--- Input for CODER at 20250925_041931 ---
================================ Human Message =================================

META THOUGHTS: The strategizer has provided an excellent algorithmic solution - a multi-step lookahead strategy using copy_game, execute_deterministic, and base_fn to simulate and evaluate actions. This directly addresses the core flaw identified by the analyzer (always picking first action) and provides a concrete implementation approach with code snippets. This should significantly improve performance against the AlphaBeta opponent by making informed decisions based on simulated future states.

META GOAL: Implement the multi-step lookahead strategy with expected value estimation to replace the naive "pick first action" approach and achieve meaningful wins against the AlphaBeta opponent.

CHOSEN AGENT: CODER

AGENT OBJECTIVE: Implement the multi-step lookahead strategy proposed by the strategizer. Replace the current naive "pick first action" logic in foo_player.py with the following components: 1) Import copy_game, execute_deterministic, and base_fn from adapters.py, 2) Initialize the value_fn as base_fn in the constructor, 3) Implement the decide method to simulate each playable action using copy_game and execute_deterministic, 4) Evaluate each resulting state using base_fn heuristic, 5) Select and return the action with the highest evaluation score, 6) Include fallback to first action if no best action is found. Use the code snippet provided by the strategizer as your foundation and ensure proper error handling and debugging output.
================================== Ai Message ==================================

CODER

Changes made to foo_player.py:
- Rewrote the entire foo_player.py to implement a lookahead-based action selection using the adapters API.
- Added required adapter imports and used only the adapter surface (no direct catanatron imports):
  - from .adapters import Game, Player, Color, copy_game, execute_deterministic, list_prunned_actions, base_fn
- Created a FooPlayer class that:
  - Initializes a heuristic value function via base_fn() and stores a configurable lookahead_depth (default 2).
  - Tracks node expansions for basic profiling/debugging (_node_expansions).
- Replaced the naive decide() implementation with a lookahead evaluator:
  - decide() converts playable_actions to a list, simulates each action with copy_game + execute_deterministic, computes expected values over deterministic outcomes, and picks the action with highest expected score.
  - Added robust try/except blocks to avoid crashing on unexpected adapter failures.
  - Prints informative debugging statements showing number of actions evaluated, expected values per action, chosen action, score, and node expansions.
- Added a recursive helper method _evaluate_node(game, depth):
  - Implements a shallow minimax-like recursion: maximizes when the current actor equals this player's color, minimizes otherwise.
  - Uses list_prunned_actions() to reduce branching and execute_deterministic() to obtain deterministic outcomes to recurse over.
  - Uses the heuristic self.value_fn(game, self.color) at depth == 0 or as a fallback.
  - Includes defensive error handling and fallbacks to heuristic evaluation if list_prunned_actions or other parts fail.

New functions created:
- FooPlayer._evaluate_node(self, game: Game, depth: int) -> float

Debugging additions:
- Multiple print() statements throughout decide() and _evaluate_node() to log progress, exceptions, expected values, chosen action, and node expansion counts.
- A node counter self._node_expansions that increments each time a node is evaluated (helpful for tuning lookahead depth).

Safety / correctness notes:
- The code strictly uses .adapters functions (copy_game, execute_deterministic, list_prunned_actions, base_fn) as required.
- No direct imports from catanatron were added.
- The code uses Python 3.11-compatible syntax (including union-type annotation str | None). If your environment enforces an older typing style, I can revert to Optional[str].
- The lookahead is intentionally shallow (default depth=2) to balance compute vs. benefit. You can tune FooPlayer(…, lookahead_depth=N) to experiment.

Potential improvements / next objectives:
- Replace execute_deterministic with execute_spectrum or expand_spectrum when modeling chance nodes (dice, development draws) for a more accurate expected-value calculation.
- Add caching (transposition table) to avoid recomputing values for identical game states encountered during lookahead.
- Use prune_robber_actions to reduce branching on robber moves when appropriate.
- Limit branching further by selecting top-k pruned actions based on a quick heuristic evaluation before full recursion.
- Consider iterative deepening or time-limited search if compute/time constraints exist.
- Optionally expose the lookahead_depth as a constructor param in external agent creation when running tournaments.

Questions / things I'd like clarified for future iterations:
- Confirm whether union type annotations (str | None) are acceptable; otherwise I'll switch to Optional[str].
- Do you want the player to use execute_spectrum or expand_spectrum to better model chance nodes in the next version?
- Any strict time/performance limits per decide() call? This affects acceptable lookahead depth.

I added plenty of comments and safe fallbacks so the player should not crash if adapters throw exceptions; instead it will fall back to heuristic evaluation.

