--- Input for STRATEGIZER at 20250925_032045 ---
================================ Human Message =================================

- META THOUGHTS:
  - Analyzer confirmed the root cause: foo_player.py always returns playable_actions[0], i.e., no decision logic or lookahead. This explains 0 wins and 2 VP average.
  - A principled next step is a 1-ply deterministic lookahead: for each candidate action, copy the game, apply the action deterministically, then evaluate the resulting state with adapters.base_fn for our color and pick the action with highest value. This uses adapters.py functions (copy_game, execute_deterministic, base_fn) rather than ad-hoc heuristics.
  - Risks/gaps: exact adapters API signatures are not shown; assume names above. If the action set is large, evaluate a capped subset or representative action per action-type to keep runtime reasonable. If no improvement after several evolutions, escalate to deeper search (2-ply) or opponent-modeling/expectimax.
  - Plan: ask STRATEGIZER to produce a concrete, implementable algorithm (pseudocode), with pruning/sampling rules, tie-breaking, fallbacks and expected performance metrics so CODER can implement it.

- META GOAL:
  - Obtain a concrete, adapter-based strategy (1-ply deterministic lookahead) that replaces the current "choose-first" policy. The strategy must be algorithmic (search/evaluation), not simple weight heuristics, and produce robust code instructions for CODER to implement and test.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
  Produce a focused, actionable strategy to fix the root flaw (always choosing the first action). Requirements and constraints:
  1. Core idea: implement a 1-ply deterministic lookahead that uses only functions from adapters.py to evaluate candidate actions. Do NOT propose simple static heuristics.
  2. Adapters to use (assume these exist and specify how to call them):
     - copy_game(game): returns an independent deep copy of the current game state.
     - execute_deterministic(game_copy, action): applies the action to game_copy deterministically and returns the modified game_copy (or modifies in-place). If the action is non-deterministic in the real engine, this helper should apply a deterministic resolution.
     - base_fn(game_copy, color): returns a numeric evaluation (higher is better) for the player color in the given game state.
     - If you think additional adapters are needed (e.g., legal_actions(game)), specify them but prefer the three above.
  3. Algorithm specification: provide clear pseudocode that:
     - Accepts (game, playable_actions, my_color)
     - Prunes or samples playable_actions if their count > MAX_ACTIONS_TO_EVAL (recommendation: default MAX=30). Propose a principled pruning/sampling approach (e.g., group by action type and sample top-K from each group, or randomly sample with seeding for reproducibility). Explain why you chose this approach.
     - For each candidate action: copy game, execute_deterministic, evaluate with base_fn for my_color.
     - Select the action with highest evaluation. Provide tie-breaker rules (e.g., prefer action that increases visible VP, else prefer action with higher immediate resource delta—only if needed as tie-breaker; keep tie-break logic simple and algorithmic).
     - Include safe fallbacks: if evaluation fails or returns None/exception, fall back to selecting a random legal action (but still deterministic if possible) or the first playable action.
  4. Performance safeguards:
     - Provide complexity estimate (approx time per action eval) and recommendations for MAX_ACTIONS_TO_EVAL to keep run-time similar to current tests.
     - Suggest logging hooks to detect if most chosen actions are still the default (for debugging).
  5. Edge cases:
     - If playable_actions contains only passive actions (e.g., "end_turn"), then return that as usual.
     - If adapters.execute_deterministic is not available or fails for a particular action type, skip that action (with logging).
  6. Testing and success criteria:
     - Recommend running N=30 games vs the same AlphaBeta opponent and compare:
       - Wins (should increase from 0),
       - AVG VP (target > 3–4 initially),
       - AVG cities/roads should be > 0.
     - If no measurable improvement after this change, propose next steps (2-ply with limited opponent model or Monte Carlo rollouts).
  7. Deliverables for CODER:
     - Exact pseudocode for the decide(game, playable_actions) function to drop into foo_player.py.
     - Concrete parameter defaults (MAX_ACTIONS_TO_EVAL, sampling strategy, tie-break rules).
     - Any recommended helper function names and signatures to add inside foo_player.py.

Be concise and precise in the plan—CODER must be able to implement this directly against adapters.py. Avoid recommending handcrafted score weights; rely on base_fn for evaluation.
================================== Ai Message ==================================

STRATEGIZER
**Proposed Strategy: 1-Ply Deterministic Lookahead with Adapter-Based Evaluation**

### **Core Idea**
Replace the current "choose-first" policy with a **1-ply deterministic lookahead** that:
1. Copies the game state for each candidate action.
2. Executes the action deterministically.
3. Evaluates the resulting state using `adapters.base_fn`.
4. Selects the action with the highest evaluation score.

---

### **Algorithm Specification**
#### **Pseudocode for `decide(game, playable_actions)`**
```python
import random
from .adapters import copy_game, execute_deterministic, base_fn

def decide(self, game, playable_actions):
    my_color = self.color
    MAX_ACTIONS_TO_EVAL = 30  # Safeguard for runtime

    # Prune or sample actions if too many
    if len(playable_actions) > MAX_ACTIONS_TO_EVAL:
        # Group by action type and sample top-2 from each group
        action_types = {}
        for action in playable_actions:
            action_types.setdefault(action.action_type, []).append(action)
        sampled_actions = []
        for action_type, actions in action_types.items():
            sampled_actions.extend(random.sample(actions, min(2, len(actions))))
    else:
        sampled_actions = playable_actions

    # Fallback for empty sampled_actions (edge case)
    if not sampled_actions:
        return playable_actions[0]

    # Evaluate each action
    best_action = None
    best_score = -float('inf')
    for action in sampled_actions:
        try:
            game_copy = copy_game(game)
            executed_games = execute_deterministic(game_copy, action)
            if not executed_games:
                continue  # Skip if execution fails
            new_game, _ = executed_games[0]  # Take the deterministic outcome
            score = base_fn()(new_game, my_color)
            if score > best_score:
                best_score = score
                best_action = action
        except Exception as e:
            print(f"Evaluation failed for action {action}: {e}")
            continue

    # Tie-breaker: prefer actions that increase visible VP or resources
    if best_action is None:
        best_action = random.choice(sampled_actions)

    return best_action
```

---

### **Key Design Choices**
1. **Pruning/Sampling**:
   - Group actions by `action_type` and sample **top-2 from each group** to ensure diversity while limiting runtime.
   - If no improvement is seen, increase `MAX_ACTIONS_TO_EVAL` or switch to random sampling.

2. **Tie-Breaking**:
   - If scores are tied, randomly select among top-scoring actions (deterministic if seeded).

3. **Fallbacks**:
   - If evaluation fails for all actions, return a random legal action.
   - If `playable_actions` is empty or contains only passive actions (e.g., "end_turn"), return the first action.

4. **Performance Safeguards**:
   - **Complexity**: ~O(N) where N = min(MAX_ACTIONS_TO_EVAL, len(playable_actions)).
   - **Runtime**: Target <1s per decision (adjust `MAX_ACTIONS_TO_EVAL` if needed).
   - **Logging**: Add debug logs to track chosen actions and scores.

---

### **Expected Improvements**
- **Wins**: Increase from 0 to >5% in 30 games.
- **Avg VP**: Rise from 2.0 to 3–4.
- **Cities/Roads**: Non-zero values in game summaries.

---

### **Next Steps if No Improvement**
1. **2-Ply Lookahead**: Evaluate opponent responses using `expand_spectrum`.
2. **Monte Carlo Rollouts**: Use `execute_spectrum` for probabilistic outcomes.
3. **Opponent Modeling**: Incorporate `AlphaBetaPlayer` for deeper search.

---
### **Deliverables for CODER**
1. Replace `decide` in `foo_player.py` with the pseudocode above.
2. Add `MAX_ACTIONS_TO_EVAL = 30` as a class variable.
3. Test with 30 games and report metrics.

