_MAX_ACTIONS = 10  # limit opponent responses considered per outcome
     - OP_SAMPLE_PER_ACTION_TYPE = 2
   - Add an “expansion potential” metric for each candidate action:
     - expansion_potential(a) = average over outcomes of (count of playable actions available to my_color in outcome_game)
     - This is computed by simulating a (spectrum/deterministic) and calling the playable-actions extractor (derive_playable_actions). Use this metric as an additional tie-breaker and as a filter to ensure road/expansion actions are represented among the top candidates.
   - Only run 2-ply if the pre-check diagnostics indicate evaluator reliability in current decide() call:
     - n_eval_attempts > 0 and (n_eval_success / n_eval_attempts) >= 0.85 and n_spectrum_success/n_spectrum_calls >= 0.7 when spectrum called frequently.
     - If reliability thresholds are not met, skip 2-ply and use the 1-ply decision.

2) Exact new/changed parameters (class defaults)
   - TOP_K_1PLY = 6
   - OP_MAX_ACTIONS = 10
   - OP_SAMPLE_PER_ACTION_TYPE = 2
   - MAX_SIMULATION_NODES = 4000  # hard cap across the 2-ply evaluation to bound runtime
   - MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.85
   - MIN_SPECTRUM_SUCCESS_RATE = 0.7

3) Candidate selection pipeline (detailed)
   - Stage A: Run 1-ply evaluation exactly as current code (sample/prune, call eval_fn, collect (action, score, vp_delta) for each candidate).
   - Stage B: From 1-ply results produce a candidate pool:
       - Always include the top 3 actions by 1-ply score.
       - Include up to TOP_K_1PLY total actions by adding actions that maximize expansion_potential among remaining 1-ply candidates (simulate each remaining action deterministically or via spectrum, compute expansion potential).
       - If there are fewer than TOP_K_1PLY candidates, use all.
       - Rationale: ensure we don’t miss actions that increase our future options even if their immediate 1-ply score is slightly lower.
   - Implementation detail: compute expansion_potential using the same simulation functions used for 2-ply (execute_spectrum/execute_deterministic). Cap spectrum outcomes to SPECTRUM_MAX_OUTCOMES and sort by prob descending. If evaluate simulation for expansion_potential fails for a candidate, treat its expansion_potential as -inf for selection so we avoid relying on unreliable sims.

4) 2-ply adversarial evaluation (for each selected candidate a)
   - For each candidate a:
       - Simulate its outcome branches (spectrum preferred; otherwise deterministic). Normalize and cap outcomes as before.
       - For each outcome_game_i (prob p_i):
           - Determine opponent color opp_color.
           - Obtain opponent actions opp_actions via _derive_opponent_actions.
           - Prune/sampling opponent actions deterministically using _sample_opponent_actions to at most OP_MAX_ACTIONS (group+sample).
           - For each opponent action b in pruned list:
               - Simulate b (spectrum/deterministic) and evaluate resulting game state via safe_eval_base_fn for my_color to get score_after_b.
           - Adversarial aggregation: value_i = min_b(score_after_b) if any b simulated; else value_i = safe_eval_base_fn(outcome_game_i).
       - Aggregate candidate value: expected_value_a = sum_i p_i * value_i.
   - Maintain a global simulated_nodes counter; if simulated_nodes > MAX_SIMULATION_NODES abort remaining deeper sims and fall back to selecting best 1-ply action (log that cap was hit).

5) Selection Rule / Tie-breaks
   - Primary: expected_value_a (higher better).
   - Secondary: expansion_potential(a) (higher is better) — promotes long-term mobility/road expansion.
   - Tertiary: 1-ply vp_delta (higher better).
   - Final: lexicographic repr(action) (smaller wins).
   - Deterministic ordering must be preserved.

6) Pre-2-ply reliability checks (safe guard)
   - Before running Stage D (2-ply), compute:
       - eval_success_rate = n_eval_success / max(1, n_eval_attempts)
       - If eval_success_rate < MIN_EVAL_SUCCESS_RATE_FOR_2PLY: skip 2-ply.
       - If n_spectrum_calls > 0 and (n_spectrum_success / n_spectrum_calls) < MIN_SPECTRUM_SUCCESS_RATE: skip 2-ply.
       - If skip: log reason in debug and return best 1-ply action.

7) Diagnostics to add/collect (debug)
   - For each decide call (print when debug True):
       - Pre-2-ply stats: n_candidates, n_eval_attempts, n_eval_success, n_spectrum_calls, n_spectrum_success, eval_success_rate.
       - Candidate pool: list top-1ply actions and selected expansion-based additions with (1-ply score, expansion_potential).
       - For each candidate deepened: outcomes_count, total simulated nodes used for candidate, min opponent response score, expected_value_a.
       - If MAX_SIMULATION_NODES reached, print where and current totals.
   - After 30-game batch collect aggregate: times 2-ply was run vs skipped; average simulated nodes per 2-ply invocation; distribution of expansion_potential for chosen actions; road counts and VP changes.

8) Fallback behavior
   - If any simulation function or evaluation fails during 2-ply for a candidate, skip that candidate (do not let failures crash decide()). If more than 50% of candidates fail to simulate, skip 2-ply entirely for this decide() and use 1-ply selection.
   - If no candidate succeeds, return best 1-ply action or fallback to actions[0].
   - Ensure no exceptions escape decide().

9) Implementation pseudocode sketch for CODER (compact)
   - After current 1-ply loop:
       compute eval_success_rate
       if not reliability_ok: return best_1ply_action
       # Build candidate pool:
       top_by_1ply = top 3 by score
       remaining = other candidates
       expansion_scores = {}
       for a in remaining:
           eps = compute_expansion_potential(a)  # simulate a's outcomes and count playable actions for my_color
           if eps is None: continue
           expansion_scores[a] = eps
       add top (TOP_K_1PLY - len(top_by_1ply)) actions from remaining sorted by expansion_scores descending to make candidate_pool
       # 2-ply
       for a in candidate_pool:
           expected_value_a = 0
           for outcome, p in simulate_outcomes(a):
               opp_actions = derive_opponent_actions(outcome)
               opp_pruned = sample_and_prune(opp_actions, OP_MAX_ACTIONS)
               if not opp_pruned:
                   value_i = safe_eval_base_fn(outcome)
               else:
                   min_score = +inf
                   for b in opp_pruned:
                       score_after_b = simulate_and_eval(outcome, b)
                       min_score = min(min_score, score_after_b)
                   value_i = min_score
               expected_value_a += p * value_i
           record expected_value_a
       choose action by tie-breaks above
       return chosen

10) Success criteria for this iteration
   - After re-enabling conservative 2-ply with expansion potential and running 30 games:
       - Win rate should improve towards at least previous best (>= 50% wins) or avg VP gap narrow (our avg VP within 0.5 of opponent).
       - Road count should increase (target avg road >= 0.5).
       - Diagnostics show eval_success_rate >= 0.85 and average simulated nodes per 2-ply <= MAX_SIMULATION_NODES.

11) Deliverables for CODER
   - Implement the above reintroduction of selective 2-ply and expansion_potential metric.
   - Ensure all adapter calls are try/except wrapped and simulated_nodes counting and early aborting on MAX_SIMULATION_NODES is enforced.
   - Run a diagnostic debug run of 3 games (player.debug=True), return the per-decision logs.
   - If diagnostics are healthy, run the 30-game batch (player.debug=False) and return the JSON metrics and aggregate diagnostics (how often 2-ply ran/skipped, avg simulated nodes).
   - If many simulation failures occur, capture up to 5 full stack traces and report them so we can call RESEARCHER.

If you want, I will now prepare exact code snippets for:
- compute_expansion_potential implementation,
- candidate pool construction and selection logic,
- simulated_nodes accounting,
- pre-2-ply reliability checks and logging.

Which would you prefer — ready-to-paste snippets for CODER to paste into foo_player.py, or let CODER implement using the algorithm above and then run the debug experiments?
================================ Human Message =================================

STRATEGIZER
**Ready-to-Paste Code Snippets for Reintroducing 2-Ply Lookahead with Expansion Potential**

---

### **1. Update Class Parameters**
Add or update the following parameters in the `FooPlayer` class:
```python
# Tunable class defaults (updated per STRATEGIZER recommendations)
MAX_ACTIONS_TO_EVAL: int = 80
SAMPLE_PER_ACTION_TYPE: int = 4
SPECTRUM_MAX_OUTCOMES: int = 8
EARLY_TURN_THRESHOLD: int = 30

# Reintroduce selective 2-ply with conservative parameters
TOP_K_1PLY: int = 6
OP_MAX_ACTIONS: int = 10
OP_SAMPLE_PER_ACTION_TYPE: int = 2
MAX_SIMULATION_NODES: int = 4000  # Hard cap on simulated nodes
MIN_EVAL_SUCCESS_RATE_FOR_2PLY: float = 0.85
MIN_SPECTRUM_SUCCESS_RATE: float = 0.7
```

---
### **2. Add `compute_expansion_potential` Method**
```python
def _compute_expansion_potential(self, game: Game, action) -> float:
    """Compute the expansion potential of an action.

    Expansion potential is the average number of playable actions available to us
    in the resulting game state(s) after executing `action`.
    """
    try:
        game_copy = copy_game(game)
    except Exception:
        if self.debug:
            print("_compute_expansion_potential: copy_game failed")
            traceback.print_exc()
        return -float("inf")

    # Simulate the action to get outcome branches
    outcomes = []
    try:
        if self._is_robber_or_chance(action):
            spec = None
            try:
                spec = execute_spectrum(game_copy, action)
            except Exception:
                try:
                    spec_map = expand_spectrum(game_copy, [action])
                    if isinstance(spec_map, dict):
                        spec = spec_map.get(action, None)
                except Exception:
                    spec = None
            if spec:
                outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
        else:
            det_res = execute_deterministic(game_copy, action)
            if det_res:
                outcomes = [(det_res[0][0], 1.0)] if isinstance(det_res[0], tuple) else [(det_res[0], 1.0)]
    except Exception:
        if self.debug:
            print("_compute_expansion_potential: failed to simulate action")
            traceback.print_exc()
        return -float("inf")

    if not outcomes:
        return -float("inf")

    total_expansion = 0.0
    for outcome_game, prob in outcomes:
        try:
            playable = self._derive_opponent_actions(outcome_game, self.color)
            expansion = len(playable) if playable else 0
            total_expansion += prob * expansion
        except Exception:
            if self.debug:
                print("_compute_expansion_potential: failed to derive playable actions")
                traceback.print_exc()
            return -float("inf")

    return total_expansion
```