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

CODER

Changes made to foo_player.py:
- Rewrote the entire foo_player.py to implement a lookahead-based action selection using the adapters API.
- Added required adapter imports and used only the adapter surface (no direct catanatron imports):
  - from .adapters import Game, Player, Color, copy_game, execute_deterministic, list_prunned_actions, base_fn
- Created a FooPlayer class that:
  - Initializes a heuristic value function via base_fn() and stores a configurable lookahead_depth (default 2).
  - Tracks node expansions for basic profiling/debugging (_node_expansions).
- Replaced the naive decide() implementation with a lookahead evaluator:
  - decide() converts playable_actions to a list, simulates each action with copy_game + execute_deterministic, computes expected values over deterministic outcomes, and picks the action with highest expected score.
  - Added robust try/except blocks to avoid crashing on unexpected adapter failures.
  - Prints informative debugging statements showing number of actions evaluated, expected values per action, chosen action, score, and node expansions.
- Added a recursive helper method _evaluate_node(game, depth):
  - Implements a shallow minimax-like recursion: maximizes when the current actor equals this player's color, minimizes otherwise.
  - Uses list_prunned_actions() to reduce branching and execute_deterministic() to obtain deterministic outcomes to recurse over.
  - Uses the heuristic self.value_fn(game, self.color) at depth == 0 or as a fallback.
  - Includes defensive error handling and fallbacks to heuristic evaluation if list_prunned_actions or other parts fail.

New functions created:
- FooPlayer._evaluate_node(self, game: Game, depth: int) -> float

Debugging additions:
- Multiple print() statements throughout decide() and _evaluate_node() to log progress, exceptions, expected values, chosen action, and node expansion counts.
- A node counter self._node_expansions that increments each time a node is evaluated (helpful for tuning lookahead depth).

Safety / correctness notes:
- The code strictly uses .adapters functions (copy_game, execute_deterministic, list_prunned_actions, base_fn) as required.
- No direct imports from catanatron were added.
- The code uses Python 3.11-compatible syntax (including union-type annotation str | None). If your environment enforces an older typing style, I can revert to Optional[str].
- The lookahead is intentionally shallow (default depth=2) to balance compute vs. benefit. You can tune FooPlayer(…, lookahead_depth=N) to experiment.

Potential improvements / next objectives:
- Replace execute_deterministic with execute_spectrum or expand_spectrum when modeling chance nodes (dice, development draws) for a more accurate expected-value calculation.
- Add caching (transposition table) to avoid recomputing values for identical game states encountered during lookahead.
- Use prune_robber_actions to reduce branching on robber moves when appropriate.
- Limit branching further by selecting top-k pruned actions based on a quick heuristic evaluation before full recursion.
- Consider iterative deepening or time-limited search if compute/time constraints exist.
- Optionally expose the lookahead_depth as a constructor param in external agent creation when running tournaments.

Questions / things I'd like clarified for future iterations:
- Confirm whether union type annotations (str | None) are acceptable; otherwise I'll switch to Optional[str].
- Do you want the player to use execute_spectrum or expand_spectrum to better model chance nodes in the next version?
- Any strict time/performance limits per decide() call? This affects acceptable lookahead depth.

I added plenty of comments and safe fallbacks so the player should not crash if adapters throw exceptions; instead it will fall back to heuristic evaluation.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 19,
      "AVG VP": 8.533333333333333,
      "AVG SETTLES": 2.7,
      "AVG CITIES": 2.3666666666666667,
      "AVG ROAD": 0.5333333333333333,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.03333333333333333
    },
    "FooPlayer:BLUE": {
      "WINS": 11,
      "AVG VP": 7.1,
      "AVG SETTLES": 2.2666666666666666,
      "AVG CITIES": 1.9333333333333333,
      "AVG ROAD": 0.4666666666666667,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.03333333333333333
    }
  },
  "Game Summary": {
    "AVG TICKS": 221.6,
    "AVG TURNS": 76.46666666666667,
    "AVG DURATION": 2.307358646392822
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (11 wins, 19 losses).
- **VP Comparison**: FooPlayer (7.1 VP) vs. Opponent (8.5 VP), **VP Diff: -1.4**.
- **Key Counts**:
  - Settlements: 2.3
  - Cities: 1.9
  - Roads: 0.5
  - Dev Cards: 0.0
  - Total Turns: 76.5

---
### **VERDICT**
**Borderline**: FooPlayer won 11/30 games but lost with a VP deficit of -1.4. The player shows improvement but still underperforms in infrastructure and long-term strategy.

---
### **LIKELY REASONS**
1. **Shallow Lookahead with Limited Depth**:
   - The current implementation uses a **depth-2 lookahead**, which is insufficient for long-term planning (e.g., road/settlement expansion, resource monopolies).
   - **Log Evidence**: Actions are evaluated with `lookahead_depth=2` (e.g., `"FooPlayer.decide: Evaluating 5 actions with lookahead depth=2"`).

