--- Input for CODER at 20250925_032155 ---
================================ Human Message =================================

- META THOUGHTS:
  - Analyzer confirmed the root cause: foo_player.py always returns playable_actions[0]. Strategizer proposed a 1-ply deterministic lookahead using adapters.copy_game, adapters.execute_deterministic, and adapters.base_fn. I will now instruct CODER to implement that precisely, with robust fallbacks, sampling/pruning, deterministic tie-breaking, and logging so we can evaluate progress.
- META GOAL:
  - Implement a 1-ply deterministic lookahead decision policy in foo_player.py that uses only adapters functions (copy_game, execute_deterministic, base_fn), with safe sampling/pruning and deterministic tie-breakers. Then run the next experiment (30 games) and report results.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the 1-ply deterministic lookahead described by the Strategizer directly into foo_player.py. Make changes minimal and drop-in so the test harness runs automatically. Follow these exact implementation requirements and defaults.

1) Imports and constants
- Add at top of foo_player.py:
  - import random
  - from .adapters import copy_game, execute_deterministic, base_fn
- Add class-level defaults in the player class (e.g., FooPlayer):
  - MAX_ACTIONS_TO_EVAL = 30
  - SAMPLE_PER_ACTION_TYPE = 2
  - RNG_SEED = 0  # deterministic sampling across runs

2) New helper methods to add inside the player class
- _action_type_key(action): return a stable key string for grouping actions. Implement robustly:
  - Try to use action.action_type, then getattr(action, "type", None), then action.__class__.__name__, then str(action).
- _sample_actions(playable_actions): returns a pruned list of candidate actions:
  - If len(playable_actions) <= MAX_ACTIONS_TO_EVAL: return playable_actions.
  - Else:
    - Group by _action_type_key(action).
    - For each group, use a deterministic Random(rng_seed) instance to sample up to SAMPLE_PER_ACTION_TYPE actions per group.
    - Collect sampled actions until MAX_ACTIONS_TO_EVAL; if still under limit, add remaining actions by deterministic iteration until reaching MAX_ACTIONS_TO_EVAL.
  - Use deterministic RNG: rng = random.Random(self.RNG_SEED + hash(self.color)) to vary between colors but remain reproducible.

3) _evaluate_action(game, action, my_color):
- Purpose: copy the game, execute the action deterministically, and evaluate the resulting state with base_fn.
- Implementation details:
  - Try:
    - game_copy = copy_game(game)
    - res = execute_deterministic(game_copy, action)
      - Accept both possible return styles:
        - If res is None or False: treat as failure and return None
        - If res is a list/iterable: take first element. If element is (game_obj, info), extract game_obj; else if element is game_obj, use it.
        - If res is a single game object, use it.
    - score = base_fn(game_copy, my_color) OR base_fn()(game_copy, my_color)? Use base_fn(game_obj, my_color). If that raises TypeError, fall back to base_fn()(game_obj, my_color) inside a try.
  - Catch all exceptions and return None (log the exception).
  - Return numeric score on success.

4) Replacement decide(...) method
- Signature: decide(self, game, playable_actions)
- Steps:
  - If not playable_actions: return None
  - If len(playable_actions) == 1: return playable_actions[0]
  - Use sampled_actions = self._sample_actions(playable_actions)
  - Iterate sampled_actions and call _evaluate_action for each:
    - Keep best_action and best_score.
    - Use deterministic tie-break: if score == best_score, compare tie_key = (score, repr(action) sorted lexicographically). Always pick the action with the larger tuple (score, tie_key) where tie_key = lexicographically smallest repr(action) to prefer stable choice.
  - If no action yields a valid score (best_action is None):
    - As fallback, return playable_actions[0] (preserves prior deterministic behavior).
  - Return best_action.

5) Logging / Debugging hooks
- Print (or use self.logger if present) at debug level:
  - Number of playable_actions and sampled_actions size.
  - For each evaluated action: action repr and score (or 'failed').
  - Final chosen action and score.
- Avoid excessive logs in normal runs; gate with an attribute like self.debug = True (default False). Use self.debug to decide whether to print.

6) Edge-case & robustness handling
- If execute_deterministic returns multiple deterministic outcomes, pick the first entry (index 0) consistently.
- If base_fn raises TypeError or returns None, catch and treat as failed evaluation.
- If action groups are named inconsistently across actions, grouping falls back to class name or str(action) - preserves diversity.
- Deterministic randomness: Use random.Random(self.RNG_SEED + hash(self.color)) seeded once inside _sample_actions.

7) Complexity & Safeguards
- The evaluate loop should stop early if time becomes a concern. Implement an optional early exit: if number of evaluated actions reaches MAX_ACTIONS_TO_EVAL, break.
- Keep the copied game references short-lived to allow GC.

8) Expected behavior after coding
- The decide method must use only adapters.copy_game, adapters.execute_deterministic, adapters.base_fn for evaluation logic (no handcrafted scoring rules).
- Once implemented, start a new experiment of 30 games against AlphaBetaPlayer and report:
  - Wins, AVG VP for both players, AVG settlements/cities/roads, and average turns.
- If you encounter attribute or signature errors calling adapters functions, log the exact exception text and return playable_actions[0] as safe fallback. If exceptions are due to wrong function signatures, report them so we can call RESEARCHER.

9) Provide the exact code to insert
- Implement these methods verbatim inside foo_player.py (inside the player class); here's the implementation outline to be added (do not include triple-backticks in file):

from .adapters import copy_game, execute_deterministic, base_fn
import random, traceback

# class-level defaults
MAX_ACTIONS_TO_EVAL = 30
SAMPLE_PER_ACTION_TYPE = 2
RNG_SEED = 0

def _action_type_key(self, action):
    # robust grouping key
    for attr in ("action_type", "type", "name"):
        k = getattr(action, attr, None)
        if k:
            return str(k)
    try:
        return action.__class__.__name__
    except Exception:
        return str(action)

def _sample_actions(self, playable_actions):
    if len(playable_actions) <= self.MAX_ACTIONS_TO_EVAL:
        return list(playable_actions)
    groups = {}
    for a in playable_actions:
        key = self._action_type_key(a)
        groups.setdefault(key, []).append(a)
    rng = random.Random(self.RNG_SEED + (hash(self.color) & 0xffffffff))
    sampled = []
    # sample up to SAMPLE_PER_ACTION_TYPE per group
    for key in sorted(groups.keys()):
        group = groups[key]
        k = min(self.SAMPLE_PER_ACTION_TYPE, len(group))
        # deterministic sample: shuffle copy then take first k
        grp_copy = list(group)
        rng.shuffle(grp_copy)
        sampled.extend(grp_copy[:k])
        if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
            break
    # fill up deterministically if under limit
    if len(sampled) < self.MAX_ACTIONS_TO_EVAL:
        for a in playable_actions:
            if a not in sampled:
                sampled.append(a)
                if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
                    break
    return sampled

