--- Input for CODER at 20250925_052031 ---
================================ Human Message =================================

- META THOUGHTS:
The FooPlayer's current performance is abysmal, with 0 wins and a significant VP deficit compared to the AlphaBeta opponent. The root cause is the naive decision-making logic, which always selects the first available action without any evaluation. The Analyzer has identified this flaw and proposed a 1-ply value lookahead strategy as a foundational improvement. This strategy will use `copy_game` and `base_fn` from `adapters.py` to evaluate actions based on immediate outcomes, replacing the current reactive approach. While this may not be sufficient to consistently beat the AlphaBeta opponent, it is a critical first step toward introducing strategic decision-making.

- META GOAL:
Systematically improve the `foo_player.py` code to develop a strategic algorithm that can consistently win against the AlphaBeta opponent. The immediate focus is on implementing a 1-ply value lookahead to replace the naive decision-making logic.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement a **1-ply value lookahead** strategy in `foo_player.py` to replace the current naive logic (`return playable_actions[0]`). This involves:
1. Using `copy_game` to simulate the game state after executing each action in `playable_actions`.
2. Using `base_fn` to evaluate the value of the resulting game state for each simulated action.
3. Selecting the action with the highest evaluated value as the chosen move.
Ensure the implementation is robust and handles edge cases, such as empty `playable_actions`. Do not introduce additional heuristics or rule-based logic. Focus solely on the 1-ply lookahead using the provided adapters.
================================== Ai Message ==================================

CODER

Changes made to foo_player.py:
- Rewrote the FooPlayer.decide implementation to perform a 1-ply expected-value lookahead instead of always choosing the first action.
- Added imports from adapters: copy_game, execute_deterministic, base_fn (keeps the required adapters surface usage).
- Implementation details:
  - For each legal action:
    - Copy the current game with copy_game to avoid side effects.
    - Execute the action using execute_deterministic which returns a list of (game_after, probability).
    - Evaluate each outcome with the value function produced by base_fn() for this player's color.
    - Compute an expected value as the probability-weighted sum of outcome values.
  - Select the action with the highest expected value. If all evaluations fail, fallback to the first action.
  - If no playable_actions are provided, return None.
- Added robust error handling:
  - Catches exceptions in execution or evaluation and assigns -inf to bad outcomes so they won't be chosen.
- Added print debug statements to report:
  - When there are no playable actions.
  - The expected value computed for each action.
  - Any exceptions during evaluation.
  - The final chosen action and its value.
- Comments: Included extensive comments explaining the strategy, assumptions, and debugging outputs.

Files touched:
- foo_player.py (completely replaced/updated with the new code)

New functions or major code blocks added:
- No new top-level functions were created. The FooPlayer.decide method was substantially rewritten to implement the 1-ply lookahead.

Adapter usage assurances:
- The code only uses the adapters surface (copy_game, execute_deterministic, base_fn) and does not import catanatron directly. The required import pattern from .adapters is preserved.

Print statements added:
- Several print() calls added for debugging and traceability (action evaluations, exceptions, chosen action).

Potential issues / uncertainties / notes for next iteration:
- execute_deterministic is assumed to return a list of (game_after, probability). If deterministic actions always return a single (game, 1.0) tuple, this still works; the code handles multiple outcomes for generality.
- base_fn() is used with default parameters (DEFAULT_WEIGHTS) as provided by adapters. If you want tuned weights or a different value function, consider using get_value_fn or contender_fn from adapters.
- This is a single-ply lookahead. Against strong opponents (AlphaBeta), deeper search or better pruning will likely be necessary for competitive play.
- Next improvements to request:
  - Use expand_spectrum or execute_spectrum to handle chance nodes more precisely (e.g., dice, dev card draws) when relevant.
  - Use list_prunned_actions or prune_robber_actions to reduce branching before simulation.
  - Consider implementing 2-ply (expectimax/minimax) search or integrating the AlphaBeta player utilities if allowed.
  - Optionally expose configurable value function weights and allow passing a custom value function to FooPlayer.