2. **Over-Reliance on Maritime Trades**:
   - The player frequently selects `MARITIME_TRADE` actions, even when higher-value actions like `BUILD_SETTLEMENT` or `BUILD_ROAD` are available.
   - **Log Evidence**: Repeated lines like:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', None, None, 'WOOD')) score=3000000047247289.5
     ```
   - This suggests the **value function (`base_fn`)** may overvalue short-term resource gains over long-term infrastructure.

3. **No Robber/Knight Strategy**:
   - The player does not prioritize `MOVE_ROBBER` or `PLAY_KNIGHT` actions, missing opportunities to disrupt opponents or secure resources.
   - **Log Evidence**: Only one `MOVE_ROBBER` action in the entire log, chosen late in the game:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((2, -2, 0), <Color.RED: 'RED'>, None)) score=2700000233358445.5
     ```

4. **Inefficient Node Expansion**:
   - The player evaluates **all actions equally**, leading to excessive node expansions (e.g., 570 expansions for a single `BUILD_SETTLEMENT` decision).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_SETTLEMENT: 'BUILD_SETTLEMENT'>, value=37) score=3000000047247301.0 node_expansions=570
     ```

---
### **STRATEGIC FLAW**
The player's **depth-2 lookahead** and **over-reliance on short-term resource trades** prevent it from developing a cohesive long-term strategy for infrastructure and victory points.

---
### **NEXT STEP**
Send to **Strategizer** to **increase lookahead depth to 3+** and **tune the value function (`base_fn`)** to prioritize settlements, roads, and robber/knight actions over maritime trades.

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

META THOUGHTS: Excellent progress! We jumped from 0 wins to 11 wins (37% win rate) and 2.0 VP to 7.1 VP, proving our lookahead strategy is fundamentally sound. The analyzer identified specific refinements needed: deeper lookahead (depth 3+), better action prioritization to reduce computational waste, and rebalancing the evaluation to favor infrastructure over maritime trades. This is very promising - we have a working algorithmic foundation that just needs optimization.

META GOAL: Refine the existing lookahead strategy by increasing depth, improving action prioritization, and rebalancing evaluation priorities to favor long-term infrastructure building over short-term resource trades, aiming to achieve 50%+ win rate.

CHOSEN AGENT: STRATEGIZER

AGENT OBJECTIVE: Our lookahead strategy is working well - we improved from 0 wins to 11 wins (37% win rate) and 2.0 to 7.1 avg VP. However, the analyzer identified key refinements needed: 1) Current depth-2 lookahead is too shallow for long-term planning, 2) Player over-prioritizes maritime trades vs infrastructure building, 3) No robber/knight strategy, 4) Inefficient node expansion (570 expansions per decision). Design specific improvements to our existing lookahead algorithm that address these issues. Focus on: increasing lookahead depth to 3+, implementing better action prioritization/pruning to reduce computational waste, and adjusting the evaluation strategy to favor settlements/roads/robber actions over maritime trades. Build on our successful foundation rather than changing the core approach.
================================ Human Message =================================

STRATEGIZER
**Refined Strategy: Deepened Lookahead with Infrastructure Focus and Robber Optimization**

### Key Improvements:
1. **Increase Lookahead Depth to 3**:
   - Capture longer-term consequences of infrastructure decisions (settlements, roads, cities).
   - Adjust `lookahead_depth` in `FooPlayer.__init__` to 3.

2. **Action Prioritization with Pruning**:
   - Use `prune_robber_actions` to reduce computational waste and focus on high-impact actions.
   - Prioritize actions like `BUILD_SETTLEMENT`, `BUILD_ROAD`, and `BUILD_CITY` over maritime trades.

3. **Rebalance Evaluation with `contender_fn`**:
   - Replace `base_fn` with `contender_fn` to emphasize infrastructure and long-term growth.
   - Customize weights to further prioritize settlements, roads, and development cards.

4. **Robber/Knight Strategy**:
   - Add logic to prioritize robber placement actions that target opponents with high resource production or development card advantages.
   - Use `prune_robber_actions` to focus on disruptive robber placements.

---

### Implementation Steps:
1. **Update `FooPlayer.__init__`**:
   ```python
   def __init__(self, name: str | None = None, lookahead_depth: int = 3):
       super().__init__(Color.BLUE, name)
       self.value_fn = contender_fn()  # Use contender_fn for infrastructure focus
       self.lookahead_depth = max(1, int(lookahead_depth))
   ```

2. **Modify `decide` to Prioritize High-Impact Actions**:
   ```python
   def decide(self, game: Game, playable_actions: Iterable) -> object:
       actions = list(playable_actions)
       if not actions:
           return None

       # Prune robber actions to reduce computational waste
       if any(action.action_type == ActionType.PLACE_ROBBER for action in actions):
           actions = prune_robber_actions(self.color, game, actions)

       # Prioritize infrastructure actions
       infrastructure_actions = [
           action for action in actions
           if action.action_type in {ActionType.BUILD_SETTLEMENT, ActionType.BUILD_ROAD, ActionType.BUILD_CITY}
       ]
       if infrastructure_actions:
           actions = infrastructure_actions

       # Proceed with lookahead evaluation
       best_action, best_score = self._evaluate_actions(game, actions)
       return best_action if best_action else actions[0]
   ```

3. **Adjust `_evaluate_node` for Robber Strategy**:
   - In `_evaluate_node`, explicitly evaluate robber actions to target opponents with high resource production.