def _evaluate_action(self, game, action, my_color):
    try:
        game_copy = copy_game(game)
    except Exception as e:
        if getattr(self, "debug", False):
            print("copy_game failed:", e)
        return None
    try:
        res = execute_deterministic(game_copy, action)
    except Exception as e:
        if getattr(self, "debug", False):
            print("execute_deterministic failed:", e)
        return None
    # normalize returned game object
    new_game = None
    try:
        if res is None:
            return None
        # If res is a list/iterable, pick first element
        if isinstance(res, (list, tuple)):
            first = res[0]
            # some wrappers return (game, info)
            if hasattr(first, "__class__") and getattr(first, "__class__").__name__ != "tuple":
                new_game = first if not isinstance(first, tuple) else first[0]
            else:
                new_game = first[0] if isinstance(first, tuple) else first
        else:
            # assume single game returned or game_copy modified in place
            new_game = res if hasattr(res, "current_player", None) else game_copy
    except Exception:
        new_game = game_copy
    # evaluate
    try:
        score = base_fn(new_game, my_color)
    except TypeError:
        try:
            # fallback in case base_fn is a factory
            score = base_fn()(new_game, my_color)
        except Exception as e:
            if getattr(self, "debug", False):
                print("base_fn evaluation failed:", e)
            return None
    except Exception as e:
        if getattr(self, "debug", False):
            print("base_fn evaluation error:", e)
        return None
    # ensure numeric
    try:
        return float(score)
    except Exception:
        return None

def decide(self, game, playable_actions):
    if not playable_actions:
        return None
    if len(playable_actions) == 1:
        return playable_actions[0]
    my_color = self.color
    sampled = self._sample_actions(playable_actions)
    if getattr(self, "debug", False):
        print(f"Evaluating {len(sampled)} / {len(playable_actions)} actions")
    best_action = None
    best_score = -float('inf')
    best_tie_repr = None
    for action in sampled:
        score = self._evaluate_action(game, action, my_color)
        if getattr(self, "debug", False):
            print("Action:", repr(action), "Score:", score)
        if score is None:
            continue
        tie_repr = repr(action)
        # compare (score, -tie_repr) where lexicographically smaller tie_repr wins;
        # to pick deterministic stable action, use tuple (score, -tie_repr) concept:
        if best_action is None or (score > best_score) or (score == best_score and tie_repr < best_tie_repr):
            best_action = action
            best_score = score
            best_tie_repr = tie_repr
    if best_action is None:
        # fallback
        if getattr(self, "debug", False):
            print("No evaluated action succeeded; falling back to first playable action")
        return playable_actions[0]
    if getattr(self, "debug", False):
        print("Chosen action:", repr(best_action), "score:", best_score)
    return best_action

10) Run the experiment
- After implementing, run the batch of 30 games vs AlphaBetaPlayer exactly as before and capture the new performance metrics.
- If you encounter exceptions calling adapters functions (TypeError, AttributeError), capture full stack trace and stop the run, return the trace so we can decide if RESEARCHER is required.

11) Reporting
- Provide the new performance JSON and game logs:
  - Wins, AVG VP per player, AVG settlements/cities/roads/dev cards, and AVG turns.
  - If wins remain 0 or there's no measurable improvement in VP or builds, note that and include logs of debug prints for several sample turns (enable self.debug = True for a short run).

If anything in adapters has a different signature than assumed, catch the exception and log the text exactly so we can decide whether to call RESEARCHER. Implement the code exactly as specified above, prioritize stability and deterministic behavior over exploring more complex search for this iteration.
================================== Ai Message ==================================

CODER

- Files modified
  - Rewrote foo_player.py entirely to implement a 1-ply deterministic lookahead using the provided adapters.

- High-level changes
  - Replaced the previous trivial policy (always returning playable_actions[0]) with an adapter-driven 1-ply lookahead that:
    - Samples/prunes playable actions (keeps runtime bounded).
    - For each sampled candidate: copies the game (copy_game), executes the action deterministically (execute_deterministic), and evaluates the resulting state with adapters.base_fn().
    - Selects the highest-scoring action, using a deterministic tie-break (lexicographic repr(action)).

- New/Updated functions and methods (inside FooPlayer)
  - __init__: now initializes debug flag and pre-creates the value function via base_fn() when possible.
  - _action_type_key(action): robust grouping key for action types (uses action_type, type, name, class name, or str).
  - _sample_actions(playable_actions): prunes actions if > MAX_ACTIONS_TO_EVAL by grouping by action type and taking SAMPLE_PER_ACTION_TYPE per group using a deterministic RNG; fills remainder deterministically.
  - _evaluate_action(game, action, my_color): copies the game, executes action deterministically, normalizes outcome, evaluates with base_fn, and returns a numeric score or None on failure.
  - decide(game, playable_actions): main decision routine implementing the 1-ply lookahead, deterministic tie-breaking, and robust fallbacks.

- Constants / class defaults added
  - MAX_ACTIONS_TO_EVAL = 30
  - SAMPLE_PER_ACTION_TYPE = 2
  - RNG_SEED = 0
  - debug attribute on instance (default False) to enable diagnostic printing.

- Debug / logging
  - Added optional debug prints distributed across sampling, evaluation, and final decision. Enable by setting player.debug = True.
  - Prints include action repr and scores for transparency when debugging.

- Error handling and fallbacks
  - copy_game, execute_deterministic, and base_fn calls are wrapped in try/except; failures for particular actions return None so other candidates can be evaluated.
  - If no candidate yields a valid evaluation, the code falls back to returning the first playable action (original behavior), preserving deterministic operation.
  - If base_fn factory has a different signature, the code tries to lazily create the value function and logs errors when debug=True.

- Adapter usage
  - The code uses only the adapters surface:
    - copy_game, execute_deterministic, base_fn
  - No direct imports from catanatron were added. The file includes the required "from .adapters import ..." pattern.

- Print statements added
  - Conditional debug prints via self.debug. No unconditional noisy prints remain (so runs are quiet by default). You can enable debug output for a small test run by creating the player and setting player.debug = True.

- Robustness notes / assumptions
  - Assumes execute_deterministic returns a list-like of outcomes where the first element is either (Game, probability) or a Game-like object. The implementation normalizes these cases and, as a last resort, treats game_copy as mutated in place.
  - Assumes base_fn() returns a callable of signature (Game, Color) -> float. The code attempts to handle variations by lazily building base_fn and catching TypeErrors.
  - Grouping of actions uses action.action_type or fallback attributes. If action types are inconsistent, sampling still proceeds using class name or str(action).

- Things I would like clarified in the next objective (useful for further evolution)
  - Would you like a different default MAX_ACTIONS_TO_EVAL or SAMPLE_PER_ACTION_TYPE for more aggressive search (increase to 50 or SAMPLE_PER_ACTION_TYPE=4)?
  - When running batch tests, should I enable debug prints for a few games to capture example decision traces (recommended for diagnosing failures)?
  - If we want deterministic randomness across entire experiment runs, do you prefer seeding using a global seed value (instead of color-based seed) and passing it in as a parameter?

- Next recommended steps
  - Run the same 30-game experiment vs AlphaBetaPlayer with the new foo_player.py and collect metrics (Wins, AVG VP, settlements/cities/roads).
  - If errors occur due to adapter signature differences, capture the exact stack traces (enable debug=True for a small run) and forward them.
  - If the 1-ply lookahead shows improvement but insufficiently, iterate to:
    - Increase MAX_ACTIONS_TO_EVAL or SAMPLE_PER_ACTION_TYPE, or
    - Implement 2-ply limited search using expand_spectrum/execute_spectrum, or
    - Add light heuristics (e.g., prefer building/upgrading actions when scores near tie).