---
### **3. Update `decide` Method to Include Expansion Potential and 2-Ply Logic**
Replace the existing `decide` method with the following:

```python
def decide(self, game: Game, playable_actions: Iterable):
    """Choose an action using selective 2-ply adversarial lookahead with expansion potential."""
    actions = list(playable_actions)
    if not actions:
        if self.debug:
            print("decide: no playable_actions provided")
        return None
    if len(actions) == 1:
        if self.debug:
            print("decide: single playable action, returning it")
        return actions[0]

    # Reset diagnostics for this decision
    self._diag = {k: 0 for k in self._diag}

    # Stage 1: 1-ply evaluation
    candidates = self._sample_actions(actions, game)
    self._diag["n_candidates"] = len(candidates)
    if self.debug:
        print(f"decide: sampled {len(candidates)} candidates from {len(actions)} actions")

    one_ply_results = []
    eval_fn = getattr(self, "_evaluate_action", None) or getattr(self, "_simulate_and_evaluate", None)
    if eval_fn is None:
        if self.debug:
            print("decide: no evaluator method found; falling back to first action")
        self._diag["n_fallbacks_to_first_action"] += 1
        return actions[0]

    for idx, a in enumerate(candidates, start=1):
        try:
            res = eval_fn(game, a, self.color)
        except Exception:
            if self.debug:
                print("decide: evaluator raised exception for action", repr(a))
                traceback.print_exc()
            res = None

        if self.debug:
            print(f"1-ply [{idx}/{len(candidates)}]: {repr(a)} -> {res}")

        if res is None:
            self._diag["n_skipped"] += 1
            continue
        sc, vpd = res
        one_ply_results.append((a, float(sc), float(vpd)))

    if not one_ply_results:
        if self.debug:
            print("decide: no 1-ply evaluations succeeded; falling back to first playable action")
        self._diag["n_fallbacks_to_first_action"] += 1
        return actions[0]

    # Stage 2: Check reliability for 2-ply
    eval_success_rate = self._diag["n_eval_success"] / max(1, self._diag["n_eval_attempts"])
    spectrum_success_rate = (
        self._diag["n_spectrum_success"] / max(1, self._diag["n_spectrum_calls"])
        if self._diag["n_spectrum_calls"] > 0
        else 1.0
    )
    reliability_ok = (
        eval_success_rate >= self.MIN_EVAL_SUCCESS_RATE_FOR_2PLY
        and spectrum_success_rate >= self.MIN_SPECTRUM_SUCCESS_RATE
    )
    if self.debug:
        print(
            f"decide: eval_success_rate={eval_success_rate:.2f}, "
            f"spectrum_success_rate={spectrum_success_rate:.2f}, "
            f"reliability_ok={reliability_ok}"
        )

    if not reliability_ok:
        if self.debug:
            print("decide: skipping 2-ply due to low reliability")
        # Fall back to best 1-ply action
        best_action_1ply = None
        best_score = -float("inf")
        best_vp = -float("inf")
        best_repr = None
        for (a, s, v) in one_ply_results:
            tie_repr = repr(a)
            is_better = False
            if best_action_1ply is None:
                is_better = True
            elif s > best_score:
                is_better = True
            elif s == best_score:
                if v > best_vp:
                    is_better = True
                elif v == best_vp and (best_repr is None or tie_repr < best_repr):
                    is_better = True
            if is_better:
                best_action_1ply = a
                best_score = s
                best_vp = v
                best_repr = tie_repr

        if best_action_1ply is not None:
            if self.debug:
                print("decide: chosen action (1-ply fallback):", repr(best_action_1ply), "score:", best_score, "vp_delta:", best_vp)
                print("Diagnostics:", self._diag)
            return best_action_1ply
        else:
            if self.debug:
                print("decide: no choice after fallbacks; returning first playable action")
                self._diag["n_fallbacks_to_first_action"] += 1
            return actions[0]

    # Stage 3: Build candidate pool with expansion potential
    one_ply_results.sort(key=lambda t: (t[1], t[2]), reverse=True)
    top_by_1ply = [t[0] for t in one_ply_results[:3]]  # Always include top 3 by 1-ply score
    remaining_candidates = [t[0] for t in one_ply_results[3:]]

    expansion_scores = {}
    for a in remaining_candidates:
        exp_potential = self._compute_expansion_potential(game, a)
        if exp_potential >= 0:  # Only consider valid expansion potentials
            expansion_scores[a] = exp_potential

    # Sort remaining candidates by expansion potential
    sorted_remaining = sorted(
        expansion_scores.items(),
        key=lambda x: x[1],
        reverse=True
    )
    additional_candidates = [a for a, _ in sorted_remaining[:self.TOP_K_1PLY - len(top_by_1ply)]]
    candidate_pool = top_by_1ply + additional_candidates

    if self.debug:
        print("Candidate pool:")
        for a in candidate_pool:
            exp_potential = expansion_scores.get(a, "N/A")
            print(f"  {repr(a)} (expansion_potential={exp_potential})")

    # Stage 4: 2-ply adversarial evaluation
    best_action = None
    best_value = -float("inf")
    best_expansion = -float("inf")
    best_vp_delta = -float("inf")
    best_repr = None
    sim_count = 0

    for a in candidate_pool:
        if sim_count >= self.MAX_SIMULATION_NODES:
            if self.debug:
                print("decide: reached simulation hard limit; stopping deepening")
            break

        # Simulate our action a to produce outcome branches
        try:
            game_copy = copy_game(game)
        except Exception as e:
            if self.debug:
                print("decide: copy_game failed for candidate", repr(a), e)
                traceback.print_exc()
            continue

        # Obtain outcome branches
        outcomes = []
        try:
            if self._is_robber_or_chance(a):
                spec = None
                try:
                    spec = execute_spectrum(game_copy, a)
                except Exception:
                    try:
                        spec_map = expand_spectrum(game_copy, [a])
                        if isinstance(spec_map, dict):
                            spec = spec_map.get(a, None)
                    except Exception:
                        spec = None
                if spec:
                    outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
            if not outcomes:
                det_res = execute_deterministic(game_copy, a)
                if det_res:
                    outcomes = [(det_res[0][0], 1.0)] if isinstance(det_res[0], tuple) else [(det_res[0], 1.0)]
        except Exception as e:
            if self.debug:
                print("decide: failed to obtain outcomes for candidate", repr(a), "error:", e)
                traceback.print_exc()
            continue

        if not outcomes:
            continue

        # Cap outcomes
        if len(outcomes) > self.SPECTRUM_MAX_OUTCOMES:
            outcomes = outcomes[:self.SPECTRUM_MAX_OUTCOMES]

        if self.debug:
            print(f"Candidate {repr(a)} produced {len(outcomes)} outcome(s) to evaluate")

        expected_value_a = 0.0
        expansion_potential_a = 0.0
        one_ply_vp_delta = next((v for (act, s, v) in one_ply_results if act == a), 0.0)

        for og, p_i in outcomes:
            if sim_count >= self.MAX_SIMULATION_NODES:
                break

            # Compute expansion potential for this outcome
            try:
                playable = self._derive_opponent_actions(og, self.color)
                expansion = len(playable) if playable else 0
                expansion_potential_a += p_i * expansion
            except Exception:
                if self.debug:
                    print("decide: failed to compute expansion potential for outcome")
                    traceback.print_exc()
                expansion_potential_a += p_i * -float("inf")

            # Determine opponent color
            opp_color = self._determine_opponent_color(og, self.color)

            # Get opponent actions
            try:
                opp_actions = self._derive_opponent_actions(og, opp_color)
            except Exception:
                opp_actions = []

            if not opp_actions:
                val_i = self._simulate_and_evaluate(og, None, self.color)
                if val_i is None:
                    continue
                expected_value_a += p_i * val_i
                sim_count += 1
                continue

            # Prune opponent actions
            opp_sampled = self._sample_opponent_actions(opp_actions, og, opp_color)[:self.OP_MAX_ACTIONS]

            if self.debug:
                print(f"  outcome p={p_i:.3f}: opp_actions={len(opp_actions)} -> sampled={len(opp_sampled)}")

            # Adversarial evaluation
            min_score_after_opp = float("inf")
            for b in opp_sampled:
                if sim_count >= self.MAX_SIMULATION_NODES:
                    break
                val_after_b = self._simulate_and_evaluate(og, b, self.color)
                sim_count += 1
                if val_after_b is None:
                    continue
                if val_after_b < min_score_after_opp:
                    min_score_after_opp = val_after_b

            if min_score_after_opp == float("inf"):
                min_score_after_opp = self._simulate_and_evaluate(og, None, self.color) or 0.0

            expected_value_a += p_i * min_score_after_opp

        # Compare candidate using tie-breaks
        if self.debug:
            print(
                f"Candidate {repr(a)}: expected_value={expected_value_a}, "
                f"expansion_potential={expansion_potential_a}, "
                f"1-ply vp_delta={one_ply_vp_delta}"
            )

        is_better = False
        if best_action is None:
            is_better = True
        elif expected_value_a > best_value:
            is_better = True
        elif expected_value_a == best_value:
            if expansion_potential_a > best_expansion:
                is_better = True
            elif expansion_potential_a == best_expansion:
                if one_ply_vp_delta > best_vp_delta:
                    is_better = True
                elif one_ply_vp_delta == best_vp_delta:
                    if best_repr is None or repr(a) < best_repr:
                        is_better = True

        if is_better:
            best_action = a
            best_value = expected_value_a
            best_expansion = expansion_potential_a
            best_vp_delta = one_ply_vp_delta
            best_repr = repr(a)

    # Return best action or fallback
    if best_action is not None:
        if self.debug:
            print("decide: selected (2-ply) action:", repr(best_action), "value:", best_value)
            print("Diagnostics:", self._diag)
        return best_action
    else:
        if self.debug:
            print("decide: no 2-ply choice; falling back to best 1-ply action")
        best_action_1ply = None
        best_score = -float("inf")
        best_vp = -float("inf")
        best_repr = None
        for (a, s, v) in one_ply_results:
            tie_repr = repr(a)
            is_better = False
            if best_action_1ply is None:
                is_better = True
            elif s > best_score:
                is_better = True
            elif s == best_score:
                if v > best_vp:
                    is_better = True
                elif v == best_vp and (best_repr is None or tie_repr < best_repr):
                    is_better = True
            if is_better:
                best_action_1ply = a
                best_score = s
                best_vp = v
                best_repr = tie_repr

        if best_action_1ply is not None:
            if self.debug:
                print("decide: chosen action (1-ply fallback):", repr(best_action_1ply), "score:", best_score, "vp_delta:", best_vp)
                print("Diagnostics:", self._diag)
            return best_action_1ply
        else:
            if self.debug:
                print("decide: no choice after fallbacks; returning first playable action")
                self._diag["n_fallbacks_to_first_action"] += 1
            return actions[0]
```

---
### **4. Debug Experiment Plan**
1. **Run 3 Games with `debug=True`**:
   - Enable `debug=True` for the player instance.
   - Capture logs for each game to verify:
     - Reliability of evaluators (`eval_success_rate` and `spectrum_success_rate`).
     - Expansion potential and 2-ply candidate selection.
     - Simulated nodes and runtime.

2. **Analyze Logs**:
   - Check for frequent evaluation failures or fallbacks.
   - Validate that 2-ply is running when expected and that expansion potential is influencing decisions.

3. **Run 30-Game Batch**:
   - Disable `debug` and run the full batch.
   - Report metrics: win rate, avg VP, avg roads, and diagnostics.

---
### **Expected Outcome**
- **Win Rate**: ≥50%.
- **Avg VP**: Within 0.5 of the opponent.
- **Avg Roads**: ≥0.5.
- **Diagnostics**: Evaluator success rate ≥85% and simulated nodes ≤ `MAX_SIMULATION_NODES`.

---
### **Deliverable for CODER**
1. Implement the provided code snippets.
2. Run 3 games with `debug=True` and share logs.
3. Run the 30-game batch and report metrics.

END STRATEGIZER
================================== Ai Message ==================================

- META THOUGHTS:
  - We must reintroduce a conservative, reliable 2-ply that improves long-term expansion without destabilizing runtime. The STRATEGIZER provided ready-to-paste snippets and precise parameters. CODER should implement these exactly, keep all adapter calls defensive, and run short debug runs before the full batch.
  - Key risks: adapter signature mismatches, high simulation cost, or frequent evaluation failures. We must log diagnostics and abort 2-ply safely if reliability thresholds fail.

- META GOAL:
  - Implement the conservative selective 2-ply with expansion potential exactly as specified, run 3 debug games (debug=True) to collect per-decision diagnostics and up to 5 stack traces if failures occur, then run the 30-game batch (debug=False) if diagnostics are healthy. Return the debug logs and the 30-game metrics JSON plus aggregate diagnostics (how often 2-ply ran/skipped, avg simulated nodes).

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the prepared code changes in foo_player.py now and run the prescribed experiments. Follow these exact steps and requirements; be defensive and deterministic; do not introduce hand-tuned heuristics beyond the expansion-potential metric and phase-aware sampling.

1) Apply Parameter Updates (inside FooPlayer):
   - MAX_ACTIONS_TO_EVAL = 80
   - SAMPLE_PER_ACTION_TYPE = 4
   - SPECTRUM_MAX_OUTCOMES = 8
   - EARLY_TURN_THRESHOLD = 30
   - TOP_K_1PLY = 6
   - OP_MAX_ACTIONS = 10
   - OP_SAMPLE_PER_ACTION_TYPE = 2
   - MAX_SIMULATION_NODES = 4000
   - MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.85
   - MIN_SPECTRUM_SUCCESS_RATE = 0.7

2) Insert helper method _compute_expansion_potential exactly as provided:
   - Use copy_game, execute_spectrum, expand_spectrum, execute_deterministic, _normalize_and_cap_spectrum, and _derive_opponent_actions inside it.
   - Return -inf on simulation/evaluation failures so candidates with unreliable sims are deprioritized.

3) Replace decide() with the full new decide implementation provided:
   - Reset diagnostics per decision (self._diag).
   - Stage 1: 1-ply sampling/eval via eval_fn = getattr(self, "_evaluate_action", None) or getattr(self, "_simulate_and_evaluate", None).
   - Stage 2: Compute eval_success_rate and spectrum_success_rate and skip 2-ply if below thresholds (log when debug).
   - Stage 3: Build candidate pool: always top 3 by 1-ply score, then add up to TOP_K_1PLY total candidates chosen by highest expansion_potential (computed via _compute_expansion_potential).
   - Stage 4: Conservative adversarial 2-ply as specified; count simulated nodes globally and abort deepening if MAX_SIMULATION_NODES reached.
   - Tie-break order: expected_value, expansion_potential, 1-ply vp_delta, repr(action). Deterministic RNG must be used elsewhere, but no randomness during 2-ply selection.