END STRATEGIZER
================================ Human Message =================================

- META THOUGHTS:
  - Analyzer shows clear progress: the 1-ply lookahead made FooPlayer competitive (14/30 wins, avg VP 7.83 vs 8.0). Remaining gap is small but systematic. Primary weaknesses: aggressive pruning (may cut critical actions), poor handling of robber/chance, and naive tie-breaks/lack of phase-awareness.
  - Next evolution should keep the 1-ply core but refine sampling, add small expectimax for robber/chance actions, improve deterministic tie-breaking using actual VP deltas, and make sampling phase-aware (early expansion vs. late VP-grab).
  - Plan: produce a focused, implementable strategy for CODER that (1) increases and adapts sampling budget, (2) prioritizes evaluating actions that change VP or are build/upgrade in late game, (3) for robber/chance actions compute an expected value via execute_spectrum/expand_spectrum when available, (4) use VP delta + base_fn as deterministic tie-breaker. If signatures for execute_spectrum/expand_spectrum differ, CODER should gracefully fall back to current 1-ply.

- META GOAL:
  - Provide CODER with a precise, adapter-driven strategy to (a) reduce pruning mistakes, (b) handle robber/chance via expected-value evaluation, and (c) adopt phase-aware sampling and robust deterministic tie-breaks — all without introducing handcrafted weight heuristics.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Produce a precise, implementable strategy (pseudocode + parameter defaults) to refine the existing 1-ply lookahead. The implementation must rely on adapters functions (only) and avoid ad-hoc score heuristics. Deliver the following items so CODER can implement them directly:

1) Adapter functions to use (specify usage patterns)
   - copy_game(game): deep-copy current game state.
   - execute_deterministic(game_copy, action): apply action deterministically; normalize return as a Game object (or list whose first entry contains the game).
   - base_fn(game, color) OR base_fn()(game, color): value function returning numeric evaluation for color.
   - execute_spectrum(game_copy, action) OR expand_spectrum(game_copy, action): (optional) returns a list of (game_outcome, probability) to compute expected value for chance-like actions (robber, dev-card draws). If unavailable, fall back to execute_deterministic.

2) High-level algorithm summary
   - Stage A: Candidate generation (sample/prune) with phase-awareness.
   - Stage B: Fast 1-ply deterministic evaluation for all candidates using copy_game + execute_deterministic + base_fn to get score and VP delta.
   - Stage C: For candidate actions that are chance/robber-like, compute expected value using execute_spectrum/expand_spectrum (small sample) and use that expected score in place of deterministic score.
   - Stage D: Select best action by comparing (score, vp_delta, deterministic tie-break repr) with deterministic tie-breaking.

3) Pseudocode (concise, exact; CODER should drop into foo_player.py)

- New parameters (defaults)
  - MAX_ACTIONS_TO_EVAL = 60
  - SAMPLE_PER_ACTION_TYPE = 3
  - TOP_K_DEEP = 6  # After 1-ply, do deeper expectimax/opp-model for top K only
  - EARLY_TURN_THRESHOLD = 30  # consider this "early game"
  - RNG_SEED = 0
  - SPECTRUM_MAX_OUTCOMES = 8  # cap for execute_spectrum sampling

- Helper predicates
  - is_build_or_upgrade(action): detect build_settlement, build_city, build_road, upgrade actions via action_type or class name.
  - is_robber_or_chance(action): detect robber placement, play_dev_card, draw_dev_card, etc.

- sample_actions(playable_actions, game)
  1. If len(playable_actions) <= MAX_ACTIONS_TO_EVAL: return all.
  2. Group by _action_type_key(action) as before.
  3. Determine phase:
     - current_turn = game.current_turn or use game.tick
     - early_game = (current_turn <= EARLY_TURN_THRESHOLD)
  4. Sampling policy per group:
     - If early_game: bias sample_count = min(SAMPLE_PER_ACTION_TYPE+1, len(group)) for groups where is_build_or_upgrade(group actions).
     - If late_game (not early): bias sample_count = min(SAMPLE_PER_ACTION_TYPE+1, len(group)) for groups where action increases visible VP (e.g., build_city/build_settlement/collect_vp actions).
     - Use deterministic RNG = random.Random(RNG_SEED + hash(self.color)) to shuffle group and pick sample_count.
  5. If after group sampling total < MAX_ACTIONS_TO_EVAL, fill deterministically by iterating remaining actions.

- evaluate_action(game, action)
  1. Try copy_game(game) -> game_copy.
  2. If is_robber_or_chance(action) AND adapters.execute_spectrum/expand_spectrum exists:
     - Use spectrum = expand_spectrum(game_copy, action) OR execute_spectrum(game_copy, action)
     - For each (outcome_game, prob) in spectrum (limit to SPECTRUM_MAX_OUTCOMES):
         - score_i = base_fn(outcome_game, my_color)
         - accumulate weighted_score += prob * score_i
     - expected_score = weighted_score
     - vp_delta = average visible VP gain across outcomes (or compute from original game)
     - Return (expected_score, vp_delta)
  3. Else (deterministic):
     - res = execute_deterministic(game_copy, action)
     - Normalize to new_game (take first outcome or fallback to mutated game_copy)
     - score = base_fn(new_game, my_color)
     - vp_delta = visible_VP(new_game, my_color) - visible_VP(original_game, my_color)
     - Return (score, vp_delta)
  4. On any exception, return None to signal failure for this action.

- choose_best_action(candidates_with_scores)
  - For each candidate entry: (action, score, vp_delta, repr_key)
  - Compare primarily by score (higher better), then by vp_delta (higher better), then by repr_key (lexicographically smaller = deterministic tiebreak).
  - Return action with the maximum tuple (score, vp_delta, -repr_key) equivalent.

- Optional Top-K deep opponent-aware step (if small CPU budget and expected to help)
  1. After 1-ply evaluation, take top TOP_K_DEEP actions (by the compare tuple).
  2. For each top candidate:
     - simulate: copy_game -> execute_deterministic -> get new_game
     - simulate opponent move responses using expand_spectrum/execute_spectrum or a single best opponent action set (if AlphaBeta available, simulate one opponent move by calling opponent.decide on a copied game). Compute expected value after opponent response (approx. 1.5-ply).
     - Replace initial score by expected value after opponent response.
  3. Re-select best action by new scores.

4) Tie-break & determinism
   - Use tuple (score, vp_delta, -lexicographic_repr(action)) for comparison.
   - Keep deterministic RNG seed per player (RNG_SEED + hash(color)).

5) Complexity & safeguards
   - Worst-case evaluations = MAX_ACTIONS_TO_EVAL deterministic sims; each deterministic sim = copy + execute + base_fn.
   - Spectrum-based expected-value only used for robber/chance actions or for TOP_K_DEEP extra sims to moderate runtime.
   - If any adapter isn't available or raises TypeError, fall back to deterministic path (execute_deterministic) and log exception when debug=True.
   - If all candidates fail evaluations, return first playable_action (current safe fallback).

6) Logging
   - When debug=True: log number of candidates, phase (early/late), for each evaluated action print (repr, score, vp_delta, method=deterministic/spectrum), and final chosen action.
   - For spectrum outcomes, log number of outcomes used and the weighted expected score.

7) Success criteria & experiment plan
   - Run 30 games vs the same AlphaBeta opponent.
   - Target: Win rate > 50% and average VP >= opponent (or VP difference >= 0).
   - If still marginal (< 52% win rate) after this change, proceed to:
     - Increase MAX_ACTIONS_TO_EVAL incrementally (80->120) or
     - Implement full 2-ply expectimax for top-N actions (TOP_K_DEEP up to 12),
     - Or implement a lightweight Monte Carlo rollout using execute_spectrum for random playouts (but still rely on base_fn for evaluation).

8) Deliverables for CODER (concrete)
   - Implement the new sampling function with phase-aware sampling.
   - Implement evaluate_action that uses execute_spectrum/expand_spectrum for chance-like actions (with SPECTRUM_MAX_OUTCOMES cap).
   - Implement optional TOP_K_DEEP opponent-aware refinement (make it configurable; default OFF).
   - Update constants to provided defaults.
   - Add debug logs as specified.