- Miscellaneous
  - I kept behavior deterministic by default (seed derived from RNG_SEED + color) to help reproducible debugging; randomness is confined to sampling and can be adjusted.
  - All code conforms to Python 3.11 syntax and the Coding Guidelines: comments, no backslash-escaped comments, and adapters-only interaction.

If you want, I can now:
- Run a small debug run (enable debug) for several turns to print decision traces, or
- Adjust MAX_ACTIONS_TO_EVAL/SAMPLE_PER_ACTION_TYPE and re-write foo_player.py accordingly.

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

- META THOUGHTS:
  - The 1-ply lookahead made FooPlayer competitive (14/30 wins). Remaining gap is small and systematic: action pruning may be cutting important moves, chance/robber actions are not fully evaluated, and tie-breaking is naive.
  - The next iteration should keep the 1-ply core and implement three focused improvements: (1) increase/adapt sampling budget with phase-awareness, (2) compute expected value for chance/robber actions using execute_spectrum/expand_spectrum (with a cap), and (3) improve deterministic tie-breaking by using (score, vp_delta, repr) tuple.
  - Keep changes minimal and adapter-only (copy_game, execute_deterministic, base_fn, plus optional execute_spectrum/expand_spectrum). If adapter signatures differ, catch exceptions and return the safe fallback so runs don't crash; capture traces for RESEARCHER if needed.

- META GOAL:
  - Implement refined sampling + chance-action expected-value + deterministic tie-breaks in foo_player.py so we can run the next 30-game experiment and measure whether FooPlayer reaches parity or better versus AlphaBeta (target >50% wins or avg VP ≥ opponent).

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the STRATEGIZER refinements into foo_player.py. Follow these concrete requirements exactly; keep behavior deterministic and robust; do not add handcrafted scoring heuristics.

1) Add/Update Class Constants (defaults)
   - MAX_ACTIONS_TO_EVAL = 60
   - SAMPLE_PER_ACTION_TYPE = 3
   - SPECTRUM_MAX_OUTCOMES = 8
   - EARLY_TURN_THRESHOLD = 30
   - TOP_K_DEEP = 0  # keep off by default
   - RNG_SEED = 0

2) Helper predicates (inside the player class)
   - _action_type_key(action): existing robust implementation to group actions.
   - _is_build_or_upgrade(action): return True for build/upgrade action types (use action.action_type or class name).
   - _is_robber_or_chance(action): return True for robber placement and dev-card actions.

3) Replace _sample_actions(playable_actions, game)
   - Behavior:
     - If len(actions) <= MAX_ACTIONS_TO_EVAL -> return all.
     - Determine phase: early_game = current_turn <= EARLY_TURN_THRESHOLD (use game.current_turn or game.tick).
     - Group by _action_type_key.
     - For each group (deterministically iterated by sorted keys), choose sample_count = SAMPLE_PER_ACTION_TYPE, plus +1 if group contains build/upgrade in early game, or +1 if group contains VP-generating actions in late game.
     - Use deterministic RNG = random.Random(RNG_SEED + stable_hash(self.color)) to shuffle groups before picking sample_count.
     - Collect sampled actions; if < MAX_ACTIONS_TO_EVAL, fill deterministically from remaining actions until reaching MAX_ACTIONS_TO_EVAL.
   - Return sampled list.

4) Implement _evaluate_action(game, action, my_color)
   - Use copy_game(game) -> game_copy. If copy fails, return None.
   - If _is_robber_or_chance(action) and execute_spectrum or expand_spectrum exists:
     - Try to call expand_spectrum(game_copy, action) or execute_spectrum(game_copy, action).
     - Normalize result to a list of (outcome_game, prob) and cap outcomes to SPECTRUM_MAX_OUTCOMES (take top outcomes or first N).
     - Compute expected_score = sum(prob * base_fn(outcome_game, my_color)) across outcomes.
     - Compute expected_vp_delta similarly using visible VP if accessible (fallback to 0 if not).
     - Return (expected_score, expected_vp_delta).
     - If any exceptions occur or adapter absent, catch and fall back to deterministic branch.
   - Else deterministic branch:
     - outcomes = execute_deterministic(game_copy, action) (catch exceptions and return None).
     - Normalize to resultant_game (take first outcome if list/tuple, or assume game_copy mutated).
     - score = base_fn(resultant_game, my_color) — support both base_fn(game, color) and base_fn()(game, color) by trying both forms.
     - vp_delta = visible_vp(resultant_game, my_color) - visible_vp(game, my_color) if visible_vp fields exist; else compute 0.
     - Return (score, vp_delta).
   - Always handle and suppress exceptions per-action (return None) so other candidates proceed. When debug=True, log full exception trace.

5) Update decide(game, playable_actions)
   - If no actions -> return None; if single action -> return it.
   - candidates = self._sample_actions(playable_actions, game).
   - Evaluate each candidate via _evaluate_action; collect tuple (action, score, vp_delta, repr(action)).
   - Selection rule: maximize tuple (score, vp_delta, -repr_key) where -repr_key is implemented by lexicographic smaller repr being preferred (i.e., when scores equal and vp_delta equal, pick repr(action) < repr(best_action)).
   - If multiple top candidates remain, deterministic tie-break as above.
   - If no candidate returns a valid evaluation, return playable_actions[0] as safe fallback.
   - Keep TOP_K_DEEP disabled by default; add code path RESERVED for future opponent-aware refinement but do not run it unless TOP_K_DEEP > 0.

6) Determinism & RNG
   - Use deterministic RNG seeded with RNG_SEED + stable_hash(self.color) (stable_hash can be sum of ords of color string or hash(self.color) & 0xffffffff).
   - All sampling/shuffling must use this RNG instance.

7) Logging / debug
   - Use self.debug bool (default False). When True:
     - Log phase (early/late), counts/limits used.
     - For each evaluated action: method (deterministic / spectrum), score, vp_delta, and any exceptions.
     - Final chosen action with score/vp_delta.
   - When False: minimal/no prints.

8) Robustness & fallbacks
   - If execute_spectrum/expand_spectrum not present or signature mismatch -> gracefully fall back to execute_deterministic; log exception under debug mode and capture trace.
   - If base_fn signature differs, try both base_fn(game, color) and base_fn()(game, color).
   - Never raise exceptions from decide; always return either a chosen action or playable_actions[0] / None as per above.

9) Tests to run after implementation
   - Run the same batch of 30 games vs AlphaBetaPlayer (same harness).
   - Report JSON metrics: Wins per player, AVG VP per player, AVG settlements/cities/roads/dev cards, AVG TURNS.
   - If any adapter call raises signature errors, capture full stack trace (enable debug=True for a short run) and include it verbatim; then we will call RESEARCHER.

10) If implementation succeeds without adapter errors:
   - If win rate >= 50% or avg VP >= opponent: proceed to small refinements (increase MAX_ACTIONS_TO_EVAL or enable TOP_K_DEEP).
   - If still marginal (<52% win rate), return the full logs and a few example decision traces (enable debug for 3 sample games) so we can analyze remaining mistakes.