4) Ensure phase-aware sampling (_sample_actions) and _is_road_action helper are implemented as previously agreed (use RNG seed RNG_SEED + stable color hash).

5) Instrument diagnostics:
   - Maintain and update self._diag keys: n_candidates, n_eval_attempts, n_eval_success, n_spectrum_calls, n_spectrum_success, n_det_calls, n_det_success, n_skipped, n_fallbacks_to_first_action.
   - Print per-decision diagnostics when self.debug True:
     - eval_success_rate, spectrum_success_rate, candidate pool with expansion potentials, per-candidate expected values, sim_count, and note when MAX_SIMULATION_NODES is hit.
   - Aggregate counts of how many times 2-ply was executed vs skipped across the experiment and average simulated nodes per 2-ply invocation.

6) Defensive/error handling:
   - Wrap every adapter call (copy_game, execute_spectrum, expand_spectrum, execute_deterministic, base_fn, list_prunned_actions) with try/except. On exception, log full traceback if self.debug True and continue with fallback behavior.
   - If more than 50% of candidate sims fail, skip 2-ply and choose best 1-ply.
   - Never let decide() raise an exception; always return a playable action (prefer best 1-ply then actions[0]).

7) Determinism:
   - Continue using deterministic RNG seeding for sampling: random.Random(self.RNG_SEED + stable_color_hash).
   - Do not use new randomness in 2-ply choice or expansion potential calculation.

8) Testing plan to run now:
   A) Debug smoke run:
      - Set player.debug = True.
      - Run 3 games, capturing full stdout. For each game record:
        - Per-decision diagnostic prints (as implemented).
        - Up to 5 full stack traces for the first failures (if any).
      - Return collected logs.

   B) Analysis gate:
      - If debug logs show eval_success_rate >= MIN_EVAL_SUCCESS_RATE_FOR_2PLY and spectrum_success_rate >= MIN_SPECTRUM_SUCCESS_RATE and sim_count per 2-ply calls stays below MAX_SIMULATION_NODES:
         - Proceed to step C.
      - Else:
         - Report logs and the recommendation to call RESEARCHER if adapter mismatches appear.

   C) Full experiment:
      - Set player.debug = False.
      - Run the 30-game batch vs AlphaBetaPlayer.
      - Return the performance JSON plus aggregate diagnostics:
        - times_2ply_run, times_2ply_skipped, avg_simulated_nodes_per_2ply, eval_success_rate (aggregated), spectrum_success_rate (aggregated), avg roads for FooPlayer.

9) Deliverables to return:
   - From the 3-game debug run: the full debug logs (per-decision prints) and up to 5 stack traces (if any).
   - From the 30-game batch: the JSON metrics (player summary and game summary) and aggregate diagnostics (how often 2-ply ran/skipped, avg simulated nodes).
   - If any adapter signature mismatches or persistent exceptions occur during debug or batch, stop the batch and return the full stack traces verbatim so we can call RESEARCHER.

10) Reporting format:
   - Provide the debug logs as raw text.
   - Provide the 30-game metrics as the JSON we have used previously.
   - Provide a short summary bullet list of key diagnostics (2-ply run count, avg nodes, eval rates, whether MAX_SIMULATION_NODES was hit).

Proceed to implement the snippets and run the 3-game debug experiment. Return the logs and any traces. After we inspect, we will instruct you whether to proceed with the 30-game batch.
================================ Human Message =================================

CODER

I updated foo_player.py with the STRATEGIZER's conservative selective 2-ply reintroduction and expansion-potential logic, applied the stabilization edits you requested, and preserved deterministic behavior and robust adapter usage.

What I changed (summary):
- Re-enabled selective 2-ply conservatively with TOP_K_1PLY = 6, OP_MAX_ACTIONS = 10, OP_SAMPLE_PER_ACTION_TYPE = 2.
- Increased 1-ply sampling: MAX_ACTIONS_TO_EVAL = 80, SAMPLE_PER_ACTION_TYPE = 4.
- Added MAX_SIMULATION_NODES = 4000 and reliability thresholds MIN_EVAL_SUCCESS_RATE_FOR_2PLY and MIN_SPECTRUM_SUCCESS_RATE.
- Implemented _compute_expansion_potential to estimate the average number of playable actions after a candidate action, returning -inf on failures.
- Reworked decide() to:
  - Run 1-ply sampling/evaluation.
  - Check evaluator reliability and skip 2-ply if below thresholds.
  - Build candidate pool including top-3 by 1-ply score plus expansion-promoting candidates up to TOP_K_1PLY.
  - Run conservative adversarial 2-ply with caps and simulated node counting.
  - Tie-break on expected_value, expansion_potential, 1-ply vp_delta, repr(action).
  - Extensive debug printing when self.debug is True.
- Instrumented diagnostics counters in self._diag (counts for n_2ply_runs, n_2ply_skipped, etc.).
- All adapter calls (copy_game, execute_spectrum, expand_spectrum, execute_deterministic, base_fn, list_prunned_actions) are wrapped in try/except; failures are logged under debug and handled via fallbacks.
- Ensured decide never raises and falls back deterministically to best 1-ply or the first action.

Next steps (please confirm):
- Run the 3-game debug experiment with player.debug = True to collect per-decision logs and up to 5 stack traces if failures occur.
- If diagnostics are acceptable (eval success rate and spectrum success rate above thresholds and sim node usage reasonable), run the 30-game batch with debug=False and return the JSON metrics plus aggregate diagnostics.

If you want me to run the debug experiment now, I will proceed and return the logs. END CODER
================================ Human Message =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 16,
      "AVG VP": 8.233333333333333,
      "AVG SETTLES": 2.433333333333333,
      "AVG CITIES": 2.2,
      "AVG ROAD": 0.7,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 14,
      "AVG VP": 6.866666666666666,
      "AVG SETTLES": 2.1,
      "AVG CITIES": 2.0,
      "AVG ROAD": 0.3,
      "AVG ARMY": 0.06666666666666667,
      "AVG DEV VP": 0.03333333333333333
    }
  },
  "Game Summary": {
    "AVG TICKS": 218.86666666666667,
    "AVG TURNS": 77.46666666666667,
    "AVG DURATION": 1.906960662206014
  }
}
================================ Human Message =================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

---
### PERFORMANCE SUMMARY
- **Outcome**: **Borderline** (14 wins, 16 losses).
- **VP Comparison**: FooPlayer (BLUE) averaged **6.87 VP** vs. AlphaBetaPlayer (RED) **8.23 VP**.
- **VP Difference**: **-1.36** (below parity).
- **Key Counts**:
  - FooPlayer: 2.1 settlements, 2.0 cities, 0.3 roads, 0.07 armies, 0.03 dev cards.
  - Opponent: 2.43 settlements, 2.2 cities, 0.7 roads, 0.0 armies, 0.0 dev cards.
  - **Total Turns**: 77.47 turns per game.

---
### VERDICT
**Borderline**: FooPlayer is competitive but underperforms, with a **VP difference of -1.36**.

---
### LIKELY REASONS
1. **Road-Building Deficiency**:
   - FooPlayer averages **0.3 roads** compared to the opponent's **0.7 roads**, indicating a **lack of expansion strategy**.
   - **Corroborating Log**: The opponent consistently builds more roads, which is critical for Longest Road and resource access.

2. **Robber and Development Card Underutilization**:
   - FooPlayer averages **0.07 armies** and **0.03 dev cards**, while the opponent leverages these better.
   - **Corroborating Result**: The opponent's higher VP suggests better use of disruptive strategies (e.g., robber placement).

3. **Tie-Breaking Logic**:
   - The `decide` method (lines 500–550) uses **lexicographical tie-breaking** (`repr(action)`), which may not prioritize strategically superior actions (e.g., city upgrades over roads).

4. **2-Ply Adversarial Evaluation**:
   - While the 2-ply lookahead is implemented, it **fails to simulate opponent responses effectively** due to:
     - **Low reliability thresholds** (`MIN_EVAL_SUCCESS_RATE_FOR_2PLY=0.85` and `MIN_SPECTRUM_SUCCESS_RATE=0.7`).
     - **Corroborating Log**: The opponent's higher VP suggests FooPlayer's 2-ply evaluation is **not robust enough** to counter adversarial strategies.

---
### STRATEGIC FLAW
FooPlayer's **2-ply adversarial evaluation lacks depth and robustness**, leading to suboptimal road-building, robber placement, and development card usage. The tie-breaking logic also fails to prioritize high-impact actions.

---
### NEXT STEP
Send to **Strategizer** to **refine 2-ply evaluation thresholds**, **prioritize road-building and robber strategies**, and **improve tie-breaking logic**.

---
Let me know if you need anything else.
END ANALYZER
================================== Ai Message ==================================

- META THOUGHTS:
  - The Analyzer shows FooPlayer is close but still underperforms (14/16). The core problems: low road-building, weak robber/dev usage, and tie-breaking that doesn't favor high-impact long-term actions. The conservative 2-ply reintroduction helped but didn't correct these specific strategic deficits.
  - We must avoid hand-tuned heuristics. Instead use algorithmic, adapter-driven signals (expansion potential, counts of build/road playable actions, expected opponent expansion loss from robber actions, base_fn changes) to steer the selective 2-ply and candidate selection.
  - We should make targeted changes (small, testable) that: (1) ensure road/expansion candidates are always considered, (2) evaluate robber actions by measuring adversary impact, (3) improve tie-breaks with measurable future-build metrics, and (4) make 2-ply activation adaptive and more robust (looser reliability thresholds but with safer deterministic fallbacks).

- META GOAL:
  - Produce a precise, implementable strategy specification (no heuristics) that CODER can apply to foo_player.py so the next experiment improves roads, robber/dev usage, and overall win-rate. Include exact parameter adjustments, metrics to compute from adapters, integration points, and diagnostic outputs.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Produce a concrete, adapter-driven refinement plan to fix the strategic flaws the Analyzer identified. The plan must be implementable as focused code edits in foo_player.py (small, safe, deterministic). Provide exact pseudocode and parameter recommendations CODER can paste. Requirements: do not invent new handcrafted value functions — only use adapters (copy_game, execute_spectrum/expand_spectrum, execute_deterministic, derive_playable_actions / derive_opponent_actions, base_fn, visible VP). Be explicit about tie-break rules, simulation budgeting, and diagnostics to collect.

Plan (actionable items with pseudocode & parameter suggestions)

1) Adaptive 2-ply activation (safer, more useful)
- New idea: enable 2-ply when it is most likely to help:
  - Early/mid game when expansion matters, OR
  - When top 1-ply scores are close (ambiguous immediate best).
- Change thresholds:
  - MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.80 (lower slightly to allow more 2-ply when deterministic fallbacks exist)
  - MIN_SPECTRUM_SUCCESS_RATE = 0.60
- Additional activation conditions:
  - If (best_1ply_score - second_best_1ply_score) < SCORE_AMBIGUITY_THRESHOLD (choose small value, e.g., 0.05 of normalized scale) => run 2-ply.
  - If any candidate is a road-building action or robber action and expansion potential for top candidates differs significantly (std dev > small threshold) => run 2-ply.

Pseudocode check before 2-ply:
eval_success_rate = n_eval_success / max(1, n_eval_attempts)
spectrum_success_rate = ...
score_gap = best_score - second_best_score
if (eval_success_rate >= 0.80 and spectrum_success_rate >= 0.60) or (score_gap < SCORE_AMBIGUITY_THRESHOLD) or (exists road/robber candidate with exp_potential > X):
    allow_2ply = True
else:
    allow_2ply = False

2) Guarantee road/expansion candidate inclusion
- Always include at least one road action in the candidate_pool when a road is playable and its expansion_potential is non-negative.
- Implementation:
  - Compute expansion_potential for all road actions among candidates.
  - If any road action exists with expansion_potential >= 0 (i.e., we could simulate it), include the top road action in candidate_pool even if it’s outside top_by_1ply / top-K by expansion.
- Rationale: ensures we explicitly evaluate expansion moves.

Pseudocode:
road_candidates = [a for a in candidates if _is_road_action(a)]
road_scores = {a: _compute_expansion_potential(game,a) for a in road_candidates}
if road_scores:
    best_road = argmax(road_scores)
    if best_road not in candidate_pool:
        candidate_pool.append(best_road)

3) Robber action evaluation by opponent-impact metric
- For candidate actions that are robber-like:
  - Compute opponent_expansion_before: average expansion potential for opponents on current game (simulate none).
  - For each outcome of our robber action, compute opponent_expansion_after (simulate state, then compute opponent expansion potential from derive_opponent_actions or by counting opponent playable build/road actions).
  - Opponent impact metric = opponent_expansion_before - E[opponent_expansion_after]. Positive means we reduced opponents’ future options.
- Use opponent impact as a tertiary (or secondary for robber candidates) tie-breaker favoring robber actions that reduce opponents' expansion.
- Implementation details:
  - Use _compute_expansion_potential but with opponent color and counting their playable actions.
  - If simulation fails for robber candidate, treat impact as -inf for prioritization (prefer reliable sims).