9) Edge cases & fallbacks (explicit)
   - If expand_spectrum/execute_spectrum is not present or returns unexpected shapes, fall back to deterministic single-outcome evaluation (as in current code) and log stack trace when debug=True.
   - The code must never crash; any exceptions in adapters should result in skipping that candidate.
   - Determinism must be preserved by using deterministic RNG and deterministic tie-breaks.

Implement only one major change this iteration: the refined sampling + robber/chance expected-value handling and tie-break improvement. Do NOT add handcrafted scoring heuristics. Keep changes minimal so we can attribute performance differences to these algorithmic improvements. If CODER reports adapter signature errors, include exact exception text and call RESEARCHER for precise adapter usage.

End objective: provide code-ready pseudocode and parameters so CODER can implement quickly.
================================== Ai Message ==================================

STRATEGIZER
**Refined 1-Ply Lookahead Strategy with Phase-Aware Sampling and Expected Value for Chance Actions**

---

### **Core Improvements**
1. **Phase-Aware Sampling**:
   - Prioritize **build/upgrade actions** in early game and **VP-generating actions** in late game.
   - Increase sampling budget to **60 actions** (up from 30) and sample **3 actions per type** (up from 2).

2. **Expected Value for Chance Actions**:
   - Use `execute_spectrum` or `expand_spectrum` to compute expected value for **robber/chance actions** (e.g., dev cards, robber placement).
   - Cap spectrum outcomes at **8** to balance accuracy and runtime.

3. **Deterministic Tie-Breaking**:
   - Use tuple `(score, vp_delta, -lexicographic_repr(action))` for deterministic comparison.

4. **Fallbacks**:
   - If `execute_spectrum` fails, fall back to deterministic evaluation.
   - If all evaluations fail, return the first playable action.

---

### **Pseudocode for `foo_player.py` Updates**

#### **1. New Parameters (Add to Class)**
```python
MAX_ACTIONS_TO_EVAL = 60
SAMPLE_PER_ACTION_TYPE = 3
SPECTRUM_MAX_OUTCOMES = 8
EARLY_TURN_THRESHOLD = 30
TOP_K_DEEP = 0  # Disable opponent-aware refinement for now
```

#### **2. Helper Predicates (Add to Class)**
```python
def _is_build_or_upgrade(self, action) -> bool:
    """Check if action is a build/upgrade (settlement, city, road)."""
    action_type = getattr(action, "action_type", None)
    return action_type in {
        ActionType.BUILD_SETTLEMENT,
        ActionType.BUILD_CITY,
        ActionType.BUILD_ROAD,
        ActionType.UPGRADE_SETTLEMENT,
    }

def _is_robber_or_chance(self, action) -> bool:
    """Check if action involves chance (robber, dev card)."""
    action_type = getattr(action, "action_type", None)
    return action_type in {
        ActionType.PLAY_DEV_CARD,
        ActionType.PLACE_ROBBER,
        ActionType.DRAW_DEV_CARD,
    }
```

#### **3. Updated `sample_actions` Method**
```python
def _sample_actions(self, playable_actions: Iterable, game: Game) -> List:
    """Phase-aware sampling: prioritize builds early, VP late."""
    actions = list(playable_actions)
    if len(actions) <= self.MAX_ACTIONS_TO_EVAL:
        return actions

    # Determine game phase
    current_turn = getattr(game, "current_turn", 0)
    early_game = current_turn <= self.EARLY_TURN_THRESHOLD

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

    # Phase-aware sampling
    sampled = []
    rng = random.Random(self.RNG_SEED + sum(ord(c) for c in str(self.color)))
    for key in sorted(groups.keys()):
        group = groups[key]
        sample_count = self.SAMPLE_PER_ACTION_TYPE
        # Bias sampling
        if early_game and any(self._is_build_or_upgrade(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
        # Sample deterministically
        rng.shuffle(group)
        sampled.extend(group[:sample_count])

    # 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

    return sampled
```

#### **4. Updated `evaluate_action` Method**
```python
def _evaluate_action(self, game: Game, action, my_color: Color) -> Optional[Tuple[float, float]]:
    """Evaluate action using expected value for chance actions."""
    try:
        game_copy = copy_game(game)
    except Exception:
        return None

    # Handle chance/robber actions with expected value
    if self._is_robber_or_chance(action):
        try:
            spectrum = execute_spectrum(game_copy, action)[:self.SPECTRUM_MAX_OUTCOMES]
            if not spectrum:
                return None
            weighted_score = 0.0
            vp_delta_sum = 0.0
            for outcome_game, prob in spectrum:
                score = base_fn()(outcome_game, my_color)
                weighted_score += prob * score
                # Approximate VP delta (if visible VP is trackable)
                vp_delta_sum += prob * (
                    getattr(outcome_game, "visible_vp", {}).get(my_color, 0)
                    - getattr(game, "visible_vp", {}).get(my_color, 0)
                )
            return (weighted_score, vp_delta_sum)
        except Exception:
            pass  # Fall back to deterministic

    # Default deterministic evaluation
    try:
        outcomes = execute_deterministic(game_copy, action)
        if not outcomes:
            return None
        resultant_game = outcomes[0][0] if isinstance(outcomes[0], tuple) else outcomes[0]
        score = base_fn()(resultant_game, my_color)
        vp_delta = (
            getattr(resultant_game, "visible_vp", {}).get(my_color, 0)
            - getattr(game, "visible_vp", {}).get(my_color, 0)
        )
        return (score, vp_delta)
    except Exception:
        return None
```

#### **5. Updated `decide` Method**
```python
def decide(self, game: Game, playable_actions: Iterable):
    """Refined 1-ply lookahead with phase-aware sampling and expected value."""
    actions = list(playable_actions)
    if not actions:
        return None
    if len(actions) == 1:
        return actions[0]

    candidates = self._sample_actions(actions, game)
    best_action, best_score, best_vp_delta = None, -float("inf"), -float("inf")

    for action in candidates:
        eval_result = self._evaluate_action(game, action, self.color)
        if eval_result is None:
            continue
        score, vp_delta = eval_result
        # Tie-break: (score, vp_delta, -repr(action))
        if (
            best_action is None
            or score > best_score
            or (score == best_score and vp_delta > best_vp_delta)
            or (score == best_score and vp_delta == best_vp_delta and repr(action) < repr(best_action))
        ):
            best_action, best_score, best_vp_delta = action, score, vp_delta

    return best_action if best_action else actions[0]
```

---

### **Key Design Choices**
- **Phase-Aware Sampling**: Biases toward **builds early** and **VP late** to align with Catan strategy.
- **Expected Value for Chance**: Uses `execute_spectrum` for robber/dev cards, capped at 8 outcomes.
- **Deterministic Tie-Breaks**: Ensures reproducibility.
- **Fallbacks**: Gracefully handles missing adapters or evaluation failures.

---

### **Success Criteria**
- **Target**: >50% win rate and avg VP ≥ opponent in 30 games vs AlphaBeta.
- **If Marginal (<52%)**: Increase `MAX_ACTIONS_TO_EVAL` or enable `TOP_K_DEEP` opponent-aware refinement.

---
### **Deliverables for CODER**
1. Replace `sample_actions`, `evaluate_action`, and `decide` with the pseudocode above.
2. Add the new parameters and helper predicates to the class.
3. Test with 30 games and report metrics.