4. **Optimize Node Expansion**:
   - Limit node expansions by pruning less impactful actions early in the evaluation process.

---

### Expected Outcome:
- **Higher Win Rate**: Deeper lookahead and infrastructure focus should increase win rate to 50%+.
- **Efficiency**: Pruning and prioritization will reduce node expansions, improving computational efficiency.
- **Strategic Depth**: Robber/knight strategy will disrupt opponents and create opportunities for resource dominance.

**Next Steps**: Implement these changes in `foo_player.py` and test performance.
END STRATEGIZER
================================== Ai Message ==================================

META THOUGHTS: The strategizer provided excellent targeted improvements to our successful lookahead foundation. The plan addresses all analyzer concerns: deeper lookahead (depth 3), better evaluation function (contender_fn vs base_fn), computational efficiency (pruning), action prioritization (infrastructure focus), and robber strategy. These refinements should build on our 37% win rate success while fixing the over-reliance on maritime trades and shallow planning issues.

META GOAL: Refine the existing lookahead strategy by implementing depth 3 lookahead, switching to contender_fn, adding action prioritization and pruning, and incorporating robber strategy to achieve 50%+ win rate against AlphaBeta opponent.

CHOSEN AGENT: CODER

AGENT OBJECTIVE: Implement the strategizer's refinements to our successful lookahead algorithm. Make these specific changes to foo_player.py: 1) Increase lookahead_depth default from 2 to 3 in constructor, 2) Replace base_fn() with contender_fn() for better infrastructure prioritization, 3) Import and use prune_robber_actions to reduce computational waste, 4) Add action prioritization logic in decide() to favor infrastructure actions (BUILD_SETTLEMENT, BUILD_ROAD, BUILD_CITY) over maritime trades, 5) Implement robber action pruning when PLACE_ROBBER actions are present, 6) Keep all existing debugging output and error handling. Build on the current working foundation rather than rewriting from scratch.
================================ 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 os
from typing import Iterable, List, Tuple

# MUST use adapters to interact with the game. Do NOT import catanatron directly.
from .adapters import (
    Game,
    Player,
    Color,
    copy_game,
    execute_deterministic,
    list_prunned_actions,
    base_fn,
)


class FooPlayer(Player):
    """A simple lookahead player that uses the adapters API to simulate
    actions and evaluate resulting states using a heuristic value function.

    Strategy implemented:
    - For each playable action, simulate the deterministic outcomes using
      execute_deterministic on a copied game state.
    - Recursively perform a shallow minimax-style lookahead with alternating
      players: maximize for this player, minimize for the opponent.
    - Use base_fn() from adapters as the heuristic evaluator at leaf nodes.

    Notes / learning points included as comments and print debugging to help
    evolve the player in subsequent iterations.
    """

    def __init__(self, name: str | None = None, lookahead_depth: int = 2):
        # Initialize as BLUE (same as previous implementation). The Player
        # constructor from adapters expects (Color, name)
        super().__init__(Color.BLUE, name)
        # Create a value function instance using the adapters' base_fn factory.
        # base_fn returns a callable f(game, color) -> float.
        self.value_fn = base_fn()

        # Lookahead depth controls the recursion depth for the minimax.
        # Depth 1 evaluates immediate resulting states; depth 2 looks one
        # opponent response deeper, etc. Keep small to limit compute.
        self.lookahead_depth = max(1, int(lookahead_depth))

        # Counters / debug info to monitor node expansions in a single decision.
        self._node_expansions = 0

    def decide(self, game: Game, playable_actions: Iterable) -> object:
        """Choose an action from playable_actions using a shallow lookahead.

        Args:
            game (Game): complete game state (read-only). Must use copy_game
                         to create simulations of this state.
            playable_actions (Iterable[Action]): available actions for the
                         current game state.
        Returns:
            An Action from playable_actions.
        """
        try:
            actions = list(playable_actions)
        except Exception:
            # playable_actions could be any iterable; ensure we can index it.
            actions = [a for a in playable_actions]

        # Defensive: if there are no actions, return None (game should handle it)
        if not actions:
            print('FooPlayer.decide: No playable actions available, returning None')
            return None

        # Reset debug counters
        self._node_expansions = 0

        best_action = None
        best_score = float('-inf')

        print(f'FooPlayer.decide: Evaluating {len(actions)} actions with lookahead depth={self.lookahead_depth}')

        # Evaluate each candidate action by simulating its deterministic outcomes
        for idx, action in enumerate(actions):
            try:
                # copy the game and execute the action deterministically
                game_copy = copy_game(game)
                outcomes = execute_deterministic(game_copy, action)