Implement only the changes above this iteration. After coding, run the 30-game experiment and return the performance JSON and any debug logs (if enabled). If any adapter signature or unexpected exception occurs, include full stack trace so we can call RESEARCHER.
================================== Ai Message ==================================

CODER

Changes made to foo_player.py:
- Rewrote the player to implement the STRATEGIZER refinements:
  - Increased sampling and introduced phase-aware sampling (MAX_ACTIONS_TO_EVAL=60, SAMPLE_PER_ACTION_TYPE=3, EARLY_TURN_THRESHOLD=30).
  - Added expected-value evaluation for chance-like actions (robber/dev-card) using adapters.execute_spectrum and adapters.expand_spectrum with a cap (SPECTRUM_MAX_OUTCOMES=8).
  - Improved deterministic tie-breaking using (score, vp_delta, repr(action)).
  - Preserved strict use of adapters: copy_game, execute_deterministic, execute_spectrum, expand_spectrum, base_fn.
  - Added helper predicates: _is_build_or_upgrade, _is_robber_or_chance, _get_visible_vp.
  - _evaluate_action now returns (score, vp_delta) tuple or None on failure.
  - _sample_actions now accepts the game to determine phase (early/late) and biases sampling accordingly.
  - decide() now evaluates candidates with (score, vp_delta) selection and deterministic tie-breaks, with budget guard and robust fallbacks to the first playable action.

New/modified functions:
- __init__: attempt to pre-create base value function with base_fn(); fallback to lazy creation.
- _action_type_key: robust action grouping key.
- _is_build_or_upgrade: detect build/upgrade actions defensively.
- _is_robber_or_chance: detect chance-like actions defensively.
- _get_visible_vp: defensive extractor for visible VP data (used for vp_delta).
- _sample_actions(self, playable_actions, game): phase-aware deterministic sampling and pruning.
- _evaluate_action(self, game, action, my_color): spectrum-based expected-value for chance actions, deterministic fallback, returns (score, vp_delta).
- decide(self, game, playable_actions): main decision loop using new evaluation and tie-break.

Debug/logging:
- Uses self.debug flag (default False). When True, prints phase, sampling details, per-action evaluation results including spectrum diagnostics and exception traces.

Error handling & fallbacks:
- All adapter calls are wrapped with try/except. On per-action failures we skip that action (return None). If all actions fail, decide() returns the first playable action deterministically as a safe fallback.
- If execute_spectrum/expand_spectrum or base_fn has different signatures, code gracefully falls back to deterministic evaluate path and logs the exception when debug=True.

Notes / questions for next iteration:
- Should we enable TOP_K_DEEP opponent-aware refinement (currently disabled)? When enabled, this will simulate opponent responses for top-K candidates (adds complexity).
- If you want more aggressive sampling, increase MAX_ACTIONS_TO_EVAL and/or SAMPLE_PER_ACTION_TYPE. I suggest trying MAX_ACTIONS_TO_EVAL=80 and SAMPLE_PER_ACTION_TYPE=4 if runtime allows.
- If adapters raise unexpected signature errors during runs, enable debug and capture stack traces so we can involve RESEARCHER.

Next steps you asked previously:
- I implemented the requested code changes. I can now run the 30-game experiment (enable debug=False for clean runs) and report results, or run a short debug run (enable debug=True) to capture decision traces for a few turns. Which would you like me to do?

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

CODER

Changes made to foo_player.py:
- Rewrote the player to implement the STRATEGIZER refinements:
  - Increased sampling and introduced phase-aware sampling (MAX_ACTIONS_TO_EVAL=60, SAMPLE_PER_ACTION_TYPE=3, EARLY_TURN_THRESHOLD=30).
  - Added expected-value evaluation for chance-like actions (robber/dev-card) using adapters.execute_spectrum and adapters.expand_spectrum with a cap (SPECTRUM_MAX_OUTCOMES=8).
  - Improved deterministic tie-breaking using (score, vp_delta, repr(action)).
  - Preserved strict use of adapters: copy_game, execute_deterministic, execute_spectrum, expand_spectrum, base_fn.
  - Added helper predicates: _is_build_or_upgrade, _is_robber_or_chance, _get_visible_vp.
  - _evaluate_action now returns (score, vp_delta) tuple or None on failure.
  - _sample_actions now accepts the game to determine phase (early/late) and biases sampling accordingly.
  - decide() now evaluates candidates with (score, vp_delta) selection and deterministic tie-breaks, with budget guard and robust fallbacks to the first playable action.

New/modified functions:
- __init__: attempt to pre-create base value function with base_fn(); fallback to lazy creation.
- _action_type_key: robust action grouping key.
- _is_build_or_upgrade: detect build/upgrade actions defensively.
- _is_robber_or_chance: detect chance-like actions defensively.
- _get_visible_vp: defensive extractor for visible VP data (used for vp_delta).
- _sample_actions(self, playable_actions, game): phase-aware deterministic sampling and pruning.
- _evaluate_action(self, game, action, my_color): spectrum-based expected-value for chance actions, deterministic fallback, returns (score, vp_delta).
- decide(self, game, playable_actions): main decision loop using new evaluation and tie-break.

Debug/logging:
- Uses self.debug flag (default False). When True, prints phase, sampling details, per-action evaluation results including spectrum diagnostics and exception traces.

Error handling & fallbacks:
- All adapter calls are wrapped with try/except. On per-action failures we skip that action (return None). If all actions fail, decide() returns the first playable action deterministically as a safe fallback.
- If execute_spectrum/expand_spectrum or base_fn has different signatures, code gracefully falls back to deterministic evaluate path and logs the exception when debug=True.

Notes / questions for next iteration:
- Should we enable TOP_K_DEEP opponent-aware refinement (currently disabled)? When enabled, this will simulate opponent responses for top-K candidates (adds complexity).
- If you want more aggressive sampling, increase MAX_ACTIONS_TO_EVAL and/or SAMPLE_PER_ACTION_TYPE. I suggest trying MAX_ACTIONS_TO_EVAL=80 and SAMPLE_PER_ACTION_TYPE=4 if runtime allows.
- If adapters raise unexpected signature errors during runs, enable debug and capture stack traces so we can involve RESEARCHER.

Next steps you asked previously:
- I implemented the requested code changes. I can now run the 30-game experiment (enable debug=False for clean runs) and report results, or run a short debug run (enable debug=True) to capture decision traces for a few turns. Which would you like me to do?

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 13,
      "AVG VP": 7.466666666666667,
      "AVG SETTLES": 2.4,
      "AVG CITIES": 1.9,
      "AVG ROAD": 0.6,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.06666666666666667
    },
    "FooPlayer:BLUE": {
      "WINS": 17,
      "AVG VP": 8.133333333333333,
      "AVG SETTLES": 2.566666666666667,
      "AVG CITIES": 2.3333333333333335,
      "AVG ROAD": 0.4,
      "AVG ARMY": 0.03333333333333333,
      "AVG DEV VP": 0.03333333333333333
    }
  },
  "Game Summary": {
    "AVG TICKS": 204.93333333333334,
    "AVG TURNS": 71.46666666666667,
    "AVG DURATION": 1.6836426178614299
  }
}
================================ Human Message =================================

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

---
### PERFORMANCE SUMMARY
- **Outcome**: **Good** (17 wins, 13 losses).
- **VP Comparison**: FooPlayer (BLUE) averaged **8.13 VP** vs. AlphaBetaPlayer (RED) **7.47 VP**.
- **VP Difference**: **+0.66** (above parity).
- **Key Counts**:
  - FooPlayer: 2.57 settlements, 2.33 cities, 0.4 roads, 0.03 armies, 0.03 dev cards.
  - Opponent: 2.4 settlements, 1.9 cities, 0.6 roads, 0.0 armies, 0.07 dev cards.
  - **Total Turns**: 71.47 turns per game.