END STRATEGIZER
================================ Human Message =================================

This is the current performance history

{
  "Evolution 0": {
    "wins": 0,
    "avg_score": 2.0,
    "avg_turns": 72.16666666666667,
    "full_game_log_path": "game_20250925_030949_fg/game_output.txt",
    "json_game_results_path": "game_20250925_030949_fg/game_20250925_030949_fg.json",
    "cur_foo_player_path": "game_20250925_030949_fg/foo_player.py",
    "cli_run_id": "game_20250925_030949_fg",
    "timestamp": "2025-09-25 03:10:25"
  },
  "Evolution 1": {
    "wins": 14,
    "avg_score": 7.833333333333333,
    "avg_turns": 73.43333333333334,
    "full_game_log_path": "game_20250925_031330_fg/game_output.txt",
    "json_game_results_path": "game_20250925_031330_fg/game_20250925_031330_fg.json",
    "cur_foo_player_path": "game_20250925_031330_fg/foo_player.py",
    "cli_run_id": "game_20250925_031330_fg",
    "timestamp": "2025-09-25 03:14:22"
  },
  "Evolution 2": {
    "wins": 17,
    "avg_score": 8.133333333333333,
    "avg_turns": 71.46666666666667,
    "full_game_log_path": "game_20250925_031827_fg/game_output.txt",
    "json_game_results_path": "game_20250925_031827_fg/game_20250925_031827_fg.json",
    "cur_foo_player_path": "game_20250925_031827_fg/foo_player.py",
    "cli_run_id": "game_20250925_031827_fg",
    "timestamp": "2025-09-25 03:19:20"
  }
}
================================ Human Message =================================

This is the current foo_player.py file

import random
import traceback
from typing import Iterable, List, Optional, Tuple

# Must import adapters via the provided thin wrapper. Do NOT import catanatron directly.
from .adapters import (
    Game,
    Player,
    Color,
    ActionType,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    expand_spectrum,
    base_fn,
)


class FooPlayer(Player):
    """A player that uses a phase-aware 1-ply lookahead with expected-value for chance actions.

    Strategy summary:
    - Phase-aware sampling/pruning of playable actions to keep runtime bounded.
    - For each sampled candidate:
        - Copy the game state (copy_game).
        - For chance-like actions (robber/dev-card): use execute_spectrum/expand_spectrum to compute expected value.
        - Otherwise execute deterministically (execute_deterministic).
        - Evaluate resulting states with the adapters base value function (base_fn()).
    - Select the action maximizing (score, vp_delta) with a deterministic tie-break on repr(action).

    Interactions with the engine are done through the adapters surface only.
    Debug printing is available by setting self.debug = True on the instance.
    """

    # Tunable class defaults (updated per STRATEGIZER recommendations)
    MAX_ACTIONS_TO_EVAL: int = 60
    SAMPLE_PER_ACTION_TYPE: int = 3
    SPECTRUM_MAX_OUTCOMES: int = 8
    EARLY_TURN_THRESHOLD: int = 30
    TOP_K_DEEP: int = 0  # reserved for future opponent-aware refinement (disabled by default)
    RNG_SEED: int = 0

    def __init__(self, name: Optional[str] = None):
        # Initialize as BLUE by default (preserve original behavior)
        super().__init__(Color.BLUE, name)
        # Toggle to True to get per-turn diagnostic prints
        self.debug: bool = False
        # Pre-create the value function from adapters.base_fn factory if possible.
        # base_fn returns a callable: (game, color) -> float.
        try:
            self._value_fn = base_fn()
        except Exception:
            # If the factory has a different signature, lazily resolve in evaluation.
            self._value_fn = None

    # ------------------ Helper methods ------------------
    def _action_type_key(self, action) -> str:
        """Return a stable grouping key for an action.

        Prefer action.action_type, then other attributes, then class name or string.
        """
        k = getattr(action, "action_type", None)
        if k is not None:
            return str(k)
        for attr in ("type", "name"):
            k = getattr(action, attr, None)
            if k is not None:
                return str(k)
        try:
            return action.__class__.__name__
        except Exception:
            return str(action)

    def _is_build_or_upgrade(self, action) -> bool:
        """Detect actions that build or upgrade (settlement, city, road, upgrade).

        This function is defensive: it checks action_type when available and falls back
        to class name matching so grouping remains robust.
        """
        at = getattr(action, "action_type", None)
        try:
            # Compare against ActionType enum values when possible
            return at in {
                ActionType.BUILD_SETTLEMENT,
                ActionType.BUILD_CITY,
                ActionType.BUILD_ROAD,
                # Some code-bases may expose upgrade as a separate type; include common names
            }
        except Exception:
            # Fallback to name-based detection
            name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
            name_str = str(name).lower()
            return any(k in name_str for k in ("build", "settle", "city", "road", "upgrade"))

    def _is_robber_or_chance(self, action) -> bool:
        """Detect robber placement or development-card (chance) actions.

        Uses action_type when available; otherwise checks common name tokens.
        """
        at = getattr(action, "action_type", None)
        try:
            return at in {
                ActionType.PLAY_DEV_CARD,
                ActionType.PLACE_ROBBER,
                ActionType.DRAW_DEV_CARD,
            }
        except Exception:
            name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
            name_str = str(name).lower()
            return any(k in name_str for k in ("robber", "dev", "development", "draw"))

    def _get_visible_vp(self, game: Game, my_color: Color) -> int:
        """Try to extract a visible/observable victory point count for my_color.

        This is intentionally defensive: if no visible metric exists, return 0.
        """
        try:
            vp_map = getattr(game, "visible_vp", None)
            if isinstance(vp_map, dict):
                return int(vp_map.get(my_color, 0))
        except Exception:
            pass
        # As a conservative fallback, check for an attribute `visible_victory_points` or similar
        try:
            vp_map = getattr(game, "visible_victory_points", None)
            if isinstance(vp_map, dict):
                return int(vp_map.get(my_color, 0))
        except Exception:
            pass
        # If nothing is available, return 0 — we avoid inventing game internals
        return 0

    def _sample_actions(self, playable_actions: Iterable, game: Game) -> List:
        """Phase-aware sampling: prioritize builds early, VP actions late.

        Returns a deterministic, pruned list of candidate actions up to MAX_ACTIONS_TO_EVAL.
        """
        actions = list(playable_actions)
        n = len(actions)
        if n <= self.MAX_ACTIONS_TO_EVAL:
            return actions

        # Determine phase using available heuristics on game. Use tick or current_turn if present.
        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)

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

        # Deterministic RNG seeded with a combination of RNG_SEED and player's color
        color_seed = sum(ord(c) for c in str(self.color))
        rng = random.Random(self.RNG_SEED + color_seed)

        sampled: List = []
        # Iterate through groups in a stable order to keep behavior deterministic
        for key in sorted(groups.keys()):
            group = list(groups[key])
            # Determine how many to sample from this group, with phase-aware bias
            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 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:
                # If any checks fail, fall back to default sample_count
                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

        # If under budget, fill deterministically from remaining actions
        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 "late"
            print(f"_sample_actions: phase={phase}, pruned {n} -> {len(sampled)} actions (cap={self.MAX_ACTIONS_TO_EVAL})")
        return sampled

    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.

        - For robber/chance actions, attempt to use execute_spectrum/expand_spectrum to compute expected value.
        - Otherwise run execute_deterministic and score the single resulting state.

        Any exception during evaluation for a specific action results in None so other actions
        can still be considered.
        """
        # 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()
            return None

        # Ensure we have a value function callable
        if self._value_fn is None:
            try:
                self._value_fn = base_fn()
            except Exception as e:
                if self.debug:
                    print("base_fn() factory failed during evaluate_action:", e)
                    traceback.print_exc()
                return None

        # Helper to safely compute numeric score from value function
        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):
            try:
                # Prefer execute_spectrum if available
                spectrum = None
                try:
                    spectrum = execute_spectrum(game_copy, action)
                except Exception:
                    # Try expand_spectrum with a single-action list and extract
                    try:
                        spec_map = expand_spectrum(game_copy, [action])
                        if isinstance(spec_map, dict):
                            spectrum = spec_map.get(action, [])
                    except Exception:
                        spectrum = None

                if spectrum:
                    # Cap outcomes for runtime
                    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:
                        # entry expected to be (game_state, prob) but be defensive
                        try:
                            outcome_game, prob = entry
                        except Exception:
                            # Unexpected shape; skip this outcome
                            continue
                        sc = score_for(outcome_game)
                        if sc is None:
                            # If any outcome cannot be scored, abort spectrum evaluation
                            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 None:
                        # Fall back to deterministic evaluation below
                        if self.debug:
                            print("Spectrum evaluation produced an unscorable outcome; falling back to deterministic for", repr(action))
                    else:
                        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()
                # Fall through to deterministic handling

        # Default deterministic evaluation
        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()
            return None

        # Normalize to a single resulting game state (pick the first outcome deterministically)
        try:
            if not outcomes:
                if self.debug:
                    print("execute_deterministic returned empty outcomes for", repr(action))
                return None
            first = outcomes[0]
            if isinstance(first, (list, tuple)) and len(first) >= 1:
                resultant_game = first[0]
            else:
                resultant_game = first
        except Exception:
            # As a last resort, use the mutated game_copy
            resultant_game = game_copy

        # Score and vp delta
        sc = score_for(resultant_game)
        if sc is None:
            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

        return (float(sc), float(vp_delta))

    # ------------------ Decision method (public) ------------------
    def decide(self, game: Game, playable_actions: Iterable):
        """Choose an action from playable_actions using the refined 1-ply lookahead.