If you'd like, I can:
- Implement a 2-ply expectimax or alpha-beta search using the adapters helpers.
- Integrate action pruning to reduce branching factor.
- Expose an init parameter to FooPlayer to select different value functions or weight sets.

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

CODER

Changes made to foo_player.py:
- Rewrote the FooPlayer.decide implementation to perform a 1-ply expected-value lookahead instead of always choosing the first action.
- Added imports from adapters: copy_game, execute_deterministic, base_fn (keeps the required adapters surface usage).
- Implementation details:
  - For each legal action:
    - Copy the current game with copy_game to avoid side effects.
    - Execute the action using execute_deterministic which returns a list of (game_after, probability).
    - Evaluate each outcome with the value function produced by base_fn() for this player's color.
    - Compute an expected value as the probability-weighted sum of outcome values.
  - Select the action with the highest expected value. If all evaluations fail, fallback to the first action.
  - If no playable_actions are provided, return None.
- Added robust error handling:
  - Catches exceptions in execution or evaluation and assigns -inf to bad outcomes so they won't be chosen.
- Added print debug statements to report:
  - When there are no playable actions.
  - The expected value computed for each action.
  - Any exceptions during evaluation.
  - The final chosen action and its value.
- Comments: Included extensive comments explaining the strategy, assumptions, and debugging outputs.

Files touched:
- foo_player.py (completely replaced/updated with the new code)

New functions or major code blocks added:
- No new top-level functions were created. The FooPlayer.decide method was substantially rewritten to implement the 1-ply lookahead.

Adapter usage assurances:
- The code only uses the adapters surface (copy_game, execute_deterministic, base_fn) and does not import catanatron directly. The required import pattern from .adapters is preserved.

Print statements added:
- Several print() calls added for debugging and traceability (action evaluations, exceptions, chosen action).

Potential issues / uncertainties / notes for next iteration:
- execute_deterministic is assumed to return a list of (game_after, probability). If deterministic actions always return a single (game, 1.0) tuple, this still works; the code handles multiple outcomes for generality.
- base_fn() is used with default parameters (DEFAULT_WEIGHTS) as provided by adapters. If you want tuned weights or a different value function, consider using get_value_fn or contender_fn from adapters.
- This is a single-ply lookahead. Against strong opponents (AlphaBeta), deeper search or better pruning will likely be necessary for competitive play.
- Next improvements to request:
  - Use expand_spectrum or execute_spectrum to handle chance nodes more precisely (e.g., dice, dev card draws) when relevant.
  - Use list_prunned_actions or prune_robber_actions to reduce branching before simulation.
  - Consider implementing 2-ply (expectimax/minimax) search or integrating the AlphaBeta player utilities if allowed.
  - Optionally expose configurable value function weights and allow passing a custom value function to FooPlayer.

If you'd like, I can:
- Implement a 2-ply expectimax or alpha-beta search using the adapters helpers.
- Integrate action pruning to reduce branching factor.
- Expose an init parameter to FooPlayer to select different value functions or weight sets.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 16,
      "AVG VP": 8.1,
      "AVG SETTLES": 2.6666666666666665,
      "AVG CITIES": 2.1,
      "AVG ROAD": 0.6,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.03333333333333333
    },
    "FooPlayer:BLUE": {
      "WINS": 14,
      "AVG VP": 8.233333333333333,
      "AVG SETTLES": 2.7666666666666666,
      "AVG CITIES": 2.1333333333333333,
      "AVG ROAD": 0.4,
      "AVG ARMY": 0.06666666666666667,
      "AVG DEV VP": 0.26666666666666666
    }
  },
  "Game Summary": {
    "AVG TICKS": 217.8,
    "AVG TURNS": 75.03333333333333,
    "AVG DURATION": 1.7297517776489257
  }
}
================================ Human Message =================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (14 wins, 16 losses).
- **VP Comparison**: FooPlayer:BLUE averaged **8.23 VP** vs. AlphaBetaPlayer:RED's **8.1 VP** (VP diff: **+0.13**).
- **Key Counts**:
  - Settlements: 2.77 (FooPlayer) vs. 2.67 (Opponent)
  - Cities: 2.13 (FooPlayer) vs. 2.1 (Opponent)
  - Roads: 0.4 (FooPlayer) vs. 0.6 (Opponent)
  - Dev Cards: 0.27 (FooPlayer) vs. 0.03 (Opponent)
  - Total Turns: 75.03

---
### **VERDICT**
**Borderline**: The player is competitive but slightly underperforms the opponent in wins and VP.