---
### VERDICT
**Good**: FooPlayer outperforms the opponent with a **VP difference of +0.66** and a **57% win rate**.

---
### STRATEGIC FLAW
While FooPlayer is now competitive, its **1-ply lookahead lacks depth for long-term planning**, particularly in **robber placement, development card usage, and road-building strategies**. This is evident in the lower average roads (0.4 vs. 0.6) and minimal army/dev card usage.

---
### LIKELY REASONS FOR SUBOPTIMAL PERFORMANCE
1. **Shallow Lookahead**:
   - The 1-ply lookahead (lines 140–200) evaluates actions **only one turn ahead**, missing multi-turn synergies (e.g., road-building for Longest Road).
   - **Corroborating Result**: Opponent builds more roads (0.6 vs. 0.4), suggesting better long-term positioning.

2. **Robber and Chance Handling**:
   - While `_is_robber_or_chance` (lines 70–90) identifies robber/dev card actions, the **expected value calculation** (lines 110–140) caps outcomes at `SPECTRUM_MAX_OUTCOMES=8`, potentially oversimplifying probabilistic decisions.
   - **Corroborating Result**: FooPlayer averages only **0.03 armies** and **0.03 dev cards**, indicating underutilization.

3. **Action Sampling Limits**:
   - `MAX_ACTIONS_TO_EVAL=60` and `SAMPLE_PER_ACTION_TYPE=3` (lines 22–23) may **prune high-value actions** in complex states.
   - **Corroborating Log**: No explicit errors, but the opponent’s higher road count suggests FooPlayer may miss critical expansion opportunities.

4. **Tie-Breaking Logic**:
   - The tie-break (line 180) uses `repr(action)` lexicographically, which may not prioritize **strategically superior actions** (e.g., city upgrades over roads).

---
### NEXT STEP
Send to **Strategizer** to **increase lookahead depth to 2-ply** and refine **robber/dev card policies** for long-term dominance.

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

- META THOUGHTS:
  - Analyzer shows FooPlayer is now stronger (17/30 wins) but still short on long-term planning (roads, robber/dev usage). Strategizer recommends adding a selective 2-ply adversarial lookahead applied only to the top 1-ply candidates to improve robustness without exploding runtime.
  - Next step is to have CODER implement that selective 2-ply exactly, reusing existing helpers (copy_game, execute_deterministic, execute_spectrum/expand_spectrum, base_fn) and the current sampling/evaluation code. Must be deterministic, robust to adapter signature differences, and fall back to 1-ply if problems occur.

- META GOAL:
  - Implement a selective 2-ply adversarial lookahead in foo_player.py (top-K deepen, opponent sampling, adversarial minimax over opponent responses) with the provided defaults, deterministic sampling, spectrum-based chance handling, and safe fallbacks. Then run 30 games vs AlphaBeta and report metrics and a few debug traces.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the STRATEGIZER selective 2-ply adversarial lookahead into foo_player.py. Follow these exact requirements and defaults. Keep behavior deterministic, robust, and adapter-only. If any adapter signature mismatches occur, catch and log the full exception and fallback to the 1-ply decision; capture traces for RESEARCHER if needed.

1) Add/Update Class Defaults (inside FooPlayer):
   - TOP_K_1PLY = 12
   - OP_MAX_ACTIONS = 20
   - OP_SAMPLE_PER_ACTION_TYPE = 2
   - Keep existing:
     - MAX_ACTIONS_TO_EVAL = 60
     - SAMPLE_PER_ACTION_TYPE = 3
     - SPECTRUM_MAX_OUTCOMES = 8
     - EARLY_TURN_THRESHOLD = 30
     - RNG_SEED = 0
     - debug (default False)

2) Add these helper methods (implement exactly as described):

a) _normalize_and_cap_spectrum(self, spectrum, cap)
   - Input: spectrum: iterable of (game_outcome, prob) as returned by execute_spectrum/expand_spectrum.
   - Behavior:
     - Convert to list, take first cap entries.
     - If probabilities sum > 0, normalize so they sum to 1; otherwise assign equal probabilities.
     - Return list[(game_outcome, prob_normalized)].
   - Catch exceptions and return empty list on failure.

b) _determine_opponent_color(self, game, my_color)
   - Try to read game.current_player or game.next_player to find opponent; if present and != my_color return it.
   - Fallback: iterate over known Color enumeration (if available) or use hash-based two-player assumption to select a different color deterministically.
   - Never raise; return something (may equal my_color as last resort).

c) _derive_opponent_actions(self, game, opponent_color)
   - Try in order:
     1. If adapters provides get_playable_actions(game) use it.
     2. Try outcome_game.playable_actions() or getattr(game, "playable_actions", lambda: [])().
     3. As final fallback, generate a stable list by calling existing _sample_actions on a list of all candidate actions derived from game if you can enumerate them; if not possible, return empty list.
   - All attempts wrapped in try/except; on exception return empty list and log when debug=True.

d) _simulate_and_evaluate(self, game, action, my_color)
   - Purpose: simulate a single action (chance-aware) from the given game state and return a numeric evaluation (float) for my_color or None on failure.
   - Steps:
     1. Try game_copy = copy_game(game). If fails, return None.
     2. If action is None: return safe_eval_base_fn(game_copy, my_color) (helper below).
     3. If self._is_robber_or_chance(action) and adapters.execute_spectrum/expand_spectrum exist:
         - Try to call execute_spectrum(game_copy, action) or expand_spectrum(game_copy, action).
         - Normalize and cap with _normalize_and_cap_spectrum(..., self.SPECTRUM_MAX_OUTCOMES).
         - For each (outcome_game, prob): compute score_i = safe_eval_base_fn(outcome_game, my_color); accumulate weighted_score.
         - Return weighted_score.
         - On any exception, fall through to deterministic fallback.
     4. Deterministic fallback:
         - Try outcomes = execute_deterministic(game_copy, action).
         - Normalize: if outcomes is list/tuple, take first outcome element; if first is (game_obj, info) take game_obj; else use game_copy as mutated.
         - Compute score = safe_eval_base_fn(resultant_game, my_color).
         - Return float(score) or None if eval fails.
   - safe_eval_base_fn(g, color): try calling self._value_fn(g, color). If self._value_fn is None, try:
       - value_fn = base_fn() and call value_fn(g, color)
       - or base_fn(g, color)
     Wrap both attempts in try/except; if both fail, return None. Log trace when debug=True.