        The selection prioritizes (score, vp_delta) and breaks ties deterministically by
        lexicographic repr(action).
        """
        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)

        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

        evaluated = 0
        for action in candidates:
            evaluated += 1
            eval_res = self._evaluate_action(game, action, self.color)
            if self.debug:
                print(f"Evaluated action [{evaluated}/{len(candidates)}]: {repr(action)} -> {eval_res}")

            if eval_res is None:
                continue
            score, vp_delta = eval_res

            tie_repr = repr(action)
            # Compare by (score, vp_delta, -repr) where repr smaller is preferred deterministically
            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

            # Optional budget guard: stop early if we've evaluated MAX_ACTIONS_TO_EVAL candidates
            if evaluated >= self.MAX_ACTIONS_TO_EVAL:
                if self.debug:
                    print("decide: reached evaluation budget; stopping early")
                break

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

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

        return best_action

================================ Human Message =================================

This is the current adapters.py file

"""Unified adapter for Catanatron agents.

Expose a small, stable surface for multi-agent systems to:
- Inspect game state
- Enumerate legal actions
- Execute hypothetical moves (with/without validation)
- Expand chance outcomes (dice, dev cards, robber)
- Use pruning helpers
- Build/evaluate heuristics

Everything here is a thin re-export or trivial wrapper from catanatron & friends.
"""

from typing import Callable, List, Optional, Tuple, Dict, Any

# CORE RE-EXPORTS
from catanatron.game import Game  # Game instance with .state, .copy(), .execute(action), .winning_color()
from catanatron.models.player import Player, Color  # Player and Color types
from catanatron.models.enums import Action, ActionType  # Action = namedtuple("Action", ["color", "action_type", "value"]) 

# Player and debug node classes (re-exported so consumers can import them from adapters)
from catanatron_experimental.machine_learning.players.minimax import (
    AlphaBetaPlayer,  # Player that executes an AlphaBeta search with expected value calculation
    SameTurnAlphaBetaPlayer,  # AlphaBeta constrained to the same turn
    DebugStateNode,  # Node for debugging the AlphaBeta search tree
    DebugActionNode,  # Node representing an action in the AlphaBeta search tree
)
from catanatron_experimental.machine_learning.players.value import (
    ValueFunctionPlayer,  # Player using heuristic value functions
    DEFAULT_WEIGHTS,  # Default weight set for value functions
)

# Underlying implementation imports (underscore aliases to avoid recursion)
from catanatron_experimental.machine_learning.players.tree_search_utils import (
    execute_deterministic as _execute_deterministic,
    execute_spectrum as _execute_spectrum,
    expand_spectrum as _expand_spectrum,
    list_prunned_actions as _list_prunned_actions,  # spelling verified in source
    prune_robber_actions as _prune_robber_actions,
)
from catanatron_experimental.machine_learning.players.minimax import render_debug_tree as _render_debug_tree

from catanatron_experimental.machine_learning.players.value import (
    base_fn as _base_fn,
    contender_fn as _contender_fn,
    value_production as _value_production,
    get_value_fn as _get_value_fn,
)

# Public API
__all__ = [
    "Game",
    "Player",
    "Color",
    "Action",
    "ActionType",
    "AlphaBetaPlayer",
    "SameTurnAlphaBetaPlayer",
    "ValueFunctionPlayer",
    "DebugStateNode",
    "DebugActionNode",
    "copy_game",
    "execute_deterministic",
    "execute_spectrum",
    "expand_spectrum",
    "list_prunned_actions",
    "prune_robber_actions",
    "render_debug_tree",
    "base_fn",
    "contender_fn",
    "value_production",
    "get_value_fn",
]

# THIN CONVENIENCE WRAPPERS
def copy_game(game: Game) -> Game:
    '''Create a deep copy of the game state.'''
    return game.copy()

def execute_deterministic(game: Game, action: Action) -> List[Tuple[Game, float]]:
    '''Execute a deterministic action and return the resulting game state with probability 1.'''
    return _execute_deterministic(game, action)

def execute_spectrum(game: Game, action: Action) -> List[Tuple[Game, float]]:
    '''Return a list of (game_copy, probability) tuples for all possible outcomes of an action.'''
    return _execute_spectrum(game, action)

def expand_spectrum(game: Game, actions: List[Action]) -> Dict[Action, List[Tuple[Game, float]]]:
    '''Expand a game state into all possible outcomes for a list of actions.'''
    return _expand_spectrum(game, actions)

def list_prunned_actions(game: Game) -> List[Action]:
    '''Returns a pruned list of actions to reduce the search space.'''
    return _list_prunned_actions(game)

def prune_robber_actions(current_color: Color, game: Game, actions: List[Action]) -> List[Action]:
    '''Prunes robber actions to keep only the most impactful ones.'''
    return _prune_robber_actions(current_color, game, actions)

def render_debug_tree(node: DebugStateNode) -> str:
    '''Renders the AlphaBeta search tree using Graphviz.'''
    return _render_debug_tree(node)

# HEURISTIC BUILDERS
def base_fn(params=DEFAULT_WEIGHTS) -> Callable[[Game, Color], float]:
    '''Base value function factory for evaluating game states.'''
    return _base_fn(params)

def contender_fn(params) -> Callable[[Game, Color], float]:
    '''Alternative value function factory with tuned weights.'''
    return _contender_fn(params)

def value_production(sample, player_name: str = "P0", include_variety: bool = True) -> float:
    '''Compute the production value of a player's state.'''
    return _value_production(sample, player_name, include_variety)

def get_value_fn(name: str, params, value_function=None) -> Callable[[Game, Color], float]:
    '''Factory that returns a value function by name and parameters.'''
    return _get_value_fn(name, params, value_function)

================================ Human Message =================================

- META THOUGHTS:
  - Progress is solid: FooPlayer moved from 0 wins to 17/30 wins with the 1-ply + spectrum + phase-aware sampling changes. The remaining weaknesses are long-term planning (roads/longest-road), more effective robber/dev-card play, and occasional pruning of important actions.
  - A focused next improvement is to add a selective 2-ply adversarial lookahead (our action → opponent response) for a small set of top candidate actions. This preserves the adapter-only constraint and avoids handcrafted heuristics: evaluate our top 1-ply actions, then simulate likely opponent responses and use a minimax (adversarial) score to choose actions robust to opponent counters. Use spectrum-based expected values for chance nodes when available.
  - Trade-offs: 2-ply increases computation. To keep runtime bounded, apply 2-ply only to TOP_K_1PLY best 1-ply candidates and limit opponent action sampling. Keep deterministic behavior via seeded RNG. If adapters for getting opponent playable actions are not available, fall back to simple opponent-modeling via executing the opponent's best 1-ply action found by sampling and base_fn.

- META GOAL:
  - Produce a clear, adapter-driven, implementable STRATEGY: selective 2-ply (adversarial) lookahead using copy_game, execute_deterministic, execute_spectrum/expand_spectrum, and base_fn. Provide exact pseudocode, parameter defaults, tie-break rules, complexity bounds, and robust fallbacks for CODER to implement directly.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Create a concise, concrete strategy specification that CODER can implement to add a selective 2-ply adversarial lookahead to foo_player.py. Requirements:

1) Adapter functions to use (explicit)
   - copy_game(game)
   - execute_deterministic(game_copy, action)
   - execute_spectrum(game_copy, action) OR expand_spectrum(game_copy, action)
   - base_fn(game, color) OR base_fn()(game, color)
   - (Optional) adapters.get_playable_actions(game) or game.playable_actions() — if not present, provide fallback approach (see below).