---
### **LIKELY REASONS**
1. **Limited Lookahead Depth**:
   - The player uses a **1-ply expected-value lookahead**, which evaluates immediate outcomes but lacks deeper strategic planning.
   - **Code Citation**: `outcomes = execute_deterministic(game_copy, action)` (Line 50, `foo_player.py`).
   - **Log Corroboration**: Actions are chosen based on immediate expected value (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_CITY: 'BUILD_CITY'>, value=5) with value 900000100019167.0").

2. **No Chance Handling for Probabilistic Actions**:
   - The player does not simulate or evaluate probabilistic outcomes (e.g., dice rolls, dev cards, robber placement).
   - **Code Citation**: Uses `execute_deterministic` instead of `execute_spectrum` for actions with inherent randomness.
   - **Log Corroboration**: No evidence of probabilistic evaluation in logs (e.g., no "execute_spectrum" calls).

3. **No Robber/Knight Strategy**:
   - The player does not prioritize or optimize robber placement or knight usage, which are critical for disrupting opponents.
   - **Log Corroboration**: Robber actions are evaluated but lack strategic depth (e.g., "FooPlayer: Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), <Color.RED: 'RED'>, None)) => expected value 899999944462766.1").

4. **No End-Turn Optimization**:
   - The player often defaults to ending turns without evaluating alternative actions (e.g., trades, development cards).
   - **Log Corroboration**: Frequent "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None)".

---
### **STRATEGIC FLAW**
The player's **1-ply lookahead** and **lack of probabilistic simulation** limit its ability to plan strategically, especially for actions like dice rolls, robber placement, and development card usage.

---
### **NEXT STEP**
Send to **Coder** to implement **multi-ply lookahead** and **probabilistic simulation** using `execute_spectrum` for actions like dice rolls and robber placement.

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

- META THOUGHTS:
The 1-ply lookahead implementation has significantly improved the FooPlayer's performance, achieving a near-even win rate (14 wins, 16 losses) and a slightly higher average VP than the AlphaBeta opponent. However, the current strategy is still limited by its shallow depth and lack of probabilistic handling for actions like dice rolls, robber placement, and development cards. The Analyzer has identified these gaps and proposed a multi-ply lookahead with probabilistic simulation as the next logical step. This will allow the player to evaluate deeper strategic outcomes and account for uncertainty, which is critical for outmaneuvering the AlphaBeta opponent.

- META GOAL:
Continue improving the `foo_player.py` code to develop a robust, strategic algorithm that can consistently outperform the AlphaBeta opponent. The immediate focus is on implementing a **multi-ply lookahead** with **probabilistic simulation** for actions involving chance.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Propose a **concrete strategy** to extend the current 1-ply lookahead to a **multi-ply lookahead** (e.g., 2-ply or deeper) that incorporates **probabilistic simulation** for actions like dice rolls, robber placement, and development cards. The strategy must:
1. Use `execute_spectrum` from `adapters.py` to simulate probabilistic outcomes.
2. Integrate a deeper search algorithm (e.g., expectimax or minimax) to evaluate multi-step sequences.
3. Prioritize actions with high long-term value while accounting for uncertainty.
4. Avoid simple heuristics and focus on algorithmic solutions using the provided adapters.
Provide a clear, actionable plan for the Coder to implement.
================================ Human Message =================================

STRATEGIZER
To extend the current 1-ply lookahead to a **multi-ply lookahead with probabilistic simulation**, we will implement an **Expectimax** algorithm. This approach is ideal for games with chance elements like Catan, as it combines probabilistic outcomes with adversarial reasoning. Below is the proposed strategy:

---

### **Proposed Strategy: Multi-Ply Expectimax with Probabilistic Simulation**
#### **Key Features**
1. **Expectimax Algorithm**:
   - Extend the current 1-ply lookahead to **2-ply or deeper** using Expectimax, which handles both adversarial (opponent) and chance (dice, robber, dev cards) nodes.
   - Use `execute_spectrum` to simulate probabilistic outcomes for actions like dice rolls, robber placement, and development cards.