Pseudocode for robber candidate:
opp_before = average_playable_count_for_opp(game)
for outcome_game, p in outcomes:
    opp_after = count_playable_for_opp(outcome_game)
    expected_opp_after += p * opp_after
opp_impact = opp_before - expected_opp_after

Include opp_impact in candidate comparison, higher is better for robber actions.

4) Future-build-count metric for tie-breaking (algorithmic, not heuristic)
- Compute future_build_count(a) = E[number of build actions available to my_color in immediate next state after a].
- This is simply expansion_potential but specifically counting build-type playable actions (settlement/city/road/dev plays).
- Use as secondary tie-breaker after expected_value and expansion_potential to prefer actions that increase our ability to build.

Pseudocode:
future_build_count = sum_i p_i * count_build_actions(outcome_game_i)

Tie-break order:
1. expected_value (2-ply)
2. expansion_potential (our average playable actions)
3. opp_impact if action is robber (higher better)
4. future_build_count (higher better)
5. 1-ply vp_delta
6. repr(action)

5) More robust opponent simulation fallbacks
- When opponent spectrum simulation fails, instead of skipping the opponent action entirely, fallback to:
  - Deterministic simulation for the opponent action if available.
  - If both fail, use safe_eval_base_fn(outcome_game) as approximation for that branch (no opponent action).
- This reduces the number of skipped opponent branches and therefore reduces candidate failures.
- Also keep MAX_SIMULATION_NODES hard cap; allocate simulation budget proportionally:
  - sim_budget_per_candidate = min(MAX_SIMULATION_NODES_left, MAX_SIMULATION_NODES / max(1, len(candidate_pool))) and track simulated nodes consumed.

Pseudocode inside opponent loop:
try:
    opp_outcomes = execute_spectrum(og, b)
except:
    try:
        det = execute_deterministic(og, b)
        opp_outcomes = [(det_game, 1.0)]
    except:
        # approximate: evaluate og itself (no opponent action effect)
        score_after_b = safe_eval_base_fn(og)

6) Slight parameter adjustments (recommendations)
- MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.80
- MIN_SPECTRUM_SUCCESS_RATE = 0.60
- SCORE_AMBIGUITY_THRESHOLD = 0.05  # small
- Keep MAX_SIMULATION_NODES = 4000, TOP_K_1PLY = 6, OP_MAX_ACTIONS = 10

7) Diagnostics to collect (must be printed under debug)
- Number of times 2-ply was executed vs skipped and reason for skipping (low eval rate, low spectrum rate, sim cap).
- For each decide where 2-ply runs, print:
  - candidate_pool list with (is_road, is_robber, expansion_potential, future_build_count)
  - For robber candidates: opponent_impact metric and its sign.
  - For each candidate: expected_value_a and sim_nodes_used_for_candidate.
  - sim_nodes total and whether cap hit.
- Add counters: n_road_candidates_included, n_robber_candidates_included, avg_opponent_impact_for_chosen_robber (aggregated over games).

8) Small safe code-level changes CODER must make (summary)
- Add inclusion rule for road candidate (guarantee one road candidate).
- Compute opponent_impact for robber candidates and include in tie-break comparison.
- Lower reliability thresholds to 0.80 and 0.60.
- Add fallback for opponent spectrum failures: attempt deterministic, then approximate by evaluating current outcome_game.
- Implement sim budget allocation per candidate and track sim_count; abort safely when cap reached.
- Add the requested diagnostics prints.

9) Hypothesis / Success criteria
- If implemented, we expect:
  - Increase in average roads (aim ≥ 0.5).
  - Smaller VP gap (target within 0.5).
  - Improved win rate (>= 50%).
  - Diagnostics will show 2-ply enabled more often for ambiguous or expansion-critical decisions and robber actions will be selected when they demonstrably reduce opponent expansion potential.

10) Quick pseudocode snippet to compare two candidates (to paste inside candidate evaluation loop):
```
# After computing expected_value_a, expansion_potential_a, future_build_count_a
robber_impact_a = -float('inf')
if self._is_robber_or_chance(a):
    robber_impact_a = compute_opponent_impact(game, a)  # implement as described

# candidate comparison:
if best_action is None or expected_value_a > best_value:
    choose a
elif expected_value_a == best_value:
    if expansion_potential_a > best_expansion: choose a
    elif expansion_potential_a == best_expansion:
        if robber_impact_a > best_robber_impact: choose a
        elif robber_impact_a == best_robber_impact:
            if future_build_count_a > best_future_build: choose a
            elif future_build_count_a == best_future_build:
                if one_ply_vp_delta > best_vp_delta: choose a
                elif equal: use repr tie-break
```

Deliverable for CODER:
- Implement the above adapter-driven refinements:
  - Road guarantee inclusion
  - Robber opponent-impact metric
  - future_build_count tie-break
  - Opponent simulation fallbacks and sim budget allocation
  - Lowered thresholds and ambiguity-based activation
  - Diagnostics additions
- Run 3 debug games with debug=True and return per-decision logs (highlight road inclusion and robber impact prints).
- If diagnostics indicate improvements and sim nodes are within budget, run 30 games and return the results.

Would you like me to produce the exact ready-to-paste code snippets for the robber impact computation, opponent fallback logic, sim-budget allocation, and the candidate comparison block so CODER can paste them directly into foo_player.py?
================================ Human Message =================================

STRATEGIZER
**Ready-to-Paste Code Snippets for Strategic Refinements**

---
### **1. Update Class Parameters**
Add or update the following parameters in the `FooPlayer` class:
```python
# Adaptive 2-ply activation thresholds
MIN_EVAL_SUCCESS_RATE_FOR_2PLY: float = 0.80  # Lowered from 0.85
MIN_SPECTRUM_SUCCESS_RATE: float = 0.60      # Lowered from 0.70
SCORE_AMBIGUITY_THRESHOLD: float = 0.05      # New: Enable 2-ply for close scores
```

---
### **2. Add `compute_opponent_impact` Method**
```python
def _compute_opponent_impact(self, game: Game, action) -> float:
    """Compute the impact of a robber action on opponent expansion potential.

    Returns the difference between opponent's expansion potential before and after the action.
    """
    try:
        game_copy = copy_game(game)
    except Exception:
        if self.debug:
            print("_compute_opponent_impact: copy_game failed")
            traceback.print_exc()
        return -float("inf")

    # Determine opponent color
    opp_color = self._determine_opponent_color(game, self.color)

    # Compute opponent expansion potential before action
    opp_expansion_before = self._compute_expansion_potential(game_copy, None)  # None = evaluate current state

    # Simulate the action to get outcome branches
    outcomes = []
    try:
        if self._is_robber_or_chance(action):
            spec = None
            try:
                spec = execute_spectrum(game_copy, action)
            except Exception:
                try:
                    spec_map = expand_spectrum(game_copy, [action])
                    if isinstance(spec_map, dict):
                        spec = spec_map.get(action, None)
                except Exception:
                    spec = None
            if spec:
                outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
        else:
            det_res = execute_deterministic(game_copy, action)
            if det_res:
                outcomes = [(det_res[0][0], 1.0)] if isinstance(det_res[0], tuple) else [(det_res[0], 1.0)]
    except Exception:
        if self.debug:
            print("_compute_opponent_impact: failed to simulate action")
            traceback.print_exc()
        return -float("inf")

    if not outcomes:
        return -float("inf")

    # Compute expected opponent expansion potential after action
    expected_opp_expansion_after = 0.0
    for outcome_game, prob in outcomes:
        try:
            opp_expansion_after = self._compute_expansion_potential(outcome_game, None)
            expected_opp_expansion_after += prob * opp_expansion_after
        except Exception:
            if self.debug:
                print("_compute_opponent_impact: failed to compute expansion for outcome")
                traceback.print_exc()
            return -float("inf")

    return opp_expansion_before - expected_opp_expansion_after
```

---
### **3. Add `count_build_actions` Method**
```python
def _count_build_actions(self, game: Game, color: Color) -> int:
    """Count the number of build-type actions available to `color` in `game`."""
    try:
        playable = self._derive_opponent_actions(game, color)
        if not playable:
            return 0
        return sum(
            1 for a in playable
            if self._is_build_or_upgrade(a) or self._is_road_action(a)
        )
    except Exception:
        if self.debug:
            print("_count_build_actions: failed to derive playable actions")
            traceback.print_exc()
        return 0
```

---
### **4. Update `decide` Method for Adaptive 2-Ply Activation**
Replace the **reliability check** section in `decide` with the following:
```python
# Stage 2: Check reliability and ambiguity for 2-ply activation
eval_success_rate = self._diag.get("n_eval_success", 0) / max(1, self._diag.get("n_eval_attempts", 0))
spectrum_success_rate = (
    self._diag.get("n_spectrum_success", 0) / max(1, self._diag.get("n_spectrum_calls", 0))
    if self._diag.get("n_spectrum_calls", 0) > 0
    else 1.0
)
# Check if top 1-ply scores are close
one_ply_results.sort(key=lambda t: t[1], reverse=True)
if len(one_ply_results) > 1:
    score_gap = one_ply_results[0][1] - one_ply_results[1][1]
else:
    score_gap = float("inf")

# Check for road/robber candidates with high expansion potential
road_candidates = [a for a in candidates if self._is_road_action(a)]
robber_candidates = [a for a in candidates if self._is_robber_or_chance(a)]
has_high_potential_road = any(
    self._compute_expansion_potential(game, a) >= 0 for a in road_candidates
)
has_high_potential_robber = any(
    self._compute_opponent_impact(game, a) >= 0 for a in robber_candidates
)

# Enable 2-ply if:
# 1. Evaluators are reliable, OR
# 2. Top scores are ambiguous, OR
# 3. High-potential road/robber candidates exist
allow_2ply = (
    (eval_success_rate >= self.MIN_EVAL_SUCCESS_RATE_FOR_2PLY and spectrum_success_rate >= self.MIN_SPECTRUM_SUCCESS_RATE)
    or (score_gap < self.SCORE_AMBIGUITY_THRESHOLD)
    or has_high_potential_road
    or has_high_potential_robber
)

if self.debug:
    print(
        f"decide: eval_success_rate={eval_success_rate:.2f}, "
        f"spectrum_success_rate={spectrum_success_rate:.2f}, "
        f"score_gap={score_gap:.3f}, "
        f"has_high_potential_road={has_high_potential_road}, "
        f"has_high_potential_robber={has_high_potential_robber}, "
        f"allow_2ply={allow_2ply}"
    )

if not allow_2ply:
    self._diag["n_2ply_skipped"] = self._diag.get("n_2ply_skipped", 0) + 1
    if self.debug:
        print("decide: skipping 2-ply due to low reliability or lack of high-potential candidates")
    # Fall back to best 1-ply action
    # ... (existing fallback logic)
```

---
### **5. Update Candidate Pool Construction**
Replace the **candidate pool construction** section in `decide` with the following:
```python
# Stage 3: Build candidate pool with expansion potential and road guarantee
one_ply_results.sort(key=lambda t: (t[1], t[2]), reverse=True)
top_by_1ply = [t[0] for t in one_ply_results[:3]]  # Always include top 3 by 1-ply score
remaining_candidates = [t[0] for t in one_ply_results[3:]]

# Guarantee inclusion of at least one high-potential road candidate
road_candidates = [a for a in candidates if self._is_road_action(a)]
road_scores = {a: self._compute_expansion_potential(game, a) for a in road_candidates}
best_road = None
if road_scores:
    best_road = max(road_scores.items(), key=lambda x: x[1])[0]
    if best_road not in top_by_1ply and best_road not in remaining_candidates:
        remaining_candidates.append(best_road)

# Compute expansion potential for remaining candidates
expansion_scores = {}
for a in remaining_candidates:
    exp_potential = self._compute_expansion_potential(game, a)
    if exp_potential != -float("inf"):
        expansion_scores[a] = exp_potential

# Sort remaining candidates by expansion potential
sorted_remaining = sorted(
    expansion_scores.items(),
    key=lambda x: x[1],
    reverse=True
)
additional_candidates = [a for a, _ in sorted_remaining[: max(0, self.TOP_K_1PLY - len(top_by_1ply))]]
candidate_pool = top_by_1ply + additional_candidates

# Include best road candidate if not already present
if best_road and best_road not in candidate_pool:
    candidate_pool.append(best_road)
    if self.debug:
        print(f"decide: added guaranteed road candidate {repr(best_road)} with expansion_potential={road_scores[best_road]}")

if self.debug:
    print("Candidate pool:")
    for a in candidate_pool:
        exp_potential = expansion_scores.get(a, "N/A")
        is_road = self._is_road_action(a)
        is_robber = self._is_robber_or_chance(a)
        print(f"  {repr(a)} (is_road={is_road}, is_robber={is_robber}, expansion_potential={exp_potential})")
```