                # outcomes is a list of (game_after_action, probability) tuples
                # For deterministic execution this will typically be one element
                # with probability 1. We'll compute the expected value across
                # all outcomes.
                expected_value = 0.0
                for (outcome_game, prob) in outcomes:
                    # For each outcome, perform a recursive lookahead of depth-1
                    node_value = self._evaluate_node(outcome_game, self.lookahead_depth - 1)
                    expected_value += prob * node_value

                print(f'  Action {idx}: expected_value={expected_value}')

                # Since these actions are available to the current player, we
                # select the action with the highest expected value.
                if expected_value > best_score:
                    best_score = expected_value
                    best_action = action

            except Exception as e:
                # Catch exceptions per-action to avoid crashing during decide.
                print(f'FooPlayer.decide: Exception while evaluating action {action}: {e}')

        # Fallback to the first action if something went wrong and no best_action
        chosen = best_action if best_action is not None else actions[0]
        print(f'FooPlayer.decide: Chosen action={chosen} score={best_score} node_expansions={self._node_expansions}')
        return chosen

    def _evaluate_node(self, game: Game, depth: int) -> float:
        """Recursive evaluator that returns a heuristic value for the given game
        state with a remaining lookahead depth.

        Implementation details:
        - If depth == 0, evaluate the state with self.value_fn(game, self.color).
        - Otherwise, list pruned actions for the current game state using the
          adapters' list_prunned_actions(). For each action, simulate
          deterministic outcomes and compute the expected value recursively.
        - If the actions belong to this player (action.color == self.color), we
          take the maximum over actions. If they belong to the opponent, we
          take the minimum (adversarial assumption).

        This is a shallow minimax with deterministic expansions. Chance nodes
        (dice, dev draws) are respected by execute_deterministic / execute_spectrum
        when used; here we only call execute_deterministic for speed and
        simplicity. Future iterations could expand chance outcomes explicitly.
        """
        # Update expansion counter for debugging / profiling
        self._node_expansions += 1

        # Base case: evaluate with heuristic
        if depth <= 0:
            try:
                val = float(self.value_fn(game, self.color))
            except Exception as e:
                print(f'FooPlayer._evaluate_node: value_fn raised exception: {e}')
                val = 0.0
            return val

        # Get a pruned list of actions for this game state to reduce branching.
        try:
            actions = list_prunned_actions(game)
        except Exception as e:
            print(f'FooPlayer._evaluate_node: list_prunned_actions failed: {e}')
            # Fallback: evaluate directly
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        if not actions:
            # No actions -> evaluate terminal-like state
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        # Determine whether current player is us or the opponent by inspecting
        # the first action's color. All returned actions should be for the same
        # player (the current player in the provided game state).
        current_actor_color = actions[0].color
        is_maximizing = (current_actor_color == self.color)

        # Evaluate each action to compute either the max or min expected value.
        best_value = float('-inf') if is_maximizing else float('inf')

        for action in actions:
            try:
                game_copy = copy_game(game)
                outcomes = execute_deterministic(game_copy, action)

                expected = 0.0
                for (outcome_game, prob) in outcomes:
                    expected += prob * self._evaluate_node(outcome_game, depth - 1)

                if is_maximizing:
                    if expected > best_value:
                        best_value = expected
                else:
                    if expected < best_value:
                        best_value = expected

            except Exception as e:
                print(f'FooPlayer._evaluate_node: Exception on action {action}: {e}')

        # If something went wrong and best_value remains inf/-inf, evaluate directly
        if best_value == float('inf') or best_value == float('-inf'):
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        return best_value

================================ 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_042045 ---
================================== Ai Message ==================================
Tool Calls:
  write_foo (call_xu7hRh4Go6mzVV6CBAq82RYN)
 Call ID: call_xu7hRh4Go6mzVV6CBAq82RYN
  Args:
    new_text: import os
from typing import Iterable, List, Tuple

# MUST use adapters to interact with the game. Do NOT import catanatron directly.
from .adapters import (
    Game,
    Player,
    Color,
    copy_game,
    execute_deterministic,
    list_prunned_actions,
    prune_robber_actions,
    contender_fn,
    base_fn,
    DEFAULT_WEIGHTS,
    ActionType,
)


class FooPlayer(Player):
    """A lookahead player that focuses on infrastructure and robber pruning.

    Improvements over the previous version:
    - Default lookahead depth increased to 3 to capture longer-term infrastructure
      consequences (settlements, roads, cities).
    - Uses contender_fn (with DEFAULT_WEIGHTS) as the primary heuristic to bias
      evaluation toward infrastructure. Falls back to base_fn on failure.
    - Uses prune_robber_actions to reduce robber move branching when robber
      actions are available.
    - Prioritizes infrastructure actions (BUILD_SETTLEMENT, BUILD_ROAD,
      BUILD_CITY) over maritime trades when possible.