2. **Probabilistic Simulation**:
   - For actions with chance (e.g., dice rolls), use `execute_spectrum` to generate all possible outcomes and their probabilities.
   - For deterministic actions (e.g., building), use `execute_deterministic` as before.

3. **Value Function**:
   - Use the existing `base_fn` or `contender_fn` to evaluate terminal states.
   - Propagate expected values backward through the tree, weighting by probabilities for chance nodes.

4. **Depth Limitation**:
   - Limit the search depth to **2-ply** initially to balance computational cost and strategic depth.
   - Optionally, add iterative deepening for later optimizations.

---

### **Implementation Plan**
#### **Step 1: Define the Expectimax Algorithm**
- Create a recursive `expectimax` function that:
  - **Maximizes** for the player's turn (adversarial node).
  - **Expects** for chance nodes (probabilistic outcomes).
  - **Evaluates** terminal states using `base_fn` or `contender_fn`.

#### **Step 2: Integrate Probabilistic Simulation**
- For actions with chance (e.g., dice rolls, robber placement, dev cards):
  - Use `execute_spectrum` to generate all possible outcomes and their probabilities.
  - For each outcome, recursively call `expectimax` and weight the result by its probability.
- For deterministic actions (e.g., building):
  - Use `execute_deterministic` as before.

#### **Step 3: Update the `decide` Method**
- Replace the current 1-ply evaluation with the `expectimax` function.
- Pass the current game state, playable actions, and depth limit (e.g., 2) to `expectimax`.
- Return the action with the highest expected value.

#### **Step 4: Optimize for Performance**
- Use `list_prunned_actions` to reduce the action space for deeper searches.
- Cache intermediate results to avoid redundant computations.

---

### **Pseudocode for Expectimax**
```python
def expectimax(game: Game, depth: int, is_max_player: bool) -> float:
    if depth == 0 or game.is_terminal():
        return base_fn()(game, self.color)

    if is_max_player:
        # Adversarial node: maximize
        best_value = -math.inf
        for action in list_prunned_actions(game):
            outcomes = execute_spectrum(game, action) if action.has_chance() else execute_deterministic(game, action)
            value = sum(prob * expectimax(g_after, depth - 1, False) for (g_after, prob) in outcomes)
            best_value = max(best_value, value)
        return best_value
    else:
        # Chance node: expect
        outcomes = expand_spectrum(game, list_prunned_actions(game))
        return sum(prob * expectimax(g_after, depth - 1, True) for (g_after, prob) in outcomes)
```

---

### **Actionable Steps for the Coder**
1. **Implement the `expectimax` function** as described above.
2. **Modify the `decide` method** to use `expectimax` with a depth of 2.
3. **Test the player** against the AlphaBeta opponent and compare performance.
4. **Optimize** by adjusting depth, pruning, or caching as needed.

---
**Expected Outcome**:
This strategy will enable the player to evaluate deeper sequences of actions while accounting for uncertainty, leading to more robust decision-making and improved win rates.

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

- META THOUGHTS:
The Strategizer has proposed a clear and actionable plan to implement a **multi-ply Expectimax algorithm** with probabilistic simulation. This approach addresses the current limitations of the 1-ply lookahead by incorporating deeper strategic planning and handling uncertainty through `execute_spectrum`. The use of Expectimax is well-suited for Catan's mix of adversarial and chance-based elements. The pseudocode provided is a strong foundation for the Coder to build upon, and the focus on leveraging `adapters.py` functions ensures alignment with the project's goals.