2) New parameters and defaults (place as class defaults)
   - TOP_K_1PLY = 12  # number of top 1-ply candidates to deepen
   - OP_MAX_ACTIONS = 20  # cap opponent actions to consider per state
   - OP_SAMPLE_PER_ACTION_TYPE = 2  # opponent sampling per action type
   - MAX_ACTIONS_TO_EVAL (keep 60 from last iteration)
   - SPECTRUM_MAX_OUTCOMES (keep 8)
   - RNG_SEED (keep as before)
   - TIMEOUT_PER_DECISION_SEC = None (optional; only if environment supports timing)

3) High-level algorithm (what to implement)
   - Step A: Run current 1-ply pipeline for all sampled candidate actions -> obtain 1-ply (score, vp_delta) for each candidate (reuse existing _evaluate_action).
   - Step B: Sort candidates by 1-ply score (descending). Keep top TOP_K_1PLY candidates as the set to deepen; if fewer candidates exist, use all.
   - Step C: For each candidate a in top-K:
       a. Simulate a to get resulting game state(s):
          - If action is chance-like and spectrum is available: get spectrum outcomes and probabilities; each outcome_game_i has prob p_i.
          - Else: get deterministic outcome(s) via execute_deterministic; if execute_deterministic returns multiple deterministic branches, treat each as a separate outcome with implied probabilities (e.g., equal or use returned probabilities if present).
       b. For each outcome_game_i (limit total outcomes per a by SPECTRUM_MAX_OUTCOMES):
           - Generate a set of opponent playable actions OppActions_i from outcome_game_i:
               - Preferred: call adapters.get_playable_actions(outcome_game_i) or outcome_game_i.playable_actions() to obtain playable actions for the opponent (determine opponent color as outcome_game_i.current_player or compute next to move).
               - Fallback: if no API, approximate by fetching the global playable_actions passed into this player's decide for that game state is not available; instead, derive opponent actions by simulating the opponent's top responses using a sampled/pruned set of actions (reuse _sample_actions but applied in opponent context).
           - Prune OppActions_i to at most OP_MAX_ACTIONS using the same grouping+sampling strategy but seeded deterministically with RNG_SEED + hash(opponent_color).
           - For each opponent action b in OppActions_i (sample/prune as above):
               - Simulate b on a deep copy of outcome_game_i:
                   - If b is chance-like with spectrum available, compute expected outcomes (cap SPECTRUM_MAX_OUTCOMES).
                   - Otherwise execute_deterministic.
               - For each resulting game state after opponent, evaluate base_fn(result_game, my_color) to get final_score_ijlk.
           - Aggregate opponent responses into an adversarial value for outcome_game_i:
               - Adversarial (min) approach: opponent will choose action that minimizes our final score → value_i = min_b final_score_ijlk
               - Optionally, if you prefer expectation: value_i = sum_b (prob_b * final_score_ijlk) if probabilities for opponent actions are known (rare). Use adversarial/min by default.
       c. Combine outcome_game_i values into a single value for candidate a:
           - If candidate had multiple outcome branches with probabilities p_i, compute expected_value_a = sum_i p_i * value_i.
   - Step D: Choose the action a with highest expected_value_a. Use deterministic tie-breaker: (expected_value, 1-p(locally visible VP tie), repr(action) lexicographic).

4) Pseudocode (compact, exact, for CODER to implement)
   - Reuse existing helper functions: _sample_actions, _evaluate_action, _action_type_key, _is_robber_or_chance, etc.
   - New function sketch:

function decide_with_2ply(self, game, playable_actions):
    actions = list(playable_actions)
    if not actions: return None
    if len(actions) == 1: return actions[0]

    # Stage 1: 1-ply evaluate (reuse existing _evaluate_action)
    sampled = self._sample_actions(actions, game)  # existing
    one_ply_results = []  # list of (action, score, vp_delta, eval_outcomes)
    for a in sampled:
        # _evaluate_action should be able to return deterministic/outcome info OR we can regenerate outcomes below
        score_vp = self._evaluate_action(game, a, self.color)
        if score_vp is None:
            continue
        score, vp_delta = score_vp
        one_ply_results.append((a, score, vp_delta))

    if not one_ply_results:
        return actions[0]

    # Stage 2: select top-K by score to deepen
    one_ply_results.sort(key=lambda t: (t[1], t[2]), reverse=True)
    top_candidates = [t[0] for t in one_ply_results[:self.TOP_K_1PLY]]

    best_action = None
    best_value = -inf

    for a in top_candidates:
        # simulate a -> get outcome branches
        try:
            game_copy = copy_game(game)
        except Exception:
            continue
        # Prefer spectrum for chance-likes
        if self._is_robber_or_chance(a) and has_spectrum_api:
            try:
                spectrum = execute_spectrum(game_copy, a) or expand_spectrum(game_copy, a)
                # Normalize to list of (game_outcome, prob) and cap to SPECTRUM_MAX_OUTCOMES
            except Exception:
                spectrum = None
        else:
            spectrum = None

        if spectrum:
            outcomes = normalize_and_cap(spectrum, self.SPECTRUM_MAX_OUTCOMES)
            # outcomes: list of (outcome_game, prob)
        else:
            # deterministic fallback
            try:
                det_res = execute_deterministic(game_copy, a)
                outcomes = normalize_det_to_outcomes(det_res)  # list of (game_outcome, prob=1.0/len)
            except Exception:
                continue

        # For candidate a, compute expected adversarial value across outcome branches
        expected_value_a = 0.0
        for outcome_game, p_i in outcomes:
            # Determine opponent color from outcome_game (e.g., outcome_game.current_player)
            opp_color = determine_opponent_color(outcome_game, self.color)
            # Get opponent playable actions
            try:
                opp_actions = adapters.get_playable_actions(outcome_game)  # preferred if exists
            except Exception:
                opp_actions = derive_playable_actions_via_game_api(outcome_game, opp_color)
            if not opp_actions:
                # if opponent has no meaningful actions, evaluate directly
                val_i = safe_eval_base_fn(outcome_game, self.color)
                expected_value_a += p_i * val_i
                continue

            # Prune opponent actions deterministically
            opp_sampled = self._sample_actions(opp_actions, outcome_game)[:self.OP_MAX_ACTIONS]

            # For adversarial opponent, compute min over opponent responses
            min_score_after_opp = +inf
            for b in opp_sampled:
                # simulate opponent action b (use spectrum if b chance-like)
                val_after_b = simulate_and_evaluate(outcome_game, b, self.color)
                if val_after_b is None:
                    continue
                if val_after_b < min_score_after_opp:
                    min_score_after_opp = val_after_b

            # If opponent had no successful sims, fallback to base_fn on outcome_game
            if min_score_after_opp is inf:
                min_score_after_opp = safe_eval_base_fn(outcome_game, self.color)

            expected_value_a += p_i * min_score_after_opp

        # After all outcomes: compare expected_value_a
        # Deterministic tie-break: prefer higher expected_value, then higher 1-ply vp_delta, then repr(action) lexicographically smaller
        tie_key = (expected_value_a, get_1ply_vp_delta_for_action(a, one_ply_results), -repr(a))
        if expected_value_a > best_value (or tie resolved via tie_key):
            best_value = expected_value_a
            best_action = a

    return best_action if best_action else actions[0]

Helper functions to implement: normalize_and_cap, normalize_det_to_outcomes, determine_opponent_color, derive_playable_actions_via_game_api, simulate_and_evaluate (which uses execute_spectrum/execute_deterministic + base_fn evaluation with same robust fallbacks as current code).

5) Tie-break and determinism
   - Primary: expected_value_a (higher is better)
   - Secondary: 1-ply vp_delta (higher)
   - Final: lexicographically smaller repr(action)
   - Use deterministic RNG seeded with RNG_SEED + stable_hash(self.color) for all sampling.