3) Modify decide(...) to perform selective 2-ply:
   - Keep initial 1-ply pipeline unchanged (use existing _sample_actions and _evaluate_action to produce one_ply_results list of (action, score, vp_delta)).
   - Sort one_ply_results descending by (score, vp_delta). Select top_candidates = first TOP_K_1PLY actions.
   - For each candidate a in top_candidates:
       - Simulate a to get outcome branches:
           - Prefer spectrum: if self._is_robber_or_chance(a) and spectrum API exists, call execute_spectrum or expand_spectrum on a copy; normalize/cap to outcomes list via _normalize_and_cap_spectrum.
           - Else call execute_deterministic on a copy and normalize to a single outcome [(resultant_game, 1.0)] (or multiple if returned).
       - For each outcome_game, p_i in outcomes:
           - Determine opponent color opp_color = _determine_opponent_color(outcome_game, self.color).
           - Get opponent actions opp_actions = _derive_opponent_actions(outcome_game, opp_color).
           - If opp_actions empty: compute val_i = _simulate_and_evaluate(outcome_game, None, self.color) and accumulate expected_value_a += p_i * val_i (if val_i is None treat as 0 or skip; prefer skip and adjust normalization).
           - Else prune opp_actions deterministically:
               - opp_sampled = self._sample_actions(opp_actions, outcome_game)[:self.OP_MAX_ACTIONS]
               - For adversarial model (minimizer), compute min_score_after_opp = +inf
               - For each b in opp_sampled:
                   - val_after_b = _simulate_and_evaluate(outcome_game, b, self.color)
                   - If val_after_b is None: continue
                   - min_score_after_opp = min(min_score_after_opp, val_after_b)
               - If min_score_after_opp stayed +inf: fallback to val_i = _simulate_and_evaluate(outcome_game, None, self.color)
               - expected_value_a += p_i * min_score_after_opp
       - After all outcomes, expected_value_a is the adversarial expected score for candidate a.
   - Select best_action as the a with maximum expected_value_a. Use deterministic tie-break:
       - First key: expected_value_a (higher)
       - Second key: 1-ply vp_delta for that action (higher)
       - Final key: repr(action) lexicographically smaller wins
   - If best_action is None or errors prevent 2-ply completion for all, fall back to the highest 1-ply action (existing selection) or to playable_actions[0] as last resort.
   - Ensure decide never raises.

4) Determinism & RNG
   - Use deterministic RNG seed = random.Random(self.RNG_SEED + stable_color_hash) in sampling everywhere. stable_color_hash can be computed as sum(ord(c) for c in str(self.color)) & 0xffffffff.

5) Logging / debug
   - When self.debug True:
     - Log top-K 1-ply candidates and their 1-ply scores.
     - For each candidate evaluate and log outcome count, per-outcome p_i, opponent sampled count, min_score_after_opp, and final expected_value_a.
     - If any adapter call raises, log full traceback (use traceback.format_exc()).

6) Safeguards & resource limits
   - Enforce caps: at most TOP_K_1PLY candidates, outcomes per candidate capped by SPECTRUM_MAX_OUTCOMES, opponent actions capped by OP_MAX_ACTIONS.
   - If runtime of decide becomes too long, reduce TOP_K_1PLY or OP_MAX_ACTIONS. Provide easy-to-change class constants.
   - If total simulations would exceed threshold (optional): implement a simple counter of simulated nodes and stop deepening when a global MAX_SIMULATIONS limit is hit, falling back to best 1-ply.

7) Testing & reporting
   - After implementation, run 30 games vs AlphaBeta (debug=False):
       - Provide the performance JSON (wins, avg VP, avg settlements/cities/roads/dev cards, avg turns).
   - Additionally run 3 games with self.debug = True and capture decision traces for any 2–5 turns where 2-ply changed the chosen action vs 1-ply. Include those traces in the report.
   - If any adapter signature errors occur (TypeError, AttributeError, etc.), stop the 30-game batch, enable debug=True for a short run, capture full stack traces, and return them verbatim so we can call RESEARCHER.

8) Fail-safes
   - If at any point adapters required for opponent action enumeration are missing and the fallback provides no actions, the module must not crash — simply treat opponent as making no action (evaluate outcome_game directly) or fall back to 1-ply selection.

9) Implementation notes for CODER
   - Reuse existing functions where possible: _evaluate_action, _sample_actions, _action_type_key, _is_robber_or_chance, _get_visible_vp, and the pre-created self._value_fn if present.
   - Keep changes modular and well-commented.
   - Do not introduce handcrafted scoring rules — rely solely on base_fn for state valuation.
   - Keep all new code inside foo_player.py (no external modifications).

10) Report back
   - On completion, return:
     - The updated performance JSON from the 30-game batch.
     - The three debug game traces requested above (if run).
     - Any adapter exceptions/traces if occurred.

Implement this selective 2-ply now. If you encounter adapter signature mismatches or uncertainty about how to extract opponent playable actions, capture full error traces and return them so we can call RESEARCHER.
================================ Human Message =================================

This is the old foo_player.py file
Now It is your turn to update it with the new recommendations from META

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 that you must use to interact with the Catanatron API

"""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)


--- Output from CODER at 20250925_032424 ---
================================== Ai Message ==================================
Tool Calls:
  write_foo (call_7Q8UX4XWGdJm4pS1Gy5YLsEU)
 Call ID: call_7Q8UX4XWGdJm4pS1Gy5YLsEU
  Args:
    new_text: import random
import traceback
from typing import Iterable, List, Optional, Tuple, Dict, Any

# 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,
    list_prunned_actions,
)


class FooPlayer(Player):
    """A player that uses a selective 2-ply adversarial lookahead built on adapters.

    Key features:
    - Phase-aware 1-ply sampling/pruning to keep runtime bounded.
    - Chance-aware expected values using execute_spectrum/expand_spectrum when available.
    - Selective 2-ply adversarial (min over opponent responses) evaluation for top-K 1-ply
      candidates to improve robustness against counters.
    - Deterministic sampling/tie-breaking via seeded RNG.

    All interactions with the engine use only the adapters surface.
    Set self.debug = True on the instance to enable diagnostic printing.
    """

    # Tunable class defaults (STRATEGIZER recommendations)
    MAX_ACTIONS_TO_EVAL: int = 60
    SAMPLE_PER_ACTION_TYPE: int = 3
    SPECTRUM_MAX_OUTCOMES: int = 8
    EARLY_TURN_THRESHOLD: int = 30

    # New selective 2-ply parameters
    TOP_K_1PLY: int = 12
    OP_MAX_ACTIONS: int = 20
    OP_SAMPLE_PER_ACTION_TYPE: int = 2

    # reserved/compat
    TOP_K_DEEP: int = 0  # 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 _stable_color_hash(self, color: Color) -> int:
        """Stable small hash for a Color used to seed RNG deterministically.

        We keep this deterministic across runs by summing character ordinals of the color's
        string representation. This avoids relying on Python's randomized hash().
        """
        try:
            return sum(ord(c) for c in str(color)) & 0xFFFFFFFF
        except Exception:
            return 0

    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:
            return at in {
                ActionType.BUILD_SETTLEMENT,
                ActionType.BUILD_CITY,
                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 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
        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
        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: Dict[str, List] = {}
        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 = self._stable_color_hash(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:
                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 _sample_opponent_actions(self, playable_actions: Iterable, game: Game, opponent_color: Color) -> List:
        """Opponent-specific sampling that respects OP_SAMPLE_PER_ACTION_TYPE and OP_MAX_ACTIONS.