---
### **6. Update 2-Ply Evaluation Logic**
Replace the **2-ply evaluation loop** in `decide` with the following:
```python
# Stage 4: 2-ply adversarial evaluation with opponent impact and build count
best_action = None
best_value = -float("inf")
best_expansion = -float("inf")
best_robber_impact = -float("inf")
best_future_build = -float("inf")
best_vp_delta = -float("inf")
best_repr = None
sim_count = 0

# Use class cap for simulated nodes
SIMULATION_HARD_LIMIT = self.MAX_SIMULATION_NODES

# Track diagnostics
deep_successful_candidates = 0

try:
    for a in candidate_pool:
        if sim_count >= SIMULATION_HARD_LIMIT:
            if self.debug:
                print("decide: reached simulation hard limit; stopping deepening")
            break

        # Simulate our action a to produce outcome branches
        try:
            game_copy = copy_game(game)
        except Exception as e:
            if self.debug:
                print("decide: copy_game failed for candidate", repr(a), e)
                traceback.print_exc()
            continue

        # Obtain outcome branches
        outcomes = []
        try:
            if self._is_robber_or_chance(a):
                spec = None
                try:
                    spec = execute_spectrum(game_copy, a)
                except Exception:
                    try:
                        spec_map = expand_spectrum(game_copy, [a])
                        if isinstance(spec_map, dict):
                            spec = spec_map.get(a, None)
                    except Exception:
                        spec = None
                if spec:
                    outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
            if not outcomes:
                det_res = execute_deterministic(game_copy, a)
                if det_res:
                    outcomes = [(det_res[0][0], 1.0)] if isinstance(det_res[0], tuple) else [(det_res[0], 1.0)]
        except Exception as e:
            if self.debug:
                print("decide: failed to obtain outcomes for candidate", repr(a), "error:", e)
                traceback.print_exc()
            continue

        if not outcomes:
            continue

        # Cap outcomes
        if len(outcomes) > self.SPECTRUM_MAX_OUTCOMES:
            outcomes = outcomes[:self.SPECTRUM_MAX_OUTCOMES]

        if self.debug:
            print(f"Candidate {repr(a)} produced {len(outcomes)} outcome(s) to evaluate")

        expected_value_a = 0.0
        expansion_potential_a = 0.0
        robber_impact_a = -float("inf")
        future_build_count_a = 0.0
        one_ply_vp_delta = next((v for (act, s, v) in one_ply_results if act == a), 0.0)

        # Compute robber impact if applicable
        if self._is_robber_or_chance(a):
            robber_impact_a = self._compute_opponent_impact(game, a)

        for og, p_i in outcomes:
            if sim_count >= SIMULATION_HARD_LIMIT:
                break

            # Compute expansion potential for this outcome
            try:
                playable = self._derive_opponent_actions(og, self.color)
                expansion = len(playable) if playable else 0
                expansion_potential_a += p_i * expansion
            except Exception:
                if self.debug:
                    print("decide: failed to compute expansion potential for outcome")
                    traceback.print_exc()
                expansion_potential_a += p_i * -float("inf")

            # Compute future build count for this outcome
            try:
                future_build_count_a += p_i * self._count_build_actions(og, self.color)
            except Exception:
                if self.debug:
                    print("decide: failed to compute future build count for outcome")
                    traceback.print_exc()
                future_build_count_a += p_i * -float("inf")

            # Determine opponent color
            opp_color = self._determine_opponent_color(og, self.color)

            # Get opponent actions with robust fallbacks
            try:
                opp_actions = self._derive_opponent_actions(og, opp_color)
            except Exception:
                opp_actions = []

            if not opp_actions:
                val_i = self._simulate_and_evaluate(og, None, self.color)
                if val_i is None:
                    continue
                expected_value_a += p_i * val_i
                sim_count += 1
                continue

            # Prune opponent actions deterministically and cap
            opp_sampled = self._sample_opponent_actions(opp_actions, og, opp_color)[: self.OP_MAX_ACTIONS]

            if self.debug:
                print(f"  outcome p={p_i:.3f}: opp_actions={len(opp_actions)} -> sampled={len(opp_sampled)}")

            # Adversarial evaluation
            min_score_after_opp = float("inf")
            opp_successes = 0
            for b in opp_sampled:
                if sim_count >= SIMULATION_HARD_LIMIT:
                    break
                # Try spectrum first for opponent chance actions
                if self._is_robber_or_chance(b):
                    try:
                        opp_spec = execute_spectrum(og, b)
                        if opp_spec:
                            opp_outcomes = self._normalize_and_cap_spectrum(opp_spec, self.SPECTRUM_MAX_OUTCOMES)
                            opp_scores = []
                            for opp_og, opp_p in opp_outcomes:
                                opp_sc = self._safe_eval_base_fn(opp_og, self.color)
                                if opp_sc is not None:
                                    opp_scores.append(opp_p * opp_sc)
                            if opp_scores:
                                opp_val = sum(opp_scores)
                                min_score_after_opp = min(min_score_after_opp, opp_val)
                                opp_successes += 1
                    except Exception:
                        pass
                # Fallback to deterministic
                if min_score_after_opp == float("inf"):
                    try:
                        opp_det = execute_deterministic(og, b)
                        if opp_det:
                            opp_det_og = opp_det[0][0] if isinstance(opp_det[0], tuple) else opp_det[0]
                            opp_sc = self._safe_eval_base_fn(opp_det_og, self.color)
                            if opp_sc is not None:
                                min_score_after_opp = min(min_score_after_opp, opp_sc)
                                opp_successes += 1
                    except Exception:
                        pass
                # Last resort: evaluate og directly
                if min_score_after_opp == float("inf"):
                    opp_sc = self._safe_eval_base_fn(og, self.color)
                    if opp_sc is not None:
                        min_score_after_opp = opp_sc

                sim_count += 1

            if opp_successes == 0:
                min_score_after_opp = self._simulate_and_evaluate(og, None, self.color) or 0.0

            expected_value_a += p_i * min_score_after_opp

        # Compare candidate using tie-breaks
        if self.debug:
            print(
                f"Candidate {repr(a)}: expected_value={expected_value_a}, "
                f"expansion_potential={expansion_potential_a}, "
                f"robber_impact={robber_impact_a}, "
                f"future_build_count={future_build_count_a}, "
                f"1-ply vp_delta={one_ply_vp_delta}"
            )

        is_better = False
        if best_action is None:
            is_better = True
        elif expected_value_a > best_value:
            is_better = True
        elif expected_value_a == best_value:
            if expansion_potential_a > best_expansion:
                is_better = True
            elif expansion_potential_a == best_expansion:
                if robber_impact_a > best_robber_impact:
                    is_better = True
                elif robber_impact_a == best_robber_impact:
                    if future_build_count_a > best_future_build:
                        is_better = True
                    elif future_build_count_a == best_future_build:
                        if one_ply_vp_delta > best_vp_delta:
                            is_better = True
                        elif one_ply_vp_delta == best_vp_delta:
                            tie_repr = repr(a)
                            if best_repr is None or tie_repr < best_repr:
                                is_better = True

        if is_better:
            best_action = a
            best_value = expected_value_a
            best_expansion = expansion_potential_a
            best_robber_impact = robber_impact_a
            best_future_build = future_build_count_a
            best_vp_delta = one_ply_vp_delta
            best_repr = repr(a)
```

---
### **7. Add Debug Diagnostics**
Add the following to the `decide` method after the 2-ply evaluation loop:
```python
if self.debug:
    print("2-ply diagnostics:")
    print(f"  n_2ply_runs: {self._diag.get('n_2ply_runs', 0)}")
    print(f"  n_2ply_skipped: {self._diag.get('n_2ply_skipped', 0)}")
    print(f"  sim_count: {sim_count} / {SIMULATION_HARD_LIMIT}")
    print(f"  best_action: {repr(best_action)}")
    print(f"  best_value: {best_value}")
    print(f"  best_expansion: {best_expansion}")
    print(f"  best_robber_impact: {best_robber_impact}")
    print(f"  best_future_build: {best_future_build}")
```

---
### **8. Deliverable for CODER**
1. **Implement the provided snippets** in `foo_player.py`.
2. **Run 3 games with `debug=True`** and share logs.
3. **Run the 30-game batch** and report metrics.

---
### **Expected Outcome**
- **Win Rate**: ≥50%.
- **Avg Roads**: ≥0.5.
- **Avg VP**: Within 0.5 of the opponent.
- **Diagnostics**: 2-ply enabled for ambiguous or expansion-critical decisions.

END STRATEGIZER
================================== Ai Message ==================================

- META THOUGHTS:
  - Analyzer shows FooPlayer is close but still underperforming. The STRATEGIZER's refinements (road guarantee, robber opponent-impact, future-build tie-breaks, adaptive 2-ply activation, opponent-fallbacks, sim-budgeting) are appropriate, narrowly scoped, and adapter-driven. They should improve expansion and disruptive play without introducing handcrafted heuristics.
  - Next actionable step is to have CODER implement the STRATEGIZER's ready-to-paste snippets exactly, run a short debug experiment to gather diagnostics, and proceed to the full batch only if diagnostics show evaluator reliability and reasonable simulation costs.

- META GOAL:
  - Implement the STRATEGIZER's refinements in foo_player.py, run 3 debug games (player.debug=True) to collect per-decision diagnostics and up to 5 stack traces if failures occur, then — if diagnostics pass thresholds — run the 30-game batch and return the JSON metrics plus aggregate diagnostics.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement and test the STRATEGIZER's ready-to-paste refinements exactly as provided. Follow these steps and return the requested outputs.

1) Code changes to implement (apply exactly):
   - Update class parameters:
     - MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.80
     - MIN_SPECTRUM_SUCCESS_RATE = 0.60
     - SCORE_AMBIGUITY_THRESHOLD = 0.05
     - Keep previously set: TOP_K_1PLY = 6, OP_MAX_ACTIONS = 10, OP_SAMPLE_PER_ACTION_TYPE = 2, MAX_SIMULATION_NODES = 4000, MAX_ACTIONS_TO_EVAL = 80, SAMPLE_PER_ACTION_TYPE = 4, SPECTRUM_MAX_OUTCOMES = 8, EARLY_TURN_THRESHOLD = 30.

   - Add / paste the helper methods:
     - _compute_opponent_impact (exact snippet).
     - _count_build_actions (exact snippet).
     - Ensure _compute_expansion_potential already exists; if not, use the provided one previously.

   - Replace the reliability check in decide() with the Adaptive 2-Ply Activation snippet (section 4). Make sure to compute eval_success_rate and spectrum_success_rate using self._diag counters.

   - Replace candidate pool construction with the Updated Candidate Pool snippet (section 5). Guarantee inclusion of at least one high-potential road candidate.

   - Replace the 2-ply evaluation loop in decide() with the Updated 2-Ply Evaluation Logic snippet (section 6). Ensure:
     - sim_count tracks simulated nodes; enforce SIMULATION_HARD_LIMIT = self.MAX_SIMULATION_NODES.
     - Fallbacks for opponent spectrum failures: try deterministic, else evaluate og directly.
     - All adapter calls wrapped in try/except and log full tracebacks when self.debug True.
     - Tie-break order implemented: expected_value > expansion_potential > robber_impact > future_build_count > 1-ply vp_delta > repr(action).
     - Update diagnostic counters: increment n_2ply_runs when 2-ply actually executed, increment n_2ply_skipped when skipped and record reason.

   - Add debug diagnostics print block after the 2-ply loop (section 7).

2) Defensive requirements:
   - Any adapter call (copy_game, execute_spectrum, expand_spectrum, execute_deterministic, base_fn, derive_playable_actions/_derive_opponent_actions) must be in try/except. On exception, log stack trace if self.debug True, handle gracefully (treat as simulation failure or use fallback evaluation), and never let decide() raise.
   - If more than 50% of candidate sims fail for a decide() invocation, skip 2-ply and fall back to best 1-ply.
   - Allocate sim budget per candidate proportionally or simply break when sim_count >= MAX_SIMULATION_NODES; ensure deterministic ordering of candidate evaluation.

3) Determinism:
   - Preserve RNG seeding (random.Random(self.RNG_SEED + stable_color_hash)) everywhere sampling is used.
   - No use of randomization in 2-ply choice or tie-breaks.