- META GOAL:
Systematically improve the `foo_player.py` code to develop a strategic algorithm that can consistently outperform the AlphaBeta opponent. The immediate focus is on implementing the **multi-ply Expectimax algorithm** with probabilistic simulation.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **multi-ply Expectimax algorithm** with probabilistic simulation as proposed by the Strategizer. Specifically:
1. Create a recursive `expectimax` function that:
   - Maximizes for the player's turn (adversarial node).
   - Expects for chance nodes (probabilistic outcomes).
   - Evaluates terminal states using `base_fn` or `contender_fn`.
2. Integrate probabilistic simulation using `execute_spectrum` for actions involving chance (e.g., dice rolls, robber placement, dev cards).
3. Update the `decide` method to use the `expectimax` function with a depth of 2.
4. Ensure the implementation is robust, with error handling and debugging prints for traceability.
5. Optimize performance by pruning actions and caching intermediate results where possible.

Provide a clear and concise implementation that adheres to the proposed strategy.
================================ Human Message =================================

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

from .adapters import (
    Game,
    Player,
    Color,
    copy_game,
    execute_deterministic,
    base_fn,
)

import math

class FooPlayer(Player):
    def __init__(self, name=None):
        # Initialize as the BLUE player by default (keeps compatibility with previous versions)
        super().__init__(Color.BLUE, name)

    def decide(self, game: Game, playable_actions):
        """
        Choose an action using a 1-ply expected-value lookahead.

        Strategy implemented:
        - For each legal action, simulate its deterministic outcomes using execute_deterministic
          (wrapped by the adapters API). This returns a list of (game_after, probability).
        - Evaluate each outcome with a base value function (base_fn) provided by the adapters.
        - Compute the expected value for the action as the probability-weighted sum of outcome values.
        - Choose the action with the highest expected value.

        Notes & assumptions:
        - We only perform a single-ply lookahead (no deeper recursion).
        - Uses adapters.copy_game to avoid mutating the provided game state.
        - Uses adapters.execute_deterministic to get outcome game states; deterministic actions
          should return a single outcome with probability 1.0, but we still handle multiple outcomes
          for generality.
        - If playable_actions is empty, returns None.

        Debugging:
        - Print statements emit the evaluated expected value for each action and the final choice.

        Args:
            game (Game): read-only current game state
            playable_actions (Iterable[Action]): legal actions to choose from
        Returns:
            Action | None: chosen action or None if no actions available
        """
        # Convert to a concrete list so we can iterate multiple times and index
        actions = list(playable_actions) if playable_actions is not None else []

        if len(actions) == 0:
            print('FooPlayer.decide: No playable actions available; returning None')
            return None

        # Build the base value function once. It returns a function f(game, color)->float
        value_fn = base_fn()

        best_action = None
        best_value = -math.inf

        # Evaluate each action with a 1-ply expected value calculation
        for idx, action in enumerate(actions):
            try:
                # Use a copy of the game to be safe. Adapters functions typically return copies,
                # but copying upfront avoids side-effects if an adapter does in-place changes.
                game_copy = copy_game(game)

                # Execute the action deterministically; this returns a list of (game_after, prob)
                outcomes = execute_deterministic(game_copy, action)

                # Compute expected value across all possible deterministic outcomes
                expected_value = 0.0
                for (g_after, prob) in outcomes:
                    try:
                        val = value_fn(g_after, self.color)
                    except Exception as e:
                        # If the value function errors for some reason, catch and report it.
                        print(f'FooPlayer: value_fn raised {e} on action {action}; treating as -inf')
                        val = -math.inf
                    expected_value += prob * val

                print(f'FooPlayer: Action #{idx} = {action} => expected value {expected_value}')

            except Exception as e:
                # If executing the action failed (shouldn't normally happen via adapters),
                # assign a very low value so it won't be chosen.
                print(f'FooPlayer: Exception while evaluating action {action}: {e}')
                expected_value = -math.inf

            # Track the best action seen so far
            if expected_value > best_value:
                best_value = expected_value
                best_action = action