    Implementation notes / reminders:
    - Always interact with the game through the .adapters surface.
    - Keep print() debugging statements to trace decisions and node expansions.
    - Defensive try/excepts ensure we fall back to a heuristic evaluation if any
      adapter call fails instead of crashing the whole player.
    """

    def __init__(self, name: str | None = None, lookahead_depth: int = 3):
        # Initialize as BLUE (same as previous implementation). The Player
        # constructor from adapters expects (Color, name)
        super().__init__(Color.BLUE, name)

        # Prefer contender_fn to bias toward infrastructure. contender_fn in the
        # adapters expects parameters; use DEFAULT_WEIGHTS as a sensible default.
        try:
            self.value_fn = contender_fn(DEFAULT_WEIGHTS)
            print('FooPlayer.__init__: Using contender_fn with DEFAULT_WEIGHTS')
        except Exception as e:
            # If contender_fn fails for any reason, fall back to base_fn.
            print(f'FooPlayer.__init__: contender_fn failed, falling back to base_fn: {e}')
            try:
                self.value_fn = base_fn()
            except Exception as inner:
                print(f'FooPlayer.__init__: base_fn also failed, using dumb fallback. {inner}')
                # Final fallback: a lambda that returns 0.0 so code remains safe.
                self.value_fn = lambda g, c: 0.0

        # Lookahead depth controls recursion. Increase default to 3 for deeper
        # planning. Keep lower bound of 1 to avoid invalid depths.
        self.lookahead_depth = max(1, int(lookahead_depth))

        # Counters / debug info to monitor node expansions in a single decision.
        self._node_expansions = 0

    def decide(self, game: Game, playable_actions: Iterable) -> object:
        """Choose an action from playable_actions using a prioritized lookahead.

        Strategy enhancements from previous version:
        - If robber actions are present, use prune_robber_actions to keep only
          impactful robber placements.
        - Prioritize infrastructure actions (settlement/road/city) over
          maritime trades to encourage long-term VP growth.
        - Evaluate a reduced set of actions with lookahead to limit node
          expansions and computation time.
        """
        try:
            actions = list(playable_actions)
        except Exception:
            # playable_actions could be any iterable; ensure we can index it.
            actions = [a for a in playable_actions]

        # Defensive: if there are no actions, return None (game should handle it)
        if not actions:
            print('FooPlayer.decide: No playable actions available, returning None')
            return None

        # Reset debug counters
        self._node_expansions = 0

        # If there are robber actions present, prune them to reduce branching.
        try:
            has_robber = any(getattr(a, 'action_type', None) and 'ROBBER' in getattr(a.action_type, 'name', '') for a in actions)
        except Exception:
            has_robber = False

        if has_robber:
            try:
                pruned = prune_robber_actions(self.color, game, actions)
                if pruned:
                    print(f'FooPlayer.decide: Pruned robber actions from {len(actions)} to {len(pruned)}')
                    actions = pruned
            except Exception as e:
                print(f'FooPlayer.decide: prune_robber_actions failed: {e}')

        # Prioritize infrastructure actions over maritime trades and other low
        # value actions. If we have any infrastructure actions, focus on them.
        try:
            infrastructure_types = {ActionType.BUILD_SETTLEMENT, ActionType.BUILD_ROAD, ActionType.BUILD_CITY}
            infrastructure_actions = [a for a in actions if getattr(a, 'action_type', None) in infrastructure_types]
            if infrastructure_actions:
                print(f'FooPlayer.decide: Prioritizing {len(infrastructure_actions)} infrastructure actions over {len(actions)} total')
                actions = infrastructure_actions
            else:
                # If no infrastructure actions, try to deprioritize maritime trades
                # when there are many options (to avoid repeatedly choosing trades).
                if len(actions) > 6:
                    non_trade_actions = [a for a in actions if getattr(a, 'action_type', None) != ActionType.MARITIME_TRADE]
                    if non_trade_actions:
                        print(f'FooPlayer.decide: Filtering out maritime trades from {len(actions)} to {len(non_trade_actions)} actions')
                        actions = non_trade_actions
        except Exception as e:
            print(f'FooPlayer.decide: Error during action prioritization: {e}')

        best_action = None
        best_score = float('-inf')

        print(f'FooPlayer.decide: Evaluating {len(actions)} actions with lookahead depth={self.lookahead_depth}')

        # Evaluate each candidate action by simulating its deterministic outcomes
        for idx, action in enumerate(actions):
            try:
                # copy the game and execute the action deterministically
                game_copy = copy_game(game)
                outcomes = execute_deterministic(game_copy, action)

                # outcomes is a list of (game_after_action, probability) tuples
                expected_value = 0.0
                for (outcome_game, prob) in outcomes:
                    # For each outcome, perform a recursive lookahead of depth-1
                    node_value = self._evaluate_node(outcome_game, self.lookahead_depth - 1)
                    expected_value += prob * node_value

                print(f'  Action {idx}: expected_value={expected_value} action_type={getattr(action, "action_type", None)}')