4) Testing plan to run now:
   A) Debug smoke run:
     - Set player.debug = True.
     - Run 3 full games against AlphaBetaPlayer.
     - Capture and return the raw debug logs (stdout) that include:
       - Per-decision diagnostics printed from decide()
       - Candidate pool prints, expansion_potential, robber_impact, future_build_count, expected_value per candidate, sim_count per decision, and why 2-ply was skipped when applicable.
     - If any adapter exceptions occur, capture and return up to the first 5 full stack traces (raw).

   B) Analysis gate:
     - Evaluate aggregated diagnostics from 3 games:
       - eval_success_rate >= MIN_EVAL_SUCCESS_RATE_FOR_2PLY (0.80)
       - spectrum_success_rate >= MIN_SPECTRUM_SUCCESS_RATE (0.60) when spectrum used
       - Average sim_count per 2-ply invocation substantially below MAX_SIMULATION_NODES (preferably < 0.5 * MAX_SIMULATION_NODES)
       - Check n_2ply_runs > 0 and n_road_candidates_included > 0 across games
     - If diagnostics meet these criteria, proceed to the full batch. Otherwise, return logs and traces for further analysis / call RESEARCHER if adapter signature mismatches are observed.

   C) Full experiment:
     - Set player.debug = False.
     - Run 30-game batch vs AlphaBetaPlayer.
     - Return the JSON metrics (Player Summary & Game Summary) plus aggregate diagnostics:
       - times_2ply_run, times_2ply_skipped (with reasons), avg_simulated_nodes_per_2ply, eval_success_rate (aggregate), spectrum_success_rate (aggregate), avg roads for FooPlayer.

5) Reporting format:
   - Provide the 3-game debug logs as raw text output.
   - Include up to 5 full stack traces verbatim if any exceptions occurred during debug runs.
   - Provide the 30-game experiment JSON exactly as before.
   - Add a short summary with these aggregated diagnostics: times_2ply_run, times_2ply_skipped, avg_sim_nodes, eval_success_rate, spectrum_success_rate, avg roads.

6) Failure handling:
   - If decide() still raises AttributeError or other unexpected unhandled exception, stop experiments immediately and return the full stack trace(s) (then we will call RESEARCHER).
   - If many simulation failures occur (more than 50% of candidate sims across decides), stop and return logs so STRATEGIZER can propose alternate remedy.

Execute these steps now: implement the code edits, run the 3-game debug experiment (player.debug=True), and return the raw logs and up to 5 stack traces (if any). After we inspect the debug output, we'll instruct you whether to run the 30-game batch.
================================ Human Message =================================

CODER

I updated foo_player.py with the STRATEGIZER's conservative selective 2-ply refinements and stabilization changes you requested. Summary of the exact edits applied:

- Parameters updated:
  - MAX_ACTIONS_TO_EVAL = 80
  - SAMPLE_PER_ACTION_TYPE = 4
  - SPECTRUM_MAX_OUTCOMES = 8
  - EARLY_TURN_THRESHOLD = 30
  - TOP_K_1PLY = 6
  - OP_MAX_ACTIONS = 10
  - OP_SAMPLE_PER_ACTION_TYPE = 2
  - MAX_SIMULATION_NODES = 4000
  - MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.80
  - MIN_SPECTRUM_SUCCESS_RATE = 0.60
  - SCORE_AMBIGUITY_THRESHOLD = 0.05
  - RNG_SEED = 0 (kept)

- Implemented and added:
  - _compute_opponent_impact (robber opponent-impact metric).
  - _count_build_actions (build-action counter).
  - Improved _sample_actions to bias roads mid-game and builds early-game (deterministic RNG).
  - _sample_opponent_actions implemented for opponent pruning.
  - _normalize_and_cap_spectrum sorts by probability before truncating.
  - _evaluate_action added (robust, spectrum-aware, returns (score, vp_delta)).
  - _simulate_and_evaluate used for deterministic/chance single-state evaluation.
  - _compute_expansion_potential returns average playable-actions count after action; -inf on failure.
  - decide() rewritten to:
    - 1-ply evaluation with diagnostics
    - adaptive 2-ply activation (ambiguity and high-potential road/robber overrides)
    - candidate pool building with guaranteed road inclusion
    - conservative adversarial 2-ply with sim_count budget and robust opponent fallbacks
    - tie-breaks preferring expected_value, expansion_potential, future build counts, vp_delta, then repr
    - detailed debug prints when self.debug True
    - never raise exceptions; fallbacks to 1-ply or first action

- Diagnostics:
  - self._diag counters populated and updated: n_candidates, n_eval_attempts/success, n_spectrum_calls/success, n_det_calls/success, n_skipped, n_fallbacks_to_first_action, n_2ply_runs, n_2ply_skipped, n_road_candidates_included, simulated_nodes_total.
  - Debug prints added for per-decision and per-candidate diagnostics; enable with player.debug = True.

- Adapter usage:
  - Uses only functions from adapters.py: copy_game, execute_deterministic, execute_spectrum, expand_spectrum, list_prunned_actions, base_fn (via base_fn()).
  - All adapter calls wrapped in try/except; failures logged if debug True and handled gracefully.

Next steps (I can run them now if you confirm):
1) Run 3 debug games (player.debug = True) to collect per-decision logs and up to 5 stack traces if failures occur.
2) If diagnostics meet thresholds, run the 30-game batch with player.debug = False and report the JSON metrics plus aggregated diagnostics.

Tell me to proceed with the debug runs now and I'll run them and return logs. END CODER
================================ Human Message =================================

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 20,
      "AVG VP": 8.833333333333334,
      "AVG SETTLES": 2.566666666666667,
      "AVG CITIES": 2.3666666666666667,
      "AVG ROAD": 0.7666666666666667,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 10,
      "AVG VP": 6.666666666666667,
      "AVG SETTLES": 2.0,
      "AVG CITIES": 2.033333333333333,
      "AVG ROAD": 0.23333333333333334,
      "AVG ARMY": 0.03333333333333333,
      "AVG DEV VP": 0.06666666666666667
    }
  },
  "Game Summary": {
    "AVG TICKS": 207.5,
    "AVG TURNS": 73.33333333333333,
    "AVG DURATION": 1.8830600738525392
  }
}
================================ Human Message =================================

ANALYZER
---
After Running The New foo_player.py Player, Here is my analysis and findings:

---
### PERFORMANCE SUMMARY
- **Outcome**: **Poor** (10 wins, 20 losses).
- **VP Comparison**: FooPlayer (BLUE) averaged **6.67 VP** vs. AlphaBetaPlayer (RED) **8.83 VP**.
- **VP Difference**: **-2.16** (significantly below parity).
- **Key Counts**:
  - FooPlayer: 2.0 settlements, 2.03 cities, 0.23 roads, 0.03 armies, 0.07 dev cards.
  - Opponent: 2.57 settlements, 2.37 cities, 0.77 roads, 0.0 armies, 0.0 dev cards.
  - **Total Turns**: 73.33 turns per game.

---
### VERDICT
**Poor**: FooPlayer underperforms significantly, with a **VP difference of -2.16** and a **33% win rate**.

---
### LIKELY REASONS
1. **Road-Building Deficiency**:
   - FooPlayer averages **0.23 roads** compared to the opponent's **0.77 roads**, indicating a **critical lack of expansion and connectivity**.
   - **Corroborating Log**: The opponent consistently builds more roads, which is essential for Longest Road and resource access.

2. **Robber and Development Card Underutilization**:
   - FooPlayer averages **0.03 armies** and **0.07 dev cards**, while the opponent leverages these better.
   - **Corroborating Result**: The opponent's higher VP suggests better use of disruptive strategies (e.g., robber placement).

3. **2-Ply Adversarial Evaluation Issues**:
   - The 2-ply lookahead is **not robust enough** to counter adversarial strategies due to:
     - **Low reliability thresholds** (`MIN_EVAL_SUCCESS_RATE_FOR_2PLY=0.80` and `MIN_SPECTRUM_SUCCESS_RATE=0.60`).
     - **Corroborating Log**: The opponent's higher VP suggests FooPlayer's 2-ply evaluation is **not effectively simulating opponent responses**.

4. **Tie-Breaking Logic**:
   - The `decide` method (lines 500–550) uses **lexicographical tie-breaking** (`repr(action)`), which may not prioritize strategically superior actions (e.g., city upgrades over roads).

---
### STRATEGIC FLAW
FooPlayer's **2-ply adversarial evaluation lacks depth and robustness**, leading to suboptimal road-building, robber placement, and development card usage. The tie-breaking logic also fails to prioritize high-impact actions, and the **road-building strategy is critically deficient**.

---
### NEXT STEP
Send to **Strategizer** to **prioritize road-building strategies**, **refine 2-ply evaluation thresholds**, and **improve tie-breaking logic** to focus on high-impact actions.

---
Let me know if you need anything else.
END ANALYZER
================================== Ai Message ==================================

- META THOUGHTS:
  - Analyzer confirms FooPlayer regressed: road-building and disruptive play are weak and the conservative 2‑ply didn’t fix the core strategic gap. Past changes increased complexity but lowered practical expansion (roads) and VP. We must avoid more blind parameter tuning; instead add a targeted, algorithmic capability that directly measures and favors multi-turn expansion sequences (road → settlement) using the adapters, and make opponent-simulation fallbacks more reliable. This keeps us within the rule “no simple heuristics” because we’ll use explicit simulation/search to evaluate multi-step plans.
  - Keep changes small and verifiable: introduce a bounded self-only forward search (simulate only our consecutive build sequences up to depth D) to compute an expected_settlement_gain metric, incorporate it into candidate selection/tiebreaks, and make opponent modeling fallbacks more robust (deterministic fallback, probability-weighted aggregation when appropriate). Also tighten diagnostics so we can measure whether these algorithmic signals actually increase roads and VPs.

- META GOAL:
  - Produce a concise, implementable strategy plan (for CODER) that (1) increases multi-turn expansion (roads → settlements) via a bounded self-forward search and a new expected_settlement_gain metric, (2) improves opponent-simulation robustness/fallbacks, and (3) updates candidate selection/tie-breaks to use these algorithmic metrics — all while preserving determinism and adapter-only operations. Provide exact parameters, pseudocode, and diagnostics to collect.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Provide an actionable, adapter-driven plan (with ready-to-implement pseudocode and exact parameter values) that the CODER can paste into foo_player.py. Constraints: do NOT add handcrafted scoring heuristics; only use adapters (copy_game, execute_spectrum, expand_spectrum, execute_deterministic, derive_playable_actions/_derive_opponent_actions, base_fn/ safe_eval). The plan must be narrowly focused and reversible. Include diagnostics to prove the change helps (roads, VP). Deliver:

1) New parameter suggestions (exact values)
   - SELF_LOOKAHEAD_DEPTH = 3  # simulate up to 3 consecutive self actions (roads/settles/upgrades)
   - SELF_LOOKAHEAD_BUDGET = 200  # max simulation nodes used for self-only lookahead per decide
   - TOP_K_1PLY = 6  (keep)
   - OP_MAX_ACTIONS = 10  (keep)
   - MAX_SIMULATION_NODES = 4000  (keep)
   - MIN_EVAL_SUCCESS_RATE_FOR_2PLY = 0.80  (keep)
   - MIN_SPECTRUM_SUCCESS_RATE = 0.60  (keep)
   - SCORE_AMBIGUITY_THRESHOLD = 0.05  (keep)

2) New metric: expected_settlement_gain (algorithmic, adapter-driven)
   - Definition: expected number of additional settlements (or settlement opportunities) the agent can realize within SELF_LOOKAHEAD_DEPTH turns if it follows a best self-only sequence starting with candidate action a.
   - Computation method:
     - Use copy_game(game), simulate action a.
     - From each outcome branch (spectrum or deterministic) compute probability p_i and outcome_game_i.
     - For each outcome_game_i, run a bounded forward search up to depth SELF_LOOKAHEAD_DEPTH where only moves by our color are simulated (simulate our best plausible build sequence assuming opponents do nothing). For branching inside the self-only search, sample/prune playable actions deterministically with phase-aware _sample_actions but limited by SELF_LOOKAHEAD_BUDGET. Count the maximum number of settlements gained or settlement slots reached across sequences; average with probabilities p_i to compute expected_settlement_gain for a.
     - If any simulation fails, return -inf for that candidate.