        # Fallback: if best_action is still None (all evaluations failed), pick the first action
        if best_action is None and len(actions) > 0:
            print('FooPlayer: All evaluations failed; defaulting to first action')
            best_action = actions[0]

        print(f'FooPlayer: Chosen action = {best_action} with value {best_value}')
        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_052153 ---
================================== Ai Message ==================================
Tool Calls:
  write_foo (call_IUlZRcVCmKvdOmCw3nbI571Q)
 Call ID: call_IUlZRcVCmKvdOmCw3nbI571Q
  Args:
    new_text: from .adapters import (
    Game,
    Player,
    Color,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    list_prunned_actions,
    base_fn,
)

import math
from typing import List


class FooPlayer(Player):
    def __init__(self, name=None):
        # Initialize as the BLUE player by default (keeps compatibility with previous versions)
        super().__init__(Color.BLUE, name)

    def decide(self, game: Game, playable_actions):
        """
        Choose an action using a multi-ply Expectimax with probabilistic simulation.

        Strategy implemented:
        - Use an Expectimax search to a fixed depth (default 2 plies).
        - For each node, we consider the pruned action list returned by adapters.list_prunned_actions
          to reduce branching.
        - For each action we simulate all possible outcomes using execute_spectrum (which returns
          (game_after, probability) tuples). This naturally handles deterministic actions as a
          special case (single outcome with prob=1.0).
        - Chance outcomes are folded into the expected value computation for the action.
        - Nodes where the acting color equals this player's color are treated as MAX nodes;
          otherwise they are treated as MIN nodes (adversarial opponent).

        Notes & assumptions:
        - We rely only on the adapters surface (copy_game, execute_spectrum, execute_deterministic,
          list_prunned_actions, base_fn).
        - If playable_actions is empty, returns None.
        - Depth counts plies: depth=0 means evaluate the current state with the heuristic.
        - This implementation avoids additional hand-crafted heuristics and follows the
          expectimax structure proposed by the strategizer.

        Debugging:
        - Print statements emit evaluated expected values for top-level actions and any exceptions
          encountered during simulation.

        Args:
            game (Game): read-only current game state
            playable_actions (Iterable[Action]): legal actions to choose from
        Returns:
            Action | None: chosen action or None if no actions available
        """
        # Convert to a concrete list so we can iterate multiple times and index
        actions = list(playable_actions) if playable_actions is not None else []

        if len(actions) == 0:
            print('FooPlayer.decide: No playable actions available; returning None')
            return None

        # Depth for Expectimax (plies). Start with 2 as recommended.
        MAX_DEPTH = 2

        # Build the base value function once. It returns a function f(game, color)->float
        value_fn = base_fn()

        # Recursive Expectimax implementation
        def expectimax(node_game: Game, depth: int) -> float:
            """Return the expectimax value of node_game from the perspective of self.color.

            - If depth == 0 or there are no legal actions, evaluate with value_fn.
            - Otherwise, for each pruned action, compute the expected value over its spectrum
              (execute_spectrum). Then either take max or min over actions depending on the
              acting color.
            """
            try:
                # Terminal check: if the game reports a winner, evaluate directly.
                winner = None
                try:
                    # Many Game implementations expose a winning_color() method per adapters comment.
                    winner = node_game.winning_color()
                except Exception:
                    # If winning_color isn't available or errors, fall back to continuing search.
                    winner = None

                if winner is not None:
                    # Terminal state: return heuristic value (value_fn may incorporate terminal logic)
                    return value_fn(node_game, self.color)

                if depth == 0:
                    return value_fn(node_game, self.color)

                # Get a pruned list of actions to reduce branching.
                node_actions = list_prunned_actions(node_game)

                if not node_actions:
                    # No legal actions -> evaluate heuristic
                    return value_fn(node_game, self.color)

                # Determine if this node is a MAX node (our player) or MIN node (opponent).
                # We infer the acting color from the first available action; list_prunned_actions
                # returns actions with an associated color field.
                node_color = node_actions[0].color
                is_max_node = (node_color == self.color)

                if is_max_node:
                    best_value = -math.inf
                    # For each action, compute expected value across possible outcomes
                    for act in node_actions:
                        try:
                            # Use spectrum expansion to handle chance outcomes. Deterministic actions
                            # will simply return a single outcome with prob=1.0.
                            outcomes = execute_spectrum(node_game, act)
                        except Exception as e:
                            print(f'FooPlayer.expectimax: execute_spectrum failed for action {act}: {e}')
                            continue