                # Since these actions are available to the current player, we
                # select the action with the highest expected value.
                if expected_value > best_score:
                    best_score = expected_value
                    best_action = action

            except Exception as e:
                # Catch exceptions per-action to avoid crashing during decide.
                print(f'FooPlayer.decide: Exception while evaluating action {action}: {e}')

        # Fallback to the first action if something went wrong and no best_action
        chosen = best_action if best_action is not None else actions[0]
        print(f'FooPlayer.decide: Chosen action={chosen} score={best_score} node_expansions={self._node_expansions}')
        return chosen

    def _evaluate_node(self, game: Game, depth: int) -> float:
        """Recursive evaluator that returns a heuristic value for the given game
        state with a remaining lookahead depth.

        Enhancements:
        - When robber actions are present for the current actor, use
          prune_robber_actions to reduce branching and focus on impactful
          robber placements.
        - When many actions exist, deprioritize maritime trades to limit
          expansion.
        """
        # Update expansion counter for debugging / profiling
        self._node_expansions += 1

        # Base case: evaluate with heuristic
        if depth <= 0:
            try:
                val = float(self.value_fn(game, self.color))
            except Exception as e:
                print(f'FooPlayer._evaluate_node: value_fn raised exception: {e}')
                val = 0.0
            return val

        # Get a pruned list of actions for this game state to reduce branching.
        try:
            actions = list_prunned_actions(game)
        except Exception as e:
            print(f'FooPlayer._evaluate_node: list_prunned_actions failed: {e}')
            # Fallback: evaluate directly
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        if not actions:
            # No actions -> evaluate terminal-like state
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        # If robber actions are present for the current actor, prune them.
        try:
            has_robber = any(getattr(a, 'action_type', None) and 'ROBBER' in getattr(a.action_type, 'name', '') for a in actions)
        except Exception:
            has_robber = False

        if has_robber:
            try:
                # Use the color of the current actor to prune appropriately.
                current_color = actions[0].color
                pruned = prune_robber_actions(current_color, game, actions)
                if pruned:
                    actions = pruned
            except Exception as e:
                print(f'FooPlayer._evaluate_node: prune_robber_actions failed: {e}')

        # If there are many actions, deprioritize maritime trades to lower
        # branching factor. Keep trades only if no other options exist.
        try:
            if len(actions) > 8:
                non_trade_actions = [a for a in actions if getattr(a, 'action_type', None) != ActionType.MARITIME_TRADE]
                if non_trade_actions:
                    actions = non_trade_actions
        except Exception as e:
            print(f'FooPlayer._evaluate_node: Error filtering maritime trades: {e}')

        # Determine whether current player is us or the opponent by inspecting
        # the first action's color. All returned actions should be for the same
        # player (the current player in the provided game state).
        current_actor_color = actions[0].color
        is_maximizing = (current_actor_color == self.color)

        # Evaluate each action to compute either the max or min expected value.
        best_value = float('-inf') if is_maximizing else float('inf')

        for action in actions:
            try:
                game_copy = copy_game(game)
                outcomes = execute_deterministic(game_copy, action)

                expected = 0.0
                for (outcome_game, prob) in outcomes:
                    expected += prob * self._evaluate_node(outcome_game, depth - 1)

                if is_maximizing:
                    if expected > best_value:
                        best_value = expected
                else:
                    if expected < best_value:
                        best_value = expected

            except Exception as e:
                print(f'FooPlayer._evaluate_node: Exception on action {action}: {e}')

        # If something went wrong and best_value remains inf/-inf, evaluate directly
        if best_value == float('inf') or best_value == float('-inf'):
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        return best_value
================================= Tool Message =================================
Name: write_foo

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

CODER

Summary of changes made to foo_player.py
- Rewrote and updated FooPlayer to implement the Strategizer recommendations:
  - Increased default lookahead depth from 2 to 3.
  - Switched from base_fn to contender_fn (with DEFAULT_WEIGHTS) to bias evaluation toward infrastructure (settlements/roads/cities).
  - Added pruning of robber actions using prune_robber_actions when robber actions are present.
  - Added prioritization to favor infrastructure actions (BUILD_SETTLEMENT, BUILD_ROAD, BUILD_CITY). If no infrastructure actions exist, the code attempts to deprioritize maritime trades when many actions exist.
  - Kept the existing lookahead/minimax recursion but added action pruning and maritime-trade filtering to reduce node expansion.
  - Maintained defensive error handling and fallbacks to avoid crashes.

Files / imports changed
- foo_player.py now imports the following from .adapters:
  - Game, Player, Color, copy_game, execute_deterministic, list_prunned_actions, prune_robber_actions, contender_fn, base_fn, DEFAULT_WEIGHTS, ActionType
- All interactions with the game are via the adapters API (no direct catanatron imports).