   - Implementation pseudocode (ready to paste):
     ```
     def _compute_expected_settlement_gain(self, game: Game, action) -> float:
         # returns expected number of additional settlements reachable within SELF_LOOKAHEAD_DEPTH
         try:
             gc = copy_game(game)
         except Exception:
             return -float('inf')
         # get outcome branches for action a
         outcomes = _simulate_action_branches(gc, action)  # returns list of (outcome_game, prob)
         if not outcomes:
             return -float('inf')
         total_gain = 0.0
         sim_nodes_used = 0
         for og, p in outcomes:
             # run bounded self-only DFS/BFS up to depth SELF_LOOKAHEAD_DEPTH
             best_gain_for_branch = 0
             stack = [(og, 0, 0)]  # (game_state, depth, settlements_gained)
             while stack and sim_nodes_used < SELF_LOOKAHEAD_BUDGET:
                 state, depth, gained = stack.pop()
                 sim_nodes_used += 1
                 # derive playable actions for our color in state
                 plays = self._derive_opponent_actions(state, self.color) or []
                 # filter to actions that are build/road/upgrade/dev (we only simulate building)
                 candidates = [act for act in plays if self._is_build_or_upgrade(act) or self._is_road_action(act)]
                 # if no build actions, update best_gain_for_branch using base_fn difference if needed
                 for act in self._sample_actions(candidates, state):
                     # simulate deterministic application; prefer deterministic simulation for self-only lookahead
                     try:
                         det = execute_deterministic(copy_game(state), act)
                         next_state = det[0][0] if isinstance(det[0], tuple) else det[0]
                     except Exception:
                         continue
                     new_gained = gained + (1 if self._is_settlement_build(act) else 0)
                     if depth+1 < SELF_LOOKAHEAD_DEPTH:
                         stack.append((next_state, depth+1, new_gained))
                     else:
                         best_gain_for_branch = max(best_gain_for_branch, new_gained)
                 # also update best_gain_for_branch w/ current gained
                 best_gain_for_branch = max(best_gain_for_branch, gained)
             total_gain += p * best_gain_for_branch
         return float(total_gain)
     ```
     - Notes:
       - _simulate_action_branches should reuse existing code that returns spectrum/deterministic outcomes, capped/sorted by prob.
       - _is_settlement_build(action) should detect BUILD_SETTLEMENT type.
       - Keep sim_nodes_used tracked and abort early if SELF_LOOKAHEAD_BUDGET exceeded; in that case return current best estimate.

3) Integrate expected_settlement_gain into candidate selection and tie-breaks
   - Candidate pool construction:
     - Keep top_by_1ply and expansion_potential inclusion as before.
     - Additionally compute expected_settlement_gain for all remaining candidates; include top candidates by this metric (fill up to TOP_K_1PLY).
     - Guarantee at least one road candidate as before.
   - Tie-break order (exact):
     1. expected_value (2-ply expected adversarial value) — primary
     2. expected_settlement_gain — promote multi-turn expansion sequences
     3. expansion_potential (avg playable actions) — mobility
     4. robber_opponent_impact (if applicable) — defensive/disruptive value
     5. future_build_count (avg immediate build actions) — short-term buildability
     6. 1-ply vp_delta
     7. repr(action) as absolute last resort
   - Implementation: replace the candidate comparison block with a tuple compare using the above ordering (higher better). For numeric -inf handling, treat lower as strictly worse.

4) Opponent-simulation robustness improvements
   - When simulating opponent responses inside 2-ply:
     - If execute_spectrum fails for an opponent action, try execute_deterministic(next_state, opp_action). If that succeeds, use that outcome; if both fail, use safe_eval_base_fn(og, self.color) as the value for that branch (this approximates opponent effect rather than skipping it).
     - Aggregation rule: where previously you used min_b(score_after_b) (fully adversarial), consider using a conservative hybrid:
       - If opponent simulations succeeded for at least one action in the sampled set, use min_b(score_after_b).
       - If none succeeded, fallback to safe_eval_base_fn(og, self.color) rather than skipping or using +inf/0.
     - This reduces candidate elimination due to simulation brittleness.

5) Adaptive 2-ply activation refinement
   - Keep existing reliability thresholds but also allow 2-ply when expected_settlement_gain or expansion_potential shows strong disparity between top candidates (i.e., when multi-turn expansion matters).
   - Use SCORE_AMBIGUITY_THRESHOLD as before but also:
     - If max(expected_settlement_gain) among candidates >= 0.5, set allow_2ply = True (we expect at least a half settlement expected).
     - This focuses deeper search on expansion-critical decisions.

6) Diagnostics to collect (must be printed when debug=True)
   - Per decide:
     - expected_settlement_gain for each candidate (print top 6).
     - Whether 2-ply ran, sim_count used for 2-ply, and sim nodes used for SELF_LOOKAHEAD_TOTAL across candidates.
     - Number of road candidates included and whether guaranteed road was inserted.
     - Count of opponent-sim failures and whether deterministic fallback used.
   - Per game aggregate (after batch):
     - avg expected_settlement_gain for chosen actions.
     - avg roads built by FooPlayer.
     - times 2-ply ran vs skipped and reasons, avg sim_count per 2-ply invocation.
     - proportion of opponent-sim branches that used deterministic fallback vs spectrum.

7) Safety & resource constraints
   - Enforce SELF_LOOKAHEAD_BUDGET and MAX_SIMULATION_NODES hard caps. If budget exceeded during computing expected_settlement_gain for too many candidates, prioritize computing it only for candidates that are near the top_by_1ply or for road/robber candidates (deterministic order) to remain within budget.
   - If more than 50% of candidate simulations fail or budget hits hard cap before finishing, skip 2-ply for this decide and fall back to best 1-ply.

8) Success criteria (concrete)
   - After running 30-game batch:
     - avg roads for FooPlayer increases to >= 0.5
     - win rate improves toward >= 50% (or VP gap within 0.5)
     - diagnostics show expected_settlement_gain > 0 for majority of chosen actions when they are expansion choices
     - 2-ply executed more often on ambiguous/expansion-critical decisions and not executed when unreliable

9) Deliverable for CODER (exact)
   - Implement _compute_expected_settlement_gain and integrate it into candidate pool and tie-break code exactly as described.
   - Add the opponent-simulation fallback and hybrid aggregation rule.
   - Add the diagnostics prints and budget enforcement described above.
   - Run 3 debug games (player.debug = True) and return:
     - Per-decision logs showing expected_settlement_gain, road guarantees, and whether 2-ply ran.
     - Up to 5 stack traces if any adapter exceptions occur.
   - If diagnostics look healthy (expected_settlement_gain computed regularly for top candidates and sim budget not exceeded), run 30-game batch and return metrics and aggregate diagnostics.

Would you like me to produce exact ready-to-paste code snippets for:
- _compute_expected_settlement_gain (including helper _simulate_action_branches),
- integration points in decide() (candidate pool and tie-break tuple),
- opponent-simulation fallback code,
- and the diagnostic printouts?

If yes, I will produce the snippets next.
================================ Human Message =================================

STRATEGIZER
**Ready-to-Paste Code Snippets for Multi-Turn Expansion Strategy**

---
### **1. Add New Parameters**
Add the following parameters to the `FooPlayer` class:
```python
# Multi-turn expansion planning
SELF_LOOKAHEAD_DEPTH: int = 3      # Simulate up to 3 consecutive self actions
SELF_LOOKAHEAD_BUDGET: int = 200   # Max simulation nodes for self-only lookahead
```

---
### **2. Add `_simulate_action_branches` Helper**
```python
def _simulate_action_branches(self, game: Game, action) -> List[Tuple[Game, float]]:
    """Simulate action and return outcome branches (spectrum or deterministic)."""
    try:
        game_copy = copy_game(game)
    except Exception:
        if self.debug:
            print("_simulate_action_branches: copy_game failed")
            traceback.print_exc()
        return []

    outcomes = []
    try:
        if self._is_robber_or_chance(action):
            spec = None
            try:
                spec = execute_spectrum(game_copy, action)
            except Exception:
                try:
                    spec_map = expand_spectrum(game_copy, [action])
                    if isinstance(spec_map, dict):
                        spec = spec_map.get(action, None)
                except Exception:
                    spec = None
            if spec:
                outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
        else:
            det_res = execute_deterministic(game_copy, action)
            if det_res:
                outcomes = [(det_res[0][0], 1.0)] if isinstance(det_res[0], tuple) else [(det_res[0], 1.0)]
    except Exception:
        if self.debug:
            print("_simulate_action_branches: failed to simulate action")
            traceback.print_exc()
        return []

    return outcomes
```

---
### **3. Add `_is_settlement_build` Helper**
```python
def _is_settlement_build(self, action) -> bool:
    """Detect settlement-building actions."""
    at = getattr(action, "action_type", None)
    try:
        return at == ActionType.BUILD_SETTLEMENT
    except Exception:
        name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
        return "settle" in str(name).lower()
```

---
### **4. Add `_compute_expected_settlement_gain` Method**
```python
def _compute_expected_settlement_gain(self, game: Game, action) -> float:
    """Compute expected number of additional settlements reachable within SELF_LOOKAHEAD_DEPTH turns."""
    try:
        game_copy = copy_game(game)
    except Exception:
        if self.debug:
            print("_compute_expected_settlement_gain: copy_game failed")
            traceback.print_exc()
        return -float("inf")

    # Simulate action to get outcome branches
    outcomes = self._simulate_action_branches(game_copy, action)
    if not outcomes:
        return -float("inf")

    total_gain = 0.0
    sim_nodes_used = 0

    for outcome_game, prob in outcomes:
        if sim_nodes_used >= self.SELF_LOOKAHEAD_BUDGET:
            break

        # Run bounded self-only DFS to estimate settlement gain
        stack = [(outcome_game, 0, 0)]  # (game_state, depth, settlements_gained)
        best_gain_for_branch = 0

        while stack and sim_nodes_used < self.SELF_LOOKAHEAD_BUDGET:
            state, depth, gained = stack.pop()
            sim_nodes_used += 1

            # Derive playable actions for our color
            try:
                playable = self._derive_opponent_actions(state, self.color) or []
            except Exception:
                if self.debug:
                    print("_compute_expected_settlement_gain: failed to derive playable actions")
                    traceback.print_exc()
                continue

            # Filter to build/road/upgrade actions
            build_candidates = [
                act for act in playable
                if self._is_build_or_upgrade(act) or self._is_road_action(act)
            ]

            # Simulate each build candidate deterministically
            for act in self._sample_actions(build_candidates, state)[:5]:  # Limit to top 5 candidates
                try:
                    det = execute_deterministic(copy_game(state), act)
                    if not det:
                        continue
                    next_state = det[0][0] if isinstance(det[0], tuple) else det[0]
                except Exception:
                    continue

                new_gained = gained + (1 if self._is_settlement_build(act) else 0)
                if depth + 1 < self.SELF_LOOKAHEAD_DEPTH:
                    stack.append((next_state, depth + 1, new_gained))
                else:
                    best_gain_for_branch = max(best_gain_for_branch, new_gained)

        total_gain += prob * best_gain_for_branch

    return total_gain
```

---
### **5. Update Candidate Pool Construction in `decide`**
Replace the **candidate pool construction** section in `decide` with the following:
```python
# Stage 3: Build candidate pool with expansion potential, settlement gain, and road guarantee
one_ply_results.sort(key=lambda t: (t[1], t[2]), reverse=True)
top_by_1ply = [t[0] for t in one_ply_results[:3]]  # Always include top 3 by 1-ply score
remaining_candidates = [t[0] for t in one_ply_results[3:]]

# Compute expansion potential and settlement gain for remaining candidates
expansion_scores = {}
settlement_gain_scores = {}
for a in remaining_candidates:
    exp_potential = self._compute_expansion_potential(game, a)
    if exp_potential != -float("inf"):
        expansion_scores[a] = exp_potential

    settlement_gain = self._compute_expected_settlement_gain(game, a)
    if settlement_gain != -float("inf"):
        settlement_gain_scores[a] = settlement_gain

# Sort remaining candidates by settlement gain, then expansion potential
sorted_remaining = sorted(
    settlement_gain_scores.items(),
    key=lambda x: (x[1], expansion_scores.get(x[0], -float("inf"))),
    reverse=True
)
additional_candidates = [a for a, _ in sorted_remaining[: max(0, self.TOP_K_1PLY - len(top_by_1ply))]]
candidate_pool = top_by_1ply + additional_candidates

# Guarantee inclusion of at least one high-potential road candidate
road_candidates = [a for a in remaining_candidates if self._is_road_action(a)]
road_scores = {a: self._compute_expected_settlement_gain(game, a) for a in road_candidates}
best_road = None
if road_scores:
    best_road = max(road_scores.items(), key=lambda x: x[1])[0]
    if best_road not in candidate_pool:
        candidate_pool.append(best_road)
        self._diag["n_road_candidates_included"] = self._diag.get("n_road_candidates_included", 0) + 1
        if self.debug:
            print(f"decide: added guaranteed road candidate {repr(best_road)} with settlement_gain={road_scores[best_road]}")

if self.debug:
    print("Candidate pool:")
    for a in candidate_pool:
        exp_potential = expansion_scores.get(a, "N/A")
        settlement_gain = settlement_gain_scores.get(a, "N/A")
        is_road = self._is_road_action(a)
        is_robber = self._is_robber_or_chance(a)
        print(
            f"  {repr(a)} (is_road={is_road}, is_robber={is_robber}, "
            f"expansion_potential={exp_potential}, settlement_gain={settlement_gain})"
        )
```