        Uses a deterministic RNG seeded with opponent color so opponent sampling is reproducible.
        """
        actions = list(playable_actions)
        n = len(actions)
        if n <= self.OP_MAX_ACTIONS:
            return actions

        # Phase detection reused from our own sampling
        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)

        groups: Dict[str, List] = {}
        for a in actions:
            key = self._action_type_key(a)
            groups.setdefault(key, []).append(a)

        color_seed = self._stable_color_hash(opponent_color)
        rng = random.Random(self.RNG_SEED + color_seed)

        sampled: List = []
        for key in sorted(groups.keys()):
            group = list(groups[key])
            # opponent sampling budget
            sample_count = self.OP_SAMPLE_PER_ACTION_TYPE
            try:
                if early_game and any(self._is_build_or_upgrade(a) 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.OP_MAX_ACTIONS:
                break

        if len(sampled) < self.OP_MAX_ACTIONS:
            for a in actions:
                if a not in sampled:
                    sampled.append(a)
                    if len(sampled) >= self.OP_MAX_ACTIONS:
                        break

        if self.debug:
            print(f"_sample_opponent_actions: pruned {n} -> {len(sampled)} actions (cap={self.OP_MAX_ACTIONS})")
        return sampled

    def _normalize_and_cap_spectrum(self, spectrum: Iterable, cap: int) -> List[Tuple[Game, float]]:
        """Normalize spectrum outcomes and cap to `cap` entries.

        Accepts iterables like those returned by execute_spectrum or expand_spectrum entry lists.
        Returns a list of (game, prob) with probabilities summing to 1.
        """
        try:
            lst = list(spectrum)
            if not lst:
                return []
            capped = lst[:cap]
            probs = []
            games = []
            for entry in capped:
                try:
                    g, p = entry
                except Exception:
                    # Unexpected shape: skip
                    continue
                games.append(g)
                probs.append(float(p))
            if not games:
                return []
            total = sum(probs)
            if total > 0.0:
                normalized = [(g, p / total) for g, p in zip(games, probs)]
            else:
                n = len(games)
                normalized = [(g, 1.0 / n) for g in games]
            return normalized
        except Exception:
            if self.debug:
                print("_normalize_and_cap_spectrum: failed to normalize spectrum")
                traceback.print_exc()
            return []

    def _determine_opponent_color(self, game: Game, my_color: Color) -> Color:
        """Try to determine the opponent's color from the game state.

        This is defensive: it checks common attributes and falls back to a two-player assumption.
        """
        try:
            cur = getattr(game, "current_player", None)
            if cur is not None:
                # If cur is a Player instance, extract its color attribute when possible
                try:
                    if cur != my_color:
                        return cur
                except Exception:
                    pass
        except Exception:
            pass

        # As a simple fallback, assume a two-player game and pick a different color deterministically
        try:
            colors = [c for c in list(Color)]
            if len(colors) >= 2:
                for c in colors:
                    if c != my_color:
                        return c
        except Exception:
            pass
        # Last resort: return my_color (harmless, though less correct)
        return my_color

    def _derive_opponent_actions(self, game: Game, opponent_color: Color) -> List:
        """Obtain a list of opponent actions with several fallbacks.

        Order:
        1) adapters.list_prunned_actions(game)
        2) game.playable_actions() if present
        3) empty list (conservative)
        """
        try:
            # Preferred: adapters-provided pruned action list (designed for search)
            pruned = list_prunned_actions(game)
            if pruned:
                return pruned
        except Exception:
            if self.debug:
                print("_derive_opponent_actions: list_prunned_actions failed")
                traceback.print_exc()

        try:
            pa = getattr(game, "playable_actions", None)
            if callable(pa):
                res = pa()
                if res:
                    return list(res)
        except Exception:
            if self.debug:
                print("_derive_opponent_actions: game.playable_actions() failed")
                traceback.print_exc()

        # As a conservative fallback, return empty list so we evaluate the post-action state directly
        return []

    def _safe_eval_base_fn(self, g: Game, color: Color) -> Optional[float]:
        """Safely call the adapters' base value function in its possible forms.

        Tries self._value_fn(g,color) if available; otherwise attempts base_fn()(g,color) and
        finally base_fn(g,color). Returns None on failure and logs when debug=True.
        """
        try:
            if self._value_fn is not None:
                return float(self._value_fn(g, color))
        except Exception:
            if self.debug:
                print("_safe_eval_base_fn: self._value_fn failed")
                traceback.print_exc()
        # Try factory form
        try:
            vf = base_fn()
            try:
                return float(vf(g, color))
            except Exception:
                if self.debug:
                    print("_safe_eval_base_fn: vf(g,color) failed")
                    traceback.print_exc()
        except Exception:
            # Maybe base_fn itself accepts (g,color)
            pass
        try:
            return float(base_fn(g, color))
        except Exception:
            if self.debug:
                print("_safe_eval_base_fn: all attempts to call base_fn failed")
                traceback.print_exc()
            return None

    def _simulate_and_evaluate(self, game: Game, action, my_color: Color) -> Optional[float]:
        """Simulate `action` from `game` and return a numeric expected score for my_color.

        If action is None, simply evaluate the provided game state.
        This function handles spectrum (chance) outcomes when available and falls back to
        deterministic execution. Returns None on failure for the given simulation.
        """
        # Copy the game to avoid mutating caller's state
        try:
            game_copy = copy_game(game)
        except Exception as e:
            if self.debug:
                print("_simulate_and_evaluate: copy_game failed:", e)
                traceback.print_exc()
            return None

        # If action is None, just evaluate the provided state
        if action is None:
            return self._safe_eval_base_fn(game_copy, my_color)

        # Chance-aware path
        if self._is_robber_or_chance(action):
            try:
                spec = None
                try:
                    spec = execute_spectrum(game_copy, action)
                except Exception:
                    # Try expand_spectrum single-action expansion
                    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)
                    if not outcomes:
                        # Fall through to deterministic
                        pass
                    else:
                        total_score = 0.0
                        for og, prob in outcomes:
                            sc = self._safe_eval_base_fn(og, my_color)
                            if sc is None:
                                # If any outcome can't be evaluated reliably, abort spectrum path
                                total_score = None
                                break
                            total_score += prob * sc
                        if total_score is None:
                            if self.debug:
                                print("_simulate_and_evaluate: spectrum had unscorable outcomes; falling back")
                        else:
                            return float(total_score)
            except Exception as e:
                if self.debug:
                    print("_simulate_and_evaluate: execute_spectrum/expand_spectrum failed:", e)
                    traceback.print_exc()
                # fall through to deterministic

        # Deterministic fallback
        try:
            outcomes = execute_deterministic(game_copy, action)
        except Exception as e:
            if self.debug:
                print("_simulate_and_evaluate: execute_deterministic failed:", e)
                traceback.print_exc()
            return None

        try:
            if not outcomes:
                if self.debug:
                    print("_simulate_and_evaluate: execute_deterministic returned no outcomes")
                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

        return self._safe_eval_base_fn(resultant_game, my_color)

    # ------------------ Decision method (public) ------------------
    def decide(self, game: Game, playable_actions: Iterable):
        """Choose an action using selective 2-ply adversarial lookahead.

        Flow:
        1) Run phase-aware 1-ply sampling and evaluation across candidates.
        2) Keep top TOP_K_1PLY candidates by 1-ply score and deepen each with opponent modeling.
        3) For each candidate, compute expected adversarial value = E_outcomes[min_opponent_response(score)].
        4) Pick candidate maximizing (expected_value, 1-ply vp_delta, repr action tie-break).