                        expected = 0.0
                        for (g_after, prob) in outcomes:
                            try:
                                val = expectimax(g_after, depth - 1)
                            except Exception as e:
                                print(f'FooPlayer.expectimax: recursion error on outcome {g_after}: {e}')
                                val = -math.inf
                            expected += prob * val

                        if expected > best_value:
                            best_value = expected

                    return best_value

                else:
                    # MIN node: assume adversary minimizes our value (adversarial opponent)
                    worst_value = math.inf
                    for act in node_actions:
                        try:
                            outcomes = execute_spectrum(node_game, act)
                        except Exception as e:
                            print(f'FooPlayer.expectimax: execute_spectrum failed for action {act}: {e}')
                            continue

                        expected = 0.0
                        for (g_after, prob) in outcomes:
                            try:
                                val = expectimax(g_after, depth - 1)
                            except Exception as e:
                                print(f'FooPlayer.expectimax: recursion error on outcome {g_after}: {e}')
                                val = math.inf
                            expected += prob * val

                        if expected < worst_value:
                            worst_value = expected

                    return worst_value

            except Exception as e:
                # Any unexpected error during expectimax should yield a very low value so the action
                # won't be chosen at the top level.
                print(f'FooPlayer.expectimax: unexpected error: {e}')
                return -math.inf

        # Evaluate each top-level action using the expectimax search
        best_action = None
        best_value = -math.inf

        for idx, action in enumerate(actions):
            try:
                # Copy the game to avoid any in-place changes by adapters
                game_copy = copy_game(game)

                # Use execute_spectrum to capture all possible outcomes (handles deterministic as well)
                try:
                    outcomes = execute_spectrum(game_copy, action)
                except Exception as e:
                    # Fall back to deterministic execution if spectrum isn't supported for this action
                    print(f'FooPlayer.decide: execute_spectrum failed for top-level action {action}: {e}; trying deterministic')
                    try:
                        outcomes = execute_deterministic(game_copy, action)
                    except Exception as e2:
                        print(f'FooPlayer.decide: execute_deterministic also failed for action {action}: {e2}')
                        outcomes = []

                if not outcomes:
                    print(f'FooPlayer.decide: No outcomes for action {action}; skipping')
                    expected_value = -math.inf
                else:
                    expected_value = 0.0
                    for (g_after, prob) in outcomes:
                        try:
                            val = expectimax(g_after, MAX_DEPTH - 1)
                        except Exception as e:
                            print(f'FooPlayer.decide: expectimax error on outcome of action {action}: {e}')
                            val = -math.inf
                        expected_value += prob * val

                print(f'FooPlayer: Top-level Action #{idx} = {action} => expected value {expected_value}')

            except Exception as e:
                print(f'FooPlayer: Exception while evaluating top-level action {action}: {e}')
                expected_value = -math.inf

            # Track the best action seen so far (we maximize at the root for our player)
            if expected_value > best_value:
                best_value = expected_value
                best_action = action

        # Fallback: if best_action is still None (all evaluations failed), pick the first action
        if best_action is None and len(actions) > 0:
            print('FooPlayer: All evaluations failed; defaulting to first action')
            best_action = actions[0]

        print(f'FooPlayer: Chosen action = {best_action} with value {best_value}')
        return best_action
================================= Tool Message =================================
Name: write_foo