---
### **6. Update Opponent Simulation Fallbacks**
Replace the **opponent simulation loop** in `decide` with the following:
```python
# Adversarial opponent: they choose the action minimizing our final score
min_score_after_opp = float("inf")
opp_successes = 0
for b in opp_sampled:
    if sim_count >= SIMULATION_HARD_LIMIT:
        break

    # Try spectrum first for opponent chance actions
    if self._is_robber_or_chance(b):
        try:
            opp_spec = execute_spectrum(og, b)
            if opp_spec:
                opp_outcomes = self._normalize_and_cap_spectrum(opp_spec, self.SPECTRUM_MAX_OUTCOMES)
                opp_scores = []
                for opp_og, opp_p in opp_outcomes:
                    opp_sc = self._safe_eval_base_fn(opp_og, self.color)
                    if opp_sc is not None:
                        opp_scores.append(opp_p * opp_sc)
                if opp_scores:
                    opp_val = sum(opp_scores)
                    min_score_after_opp = min(min_score_after_opp, opp_val)
                    opp_successes += 1
                    continue
        except Exception:
            pass

    # Fallback to deterministic simulation
    try:
        opp_det = execute_deterministic(og, b)
        if opp_det:
            opp_det_og = opp_det[0][0] if isinstance(opp_det[0], tuple) else opp_det[0]
            opp_sc = self._safe_eval_base_fn(opp_det_og, self.color)
            if opp_sc is not None:
                min_score_after_opp = min(min_score_after_opp, opp_sc)
                opp_successes += 1
                continue
    except Exception:
        pass

    # Last resort: evaluate og directly (no opponent action effect)
    tmp = self._simulate_and_evaluate(og, None, self.color)
    if tmp is not None:
        min_score_after_opp = min(min_score_after_opp, tmp)
        opp_successes += 1

# If no opponent simulations succeeded, evaluate the post-my-action state
if opp_successes == 0:
    min_score_after_opp = self._simulate_and_evaluate(og, None, self.color) or 0.0
```

---
### **7. Update Tie-Break Logic**
Replace the **candidate comparison block** in `decide` with the following:
```python
# Compare candidate using updated tie-breaks
if self.debug:
    print(
        f"Candidate {repr(a)}: expected_value={expected_value_a}, "
        f"settlement_gain={settlement_gain_scores.get(a, 'N/A')}, "
        f"expansion_potential={expansion_potential_a}, "
        f"robber_impact={robber_impact_a}, "
        f"future_build_count={future_build_count_a}, "
        f"1-ply vp_delta={one_ply_vp_delta}"
    )

is_better = False
if best_action is None:
    is_better = True
elif expected_value_a > best_value:
    is_better = True
elif expected_value_a == best_value:
    # Secondary: expected_settlement_gain
    gain_a = settlement_gain_scores.get(a, -float("inf"))
    best_gain = settlement_gain_scores.get(best_action, -float("inf"))
    if gain_a > best_gain:
        is_better = True
    elif gain_a == best_gain:
        # Tertiary: expansion_potential
        if expansion_potential_a > best_expansion:
            is_better = True
        elif expansion_potential_a == best_expansion:
            # Quaternary: robber_impact (if applicable)
            if robber_impact_a > best_robber_impact:
                is_better = True
            elif robber_impact_a == best_robber_impact:
                # Quinary: future_build_count
                if future_build_count_a > best_future_build:
                    is_better = True
                elif future_build_count_a == best_future_build:
                    # Senary: 1-ply vp_delta
                    if one_ply_vp_delta > best_vp_delta:
                        is_better = True
                    elif one_ply_vp_delta == best_vp_delta:
                        # Septenary: repr tie-break
                        if best_repr is None or repr(a) < best_repr:
                            is_better = True

if is_better:
    best_action = a
    best_value = expected_value_a
    best_expansion = expansion_potential_a
    best_robber_impact = robber_impact_a
    best_future_build = future_build_count_a
    best_vp_delta = one_ply_vp_delta
    best_repr = repr(a)
```

---
### **8. Add Debug Diagnostics**
Add the following to the `decide` method after the 2-ply evaluation loop:
```python
if self.debug:
    print("2-ply diagnostics:")
    print(f"  n_2ply_runs: {self._diag.get('n_2ply_runs', 0)}")
    print(f"  n_2ply_skipped: {self._diag.get('n_2ply_skipped', 0)}")
    print(f"  sim_count: {sim_count} / {SIMULATION_HARD_LIMIT}")
    print(f"  best_action: {repr(best_action)}")
    print(f"  best_value: {best_value}")
    print(f"  best_settlement_gain: {settlement_gain_scores.get(best_action, 'N/A')}")
    print(f"  best_expansion: {best_expansion}")
    print(f"  best_robber_impact: {best_robber_impact}")
    print(f"  best_future_build: {best_future_build}")
    print(f"  n_road_candidates_included: {self._diag.get('n_road_candidates_included', 0)}")
```

---
### **9. Deliverable for CODER**
1. **Implement the provided snippets** in `foo_player.py`.
2. **Run 3 games with `debug=True`** and share logs.
3. **Run the 30-game batch** and report metrics.

---
### **Expected Outcome**
- **Avg Roads**: ≥0.5.
- **Win Rate**: ≥50%.
- **Avg VP**: Within 0.5 of the opponent.
- **Diagnostics**: Show `expected_settlement_gain` > 0 for expansion choices and 2-ply running on ambiguous/expansion-critical decisions.

END STRATEGIZER

--- Output from META at 20250925_035402 ---
================================== Ai Message ==================================

- META THOUGHTS:
  - The Analyzer shows FooPlayer regressed: roads and expansion remain the key weaknesses and the last 2-ply attempts increased complexity without improving expansion. We must now implement a narrowly scoped, algorithmic remedy — a short self-only forward search that measures expected_settlement_gain (roads→settlements potential) and integrate that metric into candidate selection and tie-breaking. This uses only adapters and search (not handcrafted heuristics) and should be deterministic and budgeted.
  - Keep changes minimal, defensive, and instrumented so we can measure whether the metric increases roads and VP. Run a short debug experiment (3 games) to verify diagnostics before a 30-game batch.

- META GOAL:
  - Implement the STRATEGIZER's multi-turn expansion snippets exactly (simulate_action_branches, is_settlement_build, compute_expected_settlement_gain, candidate pool integration, opponent simulation fallbacks, tie-break changes, diagnostics and budgets). Run 3 debug games with debug=True, return raw logs and up to 5 stack traces if failures. If diagnostics meet criteria, run 30-game batch and return JSON metrics plus aggregate diagnostics.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the STRATEGIZER-ready snippets and run the prescribed experiments. Follow these exact steps and constraints.

1) Code to implement (apply exactly as provided by STRATEGIZER):
   - Add parameters:
     - SELF_LOOKAHEAD_DEPTH = 3
     - SELF_LOOKAHEAD_BUDGET = 200
     - Keep existing parameters (TOP_K_1PLY, OP_MAX_ACTIONS, MAX_SIMULATION_NODES, MIN_EVAL_SUCCESS_RATE_FOR_2PLY, MIN_SPECTRUM_SUCCESS_RATE, SCORE_AMBIGUITY_THRESHOLD, etc.)
   - Add helpers:
     - _simulate_action_branches(game, action) — use copy_game, execute_spectrum/expand_spectrum, execute_deterministic; return list of (Game, prob).
     - _is_settlement_build(action) — detect BUILD_SETTLEMENT.
   - Add:
     - _compute_expected_settlement_gain(game, action) exactly as provided: bounded self-only DFS using execute_deterministic for self actions, using _sample_actions to prune branches, respect SELF_LOOKAHEAD_BUDGET, return -float("inf") on failures.
   - Integrate into decide():
     - Replace candidate pool construction with the provided Stage 3 snippet: compute expansion_scores and settlement_gain_scores; sort remaining candidates by settlement_gain then expansion; choose additional_candidates up to TOP_K_1PLY; guarantee inclusion of best_road by settlement_gain; update self._diag["n_road_candidates_included"] when guaranteed road added; print candidate pool diagnostics when debug True.
     - Replace opponent simulation loop in 2-ply with provided fallbacks (try spectrum → deterministic → evaluate og directly), track opp_successes, and if none succeeded use simulate_and_evaluate(og, None) or 0.0.
     - Replace the candidate comparison block with the new tie-break hierarchy (expected_value, expected_settlement_gain, expansion_potential, robber_impact, future_build_count, 1-ply vp_delta, repr) exactly as given.
     - Add the debug diagnostics block after 2-ply loop (print sim_count, best_settlement_gain, n_road_candidates_included, etc.)
   - Ensure helper names are referenced consistently:
     - _simulate_action_branches, _compute_expected_settlement_gain, _is_settlement_build, settlement_gain_scores local dict used in comparison.
   - Defensive coding:
     - Wrap every adapter call (copy_game, execute_spectrum, expand_spectrum, execute_deterministic, base_fn/safe_eval, derive_playable_actions/_derive_opponent_actions) with try/except; print traceback when self.debug True.
     - If more than 50% of candidate sims fail or SIMULATION_HARD_LIMIT / SELF_LOOKAHEAD_BUDGET hit before finishing, skip 2-ply and fallback to best 1-ply deterministically.
     - Never let decide() raise; always return a playable action (best 1-ply or actions[0]).

2) Diagnostics & counters:
   - Maintain/update self._diag keys (add if needed): n_road_candidates_included, n_2ply_runs, n_2ply_skipped, simulated_nodes_total, self_lookahead_nodes_total, opponent_sim_fallbacks_count.
   - Print per-decision:
     - settlement_gain for top candidates (up to TOP_K_1PLY)
     - whether guaranteed road was added and its settlement_gain
     - whether 2-ply ran, sim_count used, and whether sim caps were hit
     - opponent-sim fallback counts and whether deterministic fallback was used
   - Aggregate these counters across the run for reporting after the batch.

3) Determinism:
   - Preserve deterministic RNG seeding where used (random.Random(self.RNG_SEED + stable_color_hash)).
   - No use of non-deterministic randomness in 2-ply or lookahead choices.

4) Execution plan — run now:
   A) Debug smoke test:
     - Set player.debug = True.
     - Run 3 games vs AlphaBetaPlayer.
     - Collect and return:
       - Full raw debug logs (stdout) including per-decision prints, candidate pool prints, settlement_gain, expansion_potential, robber_impact, sim_count and reasons 2-ply ran/skipped.
       - Up to the first 5 full stack traces if any unhandled adapter exceptions occur.
   B) Diagnostics gate:
     - Criteria to proceed to 30-game batch:
       - expected_settlement_gain computed for top candidates in most decisions (i.e., not consistently -inf).
       - avg self_lookahead_nodes_total per decide < SELF_LOOKAHEAD_BUDGET (or at least not exhausting budget each time).
       - eval_success_rate >= MIN_EVAL_SUCCESS_RATE_FOR_2PLY (0.80) OR the number of decisions where allowed_by_ambiguity/road/robber is >0 (i.e., 2-ply useful).
       - No persistent adapter signature errors.
     - If diagnostics fail (many -inf, adapter failures, sim budget exhausted early), stop and return logs and up to 5 traces so we can call RESEARCHER.
   C) Full experiment (if gate passes):
     - Set player.debug = False.
     - Run 30-game batch.
     - Return:
       - The JSON metrics (Player Summary & Game Summary) as before.
       - Aggregate diagnostics: times_2ply_run, times_2ply_skipped (with reasons), avg_simulated_nodes_per_2ply, avg_self_lookahead_nodes, avg expected_settlement_gain for chosen actions, avg roads for FooPlayer, opponent-sim fallback counts.

5) Reporting:
   - For the 3-game debug run return raw logs and up to 5 stack traces.
   - For the 30-game batch return JSON metrics and a short bullet summary with the requested aggregated diagnostics.

6) If any unhandled exceptions occur while running the debug tests:
   - Stop immediately and return the full stack traces (up to 5).
   - Don’t proceed to the batch. We will call RESEARCHER if adapter signature mismatches or missing functions appear.

Please proceed to:
- Implement the STRATEGIZER snippets exactly as specified (ensure names match), compile/run the code environment, and run the 3 debug games (player.debug=True). Return the full raw logs and up to 5 stack traces (if any). After we inspect logs, we will instruct whether to run the 30-game batch.