6) Complexity & safeguards
   - Workload: TOP_K_1PLY * (avg_outcomes_per_candidate) * OP_MAX_ACTIONS * (avg_outcomes_per_opp_action)
   - Defaults keep this bounded: TOP_K_1PLY=12, outcomes capped at SPECTRUM_MAX_OUTCOMES=8, OP_MAX_ACTIONS=20. Worst-case could be large; CODER must enforce early skips if counts exceed reasonable computational budget or if an optional TIMEOUT_PER_DECISION_SEC is set.
   - If computation grows too large at runtime, reduce TOP_K_1PLY to 6 or OP_MAX_ACTIONS to 10.

7) Fallbacks (explicit)
   - If adapters.get_playable_actions is not available, fallback to deriving opponent actions using outcome_game.playable_actions() or by calling the engine API the project uses. If none available, approximate opponent by sampling a small set of legal-looking actions (reuse _sample_actions on outcome_game with opponent_color) — ensure deterministic seed.
   - If execute_spectrum/expand_spectrum unavailable or raises, fall back to deterministic execute_deterministic for both our actions and opponent actions.
   - If base_fn API differs, try both base_fn(game, color) and base_fn()(game, color). On persistent mismatch, fall back to returning 1-ply best action.
   - Never throw from decide(); on critical failure, return playable_actions[0] (deterministic fallback) and log error when debug=True.

8) Metrics & success criteria
   - Re-run N=30 games vs AlphaBetaPlayer:
       - Success: win rate >= 55% OR avg VP >= opponent by >=0.5 and improved roads/army/dev usage.
       - If win rate drops or runtime becomes unacceptable (>2x previous), scale back TOP_K_1PLY / OP_MAX_ACTIONS.

9) Deliverables for CODER
   - Implement the selective 2-ply algorithm above by:
       - Adding TOP_K_1PLY, OP_MAX_ACTIONS, OP_SAMPLE_PER_ACTION_TYPE constants.
       - Adding functions: normalize_and_cap, determine_opponent_color, derive_playable_actions_via_game_api, simulate_and_evaluate (reusing current _evaluate_action where possible).
       - Modify decide() to run 1-ply, select top-K, then compute adversarial expected values per pseudocode, and select best action.
       - Add debug logging showing top-K candidates and their 2-ply values for traceability.
   - Run a test batch of 30 games (debug=False) and report full performance JSON plus a small set (3) of debug traces (enable debug=True for 3 games) showing decisions where 2-ply changed action choice versus 1-ply.

10) If CODER encounters adapter signature errors or missing functions for opponent playable actions, capture full exception traces and return them verbatim so we can call RESEARCHER.

Keep the change focused: only add selective 2-ply adversarial lookahead (no handcrafted scoring). Provide exact pseudocode and parameter defaults above so CODER can implement it directly.

--- Output from STRATEGIZER at 20250925_032118 ---
================================== Ai Message ==================================
Tool Calls:
  read_adapter (LIDv9kU0t)
 Call ID: LIDv9kU0t
  Args:
================================= Tool Message =================================
Name: read_adapter

"""Unified adapter for Catanatron agents.

Expose a small, stable surface for multi-agent systems to:
- Inspect game state
- Enumerate legal actions
- Execute hypothetical moves (with/without validation)
- Expand chance outcomes (dice, dev cards, robber)
- Use pruning helpers
- Build/evaluate heuristics

Everything here is a thin re-export or trivial wrapper from catanatron & friends.
"""

from typing import Callable, List, Optional, Tuple, Dict, Any

# CORE RE-EXPORTS
from catanatron.game import Game  # Game instance with .state, .copy(), .execute(action), .winning_color()
from catanatron.models.player import Player, Color  # Player and Color types
from catanatron.models.enums import Action, ActionType  # Action = namedtuple("Action", ["color", "action_type", "value"]) 

# Player and debug node classes (re-exported so consumers can import them from adapters)
from catanatron_experimental.machine_learning.players.minimax import (
    AlphaBetaPlayer,  # Player that executes an AlphaBeta search with expected value calculation
    SameTurnAlphaBetaPlayer,  # AlphaBeta constrained to the same turn
    DebugStateNode,  # Node for debugging the AlphaBeta search tree
    DebugActionNode,  # Node representing an action in the AlphaBeta search tree
)
from catanatron_experimental.machine_learning.players.value import (
    ValueFunctionPlayer,  # Player using heuristic value functions
    DEFAULT_WEIGHTS,  # Default weight set for value functions
)

# Underlying implementation imports (underscore aliases to avoid recursion)
from catanatron_experimental.machine_learning.players.tree_search_utils import (
    execute_deterministic as _execute_deterministic,
    execute_spectrum as _execute_spectrum,
    expand_spectrum as _expand_spectrum,
    list_prunned_actions as _list_prunned_actions,  # spelling verified in source
    prune_robber_actions as _prune_robber_actions,
)
from catanatron_experimental.machine_learning.players.minimax import render_debug_tree as _render_debug_tree

from catanatron_experimental.machine_learning.players.value import (
    base_fn as _base_fn,
    contender_fn as _contender_fn,
    value_production as _value_production,
    get_value_fn as _get_value_fn,
)

# Public API
__all__ = [
    "Game",
    "Player",
    "Color",
    "Action",
    "ActionType",
    "AlphaBetaPlayer",
    "SameTurnAlphaBetaPlayer",
    "ValueFunctionPlayer",
    "DebugStateNode",
    "DebugActionNode",
    "copy_game",
    "execute_deterministic",
    "execute_spectrum",
    "expand_spectrum",
    "list_prunned_actions",
    "prune_robber_actions",
    "render_debug_tree",
    "base_fn",
    "contender_fn",
    "value_production",
    "get_value_fn",
]

# THIN CONVENIENCE WRAPPERS
def copy_game(game: Game) -> Game:
    '''Create a deep copy of the game state.'''
    return game.copy()

def execute_deterministic(game: Game, action: Action) -> List[Tuple[Game, float]]:
    '''Execute a deterministic action and return the resulting game state with probability 1.'''
    return _execute_deterministic(game, action)

def execute_spectrum(game: Game, action: Action) -> List[Tuple[Game, float]]:
    '''Return a list of (game_copy, probability) tuples for all possible outcomes of an action.'''
    return _execute_spectrum(game, action)

def expand_spectrum(game: Game, actions: List[Action]) -> Dict[Action, List[Tuple[Game, float]]]:
    '''Expand a game state into all possible outcomes for a list of actions.'''
    return _expand_spectrum(game, actions)

def list_prunned_actions(game: Game) -> List[Action]:
    '''Returns a pruned list of actions to reduce the search space.'''
    return _list_prunned_actions(game)

def prune_robber_actions(current_color: Color, game: Game, actions: List[Action]) -> List[Action]:
    '''Prunes robber actions to keep only the most impactful ones.'''
    return _prune_robber_actions(current_color, game, actions)

def render_debug_tree(node: DebugStateNode) -> str:
    '''Renders the AlphaBeta search tree using Graphviz.'''
    return _render_debug_tree(node)

# HEURISTIC BUILDERS
def base_fn(params=DEFAULT_WEIGHTS) -> Callable[[Game, Color], float]:
    '''Base value function factory for evaluating game states.'''
    return _base_fn(params)

def contender_fn(params) -> Callable[[Game, Color], float]:
    '''Alternative value function factory with tuned weights.'''
    return _contender_fn(params)

def value_production(sample, player_name: str = "P0", include_variety: bool = True) -> float:
    '''Compute the production value of a player's state.'''
    return _value_production(sample, player_name, include_variety)

def get_value_fn(name: str, params, value_function=None) -> Callable[[Game, Color], float]:
    '''Factory that returns a value function by name and parameters.'''
    return _get_value_fn(name, params, value_function)

================================== Ai Message ==================================

STRATEGIZER
**Selective 2-Ply Adversarial Lookahead Strategy**

---
### **Core Improvements**
1. **Selective 2-Ply Lookahead**:
   - Apply 2-ply adversarial evaluation to the **top 12 1-ply candidates** (configurable via `TOP_K_1PLY`).
   - Simulate opponent responses using `execute_deterministic` or `execute_spectrum` for chance actions.
   - Use **minimax adversarial scoring** to select actions robust to opponent counters.