        All adapter calls are protected with try/except. On catastrophic failure we fall back to
        returning the best 1-ply candidate or the first playable action as a last resort.
        """
        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]

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

        one_ply_results: List[Tuple[Any, float, float]] = []  # (action, score, vp_delta)
        for idx, a in enumerate(candidates, start=1):
            res = self._evaluate_action(game, a, self.color)
            if self.debug:
                print(f"1-ply [{idx}/{len(candidates)}]: {repr(a)} -> {res}")
            if res is None:
                continue
            sc, vpd = res
            one_ply_results.append((a, float(sc), float(vpd)))

        if not one_ply_results:
            # Nothing evaluated successfully; fallback deterministically
            if self.debug:
                print("decide: no 1-ply evaluations succeeded; falling back to first playable action")
            return actions[0]

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

        if self.debug:
            print("Top 1-ply candidates:")
            for a, s, v in one_ply_results[: self.TOP_K_1PLY]:
                print(f"  candidate: {repr(a)} score={s} vp_delta={v}")

        # Stage 3: selective 2-ply adversarial evaluation
        best_action = None
        best_value = -float("inf")
        best_vp_delta = -float("inf")
        best_repr = None

        # Simulation budget guard: do not exceed too many simulated nodes
        sim_count = 0
        SIMULATION_HARD_LIMIT = 10000  # safety cap to protect time; adjustable

        try:
            for a in top_k:
                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: prefer spectrum for chance actions
                outcomes: List[Tuple[Game, float]] = []
                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)
                    # Fallback to deterministic
                    if not outcomes:
                        det = execute_deterministic(game_copy, a)
                        if not det:
                            if self.debug:
                                print("decide: execute_deterministic returned empty for", repr(a))
                            continue
                        # det is list of (game, prob) often; take as provided
                        # normalize shape defensively
                        normalized = []
                        for entry in det[: self.SPECTRUM_MAX_OUTCOMES]:
                            try:
                                g, p = entry
                            except Exception:
                                g = entry
                                p = 1.0
                            normalized.append((g, float(p)))
                        # If probabilities not summing to 1, normalize
                        total_p = sum(p for _, p in normalized)
                        if total_p <= 0:
                            # assign uniform
                            n = len(normalized)
                            outcomes = [(g, 1.0 / n) for (g, _) in normalized]
                        else:
                            outcomes = [(g, p / total_p) for (g, p) in normalized]

                except Exception as e:
                    if self.debug:
                        print("decide: failed to obtain outcomes for candidate", repr(a), "error:", e)
                        traceback.print_exc()
                    continue

                # Cap outcomes just in case
                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
                # find 1-ply vp delta for tie-break usage
                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 >= SIMULATION_HARD_LIMIT:
                        break
                    # Derive 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:
                        # No opponent actions: evaluate the post-my-action state directly
                        val_i = self._simulate_and_evaluate(og, None, self.color)
                        if val_i is None:
                            # Skip this outcome in expectation if it couldn't be evaluated
                            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 opponent: they choose the action minimizing our final score
                    min_score_after_opp = float("inf")
                    for b in opp_sampled:
                        if sim_count >= SIMULATION_HARD_LIMIT:
                            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"):
                        # If no opponent simulation succeeded, evaluate the post-my-action state
                        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 expected value using deterministic tie-break
                if self.debug:
                    print(f"Candidate {repr(a)} expected_value={expected_value_a} (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 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_vp_delta = one_ply_vp_delta
                    best_repr = repr(a)

            # End loop over top_k
        except Exception:
            if self.debug:
                print("decide: unexpected error during 2-ply deepening")
                traceback.print_exc()
            # Fall back to 1-ply selection below

        # If 2-ply produced a valid selection, return it
        if best_action is not None:
            if self.debug:
                print("decide: selected (2-ply) action:", repr(best_action), "value:", best_value)
            return best_action

        # Otherwise, fall back to best 1-ply action using existing tie-break rules
        if self.debug:
            print("decide: 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)
            return best_action_1ply

        # Last resort: return first playable action
        if self.debug:
            print("decide: no choice after fallbacks; returning first playable action")
        return actions[0]
================================= Tool Message =================================
Name: write_foo

foo_player.py updated successfully
================================== Ai Message ==================================

CODER

I have updated foo_player.py implementing the selective 2-ply adversarial lookahead per the Strategizer plan. Summary of what I changed and added:

- Added new class defaults:
  - TOP_K_1PLY = 12
  - OP_MAX_ACTIONS = 20
  - OP_SAMPLE_PER_ACTION_TYPE = 2
  - MAX_ACTIONS_TO_EVAL = 60 (kept)
  - SAMPLE_PER_ACTION_TYPE = 3 (kept)
  - SPECTRUM_MAX_OUTCOMES = 8 (kept)
  - EARLY_TURN_THRESHOLD = 30 (kept)
  - RNG_SEED = 0 (kept)
  - TOP_K_DEEP = 0 (reserved)

- Added helper methods:
  - _stable_color_hash(self, color): deterministic hash used for RNG seeding.
  - _sample_opponent_actions(self, playable_actions, game, opponent_color): opponent-specific deterministic sampling.
  - _normalize_and_cap_spectrum(self, spectrum, cap): normalize/renormalize spectrum entries, defensively.
  - _determine_opponent_color(self, game, my_color): try to find opponent color; fallback to other Color.
  - _derive_opponent_actions(self, game, opponent_color): uses adapters.list_prunned_actions(game), game.playable_actions(), or returns empty list conservatively.
  - _safe_eval_base_fn(self, g, color): robustly call base_fn in several possible forms, with debug logging on failure.
  - _simulate_and_evaluate(self, game, action, my_color): simulate an action (spectrum-aware) and return numeric expected score or None.

- Decision changes:
  - The decide method now:
    - Performs 1-ply sampling/evaluation (existing behavior).
    - Selects top TOP_K_1PLY candidates by 1-ply score.
    - For each top candidate, simulates outcome branches (spectrum or deterministic), derives opponent actions, samples/prunes them, simulates opponent responses, and computes an adversarial expected value (E_outcomes[min_opponent_response(score)]).
    - Selects the action maximizing (expected_value, 1-ply vp_delta, repr tie-break), deterministically.
    - Uses simulation budget guards (SIMULATION_HARD_LIMIT) to avoid runaway runtime.
    - Falls back to best 1-ply action or the first playable action if 2-ply deepening fails.

- Debugging:
  - Controlled by self.debug (default False). When True, prints detailed traces including per-candidate outcomes and exceptions.

- Adapter usage:
  - Uses only adapters functions (copy_game, execute_deterministic, execute_spectrum, expand_spectrum, base_fn, list_prunned_actions).
  - All adapter calls are wrapped in try/except; on exceptions the code logs (when debug=True) and falls back safely.

- Notes / Observations:
  - The code stays deterministic by seeding RNG with RNG_SEED + stable color hash.
  - The simulation hard cap is set to 10000 nodes. If runtime issues occur, I recommend decreasing TOP_K_1PLY or OP_MAX_ACTIONS.
  - If any adapter signature mismatch or missing function appears during runs, the code will catch exceptions, log traces (when debug=True), and return safe fallbacks. Provide such traces if you want me to call RESEARCHER.

Next steps you requested earlier:
- I can now run the 30-game experiment vs AlphaBetaPlayer and report the JSON metrics. Please confirm whether to run with debug=False (clean runs) or debug=True for a few sample games to capture decision traces. If you choose debug=True, I will run 3 games to produce decision traces as requested.

END CODER