New/modified behavior in FooPlayer
- __init__:
  - Default lookahead_depth set to 3.
  - Primary heuristic: attempt contender_fn(DEFAULT_WEIGHTS). If this fails, fallback to base_fn(); if that fails, fallback to a zero-valued lambda so the agent never crashes.
  - Debug print indicating what heuristic was chosen.

- decide:
  - Converts playable_actions to a list (defensive).
  - Detects presence of robber actions and prunes them with prune_robber_actions to reduce branching.
  - Prioritizes infrastructure actions (BUILD_SETTLEMENT, BUILD_ROAD, BUILD_CITY).
  - If many options and no infrastructure, attempts to filter out MARITIME_TRADE actions to avoid over-reliance on trades.
  - Runs lookahead evaluation across the reduced/prioritized action list, using execute_deterministic + recursive evaluation to compute expected values.
  - Debug prints per-action expected values and final chosen action with node expansion count.
  - Fallback: If no best action determined, returns the first remaining action.

- _evaluate_node:
  - Maintains node expansion counter (self._node_expansions).
  - Base case uses self.value_fn(game, self.color).
  - Uses list_prunned_actions(game) to obtain candidate actions for the current node.
  - Prunes robber actions when present using prune_robber_actions.
  - When branching factor is large (>8), tries to filter out MARITIME_TRADE actions.
  - Performs deterministic execution and recursive minimax-like evaluation (maximize for our player, minimize for opponent).
  - Defensive handling: if something fails, falls back to self.value_fn.

New functions / methods created
- No new top-level functions were added. The updated behavior is implemented by modifying FooPlayer methods:
  - FooPlayer.__init__ (updated)
  - FooPlayer.decide (updated)
  - FooPlayer._evaluate_node (updated)

Debugging / print statements added
- Printouts added across:
  - __init__: indicates whether contender_fn or base_fn was selected.
  - decide: reports when robber actions were pruned, when infrastructure actions are prioritized, when maritime trades are filtered, number of actions evaluated, per-action expected_value and action_type, and final chosen action with score and node_expansions.
  - _evaluate_node: logs on exceptions from value_fn, list_prunned_actions, prune_robber_actions, and filtering errors.

Fallbacks and defensive handling
- If contender_fn(DEFAULT_WEIGHTS) raises, the code falls back to base_fn(); if that also fails, it falls back to a trivial lambda returning 0.0 to keep the agent safe.
- All adapter calls are wrapped in try/except blocks with diagnostic printouts.
- If list_prunned_actions or prune_robber_actions fail, the code will fallback to heuristic evaluation rather than crashing.

Notes / caveats / points for future iteration
- contender_fn signature: In adapters.py it is defined as contender_fn(params). I supplied DEFAULT_WEIGHTS as params. If you want tuned parameters, provide them; the code currently uses DEFAULT_WEIGHTS exposed by adapters.
- Detection of robber actions uses a string check 'ROBBER' in action_type.name. This is somewhat defensive and should be fine because ActionType enums typically include descriptive names (PLACE_ROBBER / MOVE_ROBBER). If the enum naming differs, the check may fail; consider using a direct ActionType constant if available (e.g., ActionType.PLACE_ROBBER). If you can confirm exact enum members in the environment, I can switch to a strict enum comparison.
- Maritime trade filtering is heuristic: if there are more than 6 actions, prefer non-trade actions; in nodes with >8 actions we filter trades in recursion. These thresholds are tunable — lowering thresholds reduces compute but may remove legitimate trade opportunities.
- Node expansion is still potentially high for some actions. Next improvements:
  - Add top-k pruning: compute a cheap heuristic for each action, only fully search the top-k actions.
  - Add caching / transposition table keyed on game state representation to avoid recomputing values for identical states.
  - Replace execute_deterministic with execute_spectrum or expand_spectrum for more accurate chance modeling (but at higher compute cost).
  - Consider time-limited search or iterative deepening to balance lookahead depth and time.

Questions to help future iterations
- Confirm allowed ActionType enum members for robber actions (exact name(s) expected). If you provide exact members (e.g., ActionType.MOVE_ROBBER or ActionType.PLACE_ROBBER), I'll replace the string-based detection with strict comparisons.
- Any constraints on decision time per move? This affects acceptable lookahead depth and pruning aggressiveness.
- Do you want contender_fn parameter tuning now, or should we iterate on search/pruning first?

Files changed
- foo_player.py (rewritten per above).

If you want, I can now:
- Replace string-based robber detection with strict ActionType comparisons if you confirm the enum member names.
- Add a cheap pre-evaluation (single call to value_fn on outcome states) to rank and only fully expand top-k actions, drastically reducing node expansions.
- Add a basic transposition table keyed by game.copy().state or a hash to memoize evaluated nodes.

END CODER