2. **Opponent Action Sampling**:
   - Cap opponent actions at **20** (`OP_MAX_ACTIONS`).
   - Sample **2 actions per type** (`OP_SAMPLE_PER_ACTION_TYPE`) for deterministic opponent modeling.

3. **Fallbacks**:
   - If `get_playable_actions` is unavailable, derive opponent actions from the game state or reuse existing sampling logic.
   - Fall back to 1-ply evaluation if 2-ply computation fails.

4. **Deterministic Tie-Breaking**:
   - Use tuple `(expected_value, 1-ply vp_delta, -lexicographic_repr(action))` for deterministic comparison.

---

### **Pseudocode for `foo_player.py` Updates**

#### **1. New Parameters (Add to Class)**
```python
TOP_K_1PLY: int = 12
OP_MAX_ACTIONS: int = 20
OP_SAMPLE_PER_ACTION_TYPE: int = 2
```

#### **2. Helper Functions (Add to Class)**
```python
def _normalize_and_cap_spectrum(self, spectrum: List[Tuple[Game, float]], cap: int) -> List[Tuple[Game, float]]:
    """Normalize spectrum outcomes and cap to `cap` entries."""
    if not spectrum:
        return []
    # Cap outcomes
    capped = spectrum[:cap]
    # Renormalize probabilities if needed
    total_prob = sum(p for _, p in capped)
    if total_prob > 0:
        normalized = [(g, p / total_prob) for g, p in capped]
    else:
        normalized = [(g, 1.0 / len(capped)) for g, _ in capped]
    return normalized

def _determine_opponent_color(self, game: Game, my_color: Color) -> Color:
    """Determine opponent color from game state."""
    try:
        current_player = getattr(game, "current_player", None)
        if current_player and current_player != my_color:
            return current_player
    except Exception:
        pass
    # Fallback: assume two-player game and return the other color
    all_colors = list(Color)
    for color in all_colors:
        if color != my_color:
            return color
    return my_color  # should not happen

def _derive_opponent_actions(self, game: Game, opponent_color: Color) -> List[Action]:
    """Fallback: derive opponent actions if `get_playable_actions` is unavailable."""
    try:
        # Try to use game.playable_actions() if available
        playable = getattr(game, "playable_actions", lambda: [])()
        if playable:
            return playable
    except Exception:
        pass
    # Fallback: reuse _sample_actions logic for opponent
    try:
        # Simulate opponent's perspective by sampling actions
        # Note: This is a fallback and may not be perfect
        all_actions = list_prunned_actions(game)
        sampled = self._sample_actions(all_actions, game)
        return sampled
    except Exception:
        return []

def _simulate_and_evaluate(self, game: Game, action, my_color: Color) -> Optional[float]:
    """Simulate an action and evaluate the resulting game state."""
    try:
        game_copy = copy_game(game)
    except Exception:
        return None
    # Use spectrum for chance actions
    if self._is_robber_or_chance(action):
        try:
            spectrum = execute_spectrum(game_copy, action)
            if spectrum:
                outcomes = self._normalize_and_cap_spectrum(spectrum, self.SPECTRUM_MAX_OUTCOMES)
                weighted_score = 0.0
                for outcome_game, prob in outcomes:
                    score = self._value_fn(outcome_game, my_color)
                    weighted_score += prob * score
                return weighted_score
        except Exception:
            pass  # Fall back to deterministic
    # Deterministic fallback
    try:
        outcomes = execute_deterministic(game_copy, action)
        if not outcomes:
            return None
        resultant_game = outcomes[0][0] if isinstance(outcomes[0], tuple) else outcomes[0]
        return self._value_fn(resultant_game, my_color)
    except Exception:
        return None
```

#### **3. Updated `decide` Method**
```python
def decide(self, game: Game, playable_actions: Iterable):
    """Selective 2-ply adversarial lookahead."""
    actions = list(playable_actions)
    if not actions:
        return None
    if len(actions) == 1:
        return actions[0]

    # Stage 1: 1-ply evaluation
    sampled = self._sample_actions(actions, game)
    one_ply_results = []
    for a in sampled:
        eval_res = self._evaluate_action(game, a, self.color)
        if eval_res is None:
            continue
        score, vp_delta = eval_res
        one_ply_results.append((a, score, vp_delta))

    if not one_ply_results:
        return actions[0]

    # Stage 2: Select top-K candidates
    one_ply_results.sort(key=lambda t: (t[1], t[2]), reverse=True)
    top_candidates = [t[0] for t in one_ply_results[:self.TOP_K_1PLY]]

    best_action, best_value = None, -float("inf")

    for a in top_candidates:
        # Simulate action a
        try:
            game_copy = copy_game(game)
        except Exception:
            continue
        # Use spectrum for chance actions
        if self._is_robber_or_chance(a):
            try:
                spectrum = execute_spectrum(game_copy, a)
                outcomes = self._normalize_and_cap_spectrum(spectrum, self.SPECTRUM_MAX_OUTCOMES)
            except Exception:
                outcomes = None
        else:
            outcomes = None
        # Deterministic fallback
        if not outcomes:
            try:
                det_res = execute_deterministic(game_copy, a)
                if not det_res:
                    continue
                resultant_game = det_res[0][0] if isinstance(det_res[0], tuple) else det_res[0]
                outcomes = [(resultant_game, 1.0)]
            except Exception:
                continue

        # Stage 3: Evaluate opponent responses
        expected_value_a = 0.0
        for outcome_game, p_i in outcomes:
            opp_color = self._determine_opponent_color(outcome_game, self.color)
            opp_actions = self._derive_opponent_actions(outcome_game, opp_color)
            if not opp_actions:
                # No opponent actions: evaluate directly
                score = self._simulate_and_evaluate(outcome_game, None, self.color)
                if score is None:
                    continue
                expected_value_a += p_i * score
                continue

            # Prune opponent actions
            opp_sampled = self._sample_actions(opp_actions, outcome_game)[:self.OP_MAX_ACTIONS]

            # Adversarial evaluation: opponent minimizes our score
            min_score_after_opp = float("inf")
            for b in opp_sampled:
                opp_score = self._simulate_and_evaluate(outcome_game, b, self.color)
                if opp_score is None:
                    continue
                if opp_score < min_score_after_opp:
                    min_score_after_opp = opp_score

            if min_score_after_opp == float("inf"):
                min_score_after_opp = self._value_fn(outcome_game, self.color)

            expected_value_a += p_i * min_score_after_opp

        # Stage 4: Compare expected values
        tie_key = (
            expected_value_a,
            next((vp_delta for a_cand, _, vp_delta in one_ply_results if a_cand == a), 0),
            -repr(a)
        )
        if expected_value_a > best_value or (expected_value_a == best_value and tie_key > (best_value, 0, 0)):
            best_value = expected_value_a
            best_action = a

    return best_action if best_action else actions[0]
```

---
### **Key Design Choices**
- **Selective 2-Ply**: Only deepen the top 12 1-ply candidates to balance runtime and accuracy.
- **Adversarial Scoring**: Opponent actions are evaluated to minimize our score, ensuring robust action selection.
- **Fallbacks**: Gracefully handle missing adapters or evaluation failures.
- **Deterministic Tie-Breaking**: Ensures reproducibility.

---
### **Success Criteria**
- **Target**: ≥55% win rate or avg VP ≥ opponent by ≥0.5 in 30 games vs AlphaBeta.
- **If Runtime Exceeds 2x**: Reduce `TOP_K_1PLY` to 6 or `OP_MAX_ACTIONS` to 10.

---
### **Deliverables for CODER**
1. Add the new parameters and helper functions to `foo_player.py`.
2. Replace the `decide` method with the updated pseudocode.
3. Test with 30 games and report metrics.

END STRATEGIZER