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

CODER

Changes made to foo_player.py:
- Replaced the previous 1-ply evaluation with a 2-ply Expectimax search (multi-ply framework) that uses probabilistic simulation.
- Key adapter functions used (only from .adapters): copy_game, execute_spectrum, execute_deterministic (fallback), list_prunned_actions, base_fn.
- Implemented a recursive inner function expectimax(node_game, depth) inside decide:
  - Evaluates terminal states or depth==0 with value_fn = base_fn().
  - Uses list_prunned_actions(node_game) to reduce branching.
  - Treats nodes whose acting color equals self.color as MAX nodes; otherwise as MIN nodes (adversarial).
  - For each action at a node, calls execute_spectrum(node_game, action) to get (game_after, probability) outcomes and computes expected values by recursively calling expectimax on outcomes.
  - Returns max over actions at MAX nodes, min over actions at MIN nodes (both using expected values over chance outcomes).
- Top-level decide:
  - Iterates over provided playable_actions (keeps original ordering & fallback behavior).
  - For each top-level action, uses copy_game and execute_spectrum to produce outcomes.
  - Calls expectimax on each outcome with depth = MAX_DEPTH - 1 (MAX_DEPTH set to 2).
  - Picks the action with the highest expected value; falls back to the first action if all evaluations fail.
- Robustness and fallback:
  - If execute_spectrum fails for a given action, falls back to execute_deterministic.
  - Many try/excepts added around adapter calls and recursive calls; debug prints issued for failures so bad branches get -inf/inf and are avoided.
- Debugging:
  - Added informative print statements:
    - When there are no playable actions.
    - When execute_spectrum/execute_deterministic fails.
    - Expected values for each top-level action and intermediate errors inside expectimax.
    - Final chosen action and its value.
- Comments:
  - Added explanatory comments throughout the code clarifying assumptions, behavior, and why adapters are used.

New functions / code blocks added:
- expectimax (inner recursive function inside decide) — implements the Expectimax recursion.
- MAX_DEPTH constant defined inside decide (set to 2 plies as requested).

Adapter usage notes / assurances:
- The code only imports and uses functions from .adapters; there are no direct imports from catanatron or other modules.
- Uses execute_spectrum to handle probabilistic actions; execute_deterministic is retained as a fallback for actions that do not support spectrum execution.

Print statements added:
- Prints added for:
  - No playable actions.
  - execute_spectrum failure handling (top-level and within expectimax).
  - execute_deterministic fallback failure.
  - Each top-level action's expected value.
  - Unexpected errors encountered during expectimax recursion.
  - Final chosen action and the corresponding expected value.

Potential issues / items for follow-up:
- Performance: This implementation does not implement caching (transposition table) or move ordering; deeper depths may be slow. Consider implementing memoization keyed by a game state hash or using adapters' expand_spectrum to batch expansions.
- Robber-specific pruning: list_prunned_actions is used to reduce branching, but there may be additional adapters like prune_robber_actions that can further reduce branching on robber moves — consider integrating that for efficiency.
- Terminal detection: The code attempts to call node_game.winning_color() per adapters' documentation. If the Game implementation uses a different API for terminal detection, this may need adjustment.
- Heuristic configuration: Currently uses base_fn() default weights. Consider exposing contender_fn or get_value_fn to test alternate heuristics.
- MIN node model: I treat opponent nodes as minimizers of our heuristic. If opponent behavior is not adversarial (e.g., noisy or stochastic), consider modeling opponent as expected-value maximizer for their own color or using opponent-specific value functions.
- Edge cases: If execute_spectrum returns an empty list for an action (unexpected), the code treats expected value as -inf and skips that action.

Suggestions for the next objective (to help Coder in next iteration):
- Add a transposition cache keyed by a deterministic serialization or hash of Game state to avoid recomputing expectimax on repeated states.
- Consider exposing MAX_DEPTH as a constructor parameter or environment/config option to allow tuning.
- Replace MIN-node heuristic with opponent-specific value function (e.g., evaluate from opponent perspective) or simulate opponent policy if known.
- Use prune_robber_actions for robber-specific pruning and expand_spectrum for batched expansion if available and beneficial.
- Add a simple time or node budget to avoid long blocking decisions and allow dynamic depth (iterative deepening).

If you'd like, I can:
- Add memoization (transposition table) to expectimax.
- Integrate prune_robber_actions and expand_spectrum for more efficient chance handling.
- Expose depth or value function choices via FooPlayer constructor arguments.

END CODER

