--- Input for STRATEGIZER at 20250925_053141 ---
================================ Human 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.
================================== Ai 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
================================ Human Message =================================

- META THOUGHTS:
The transition to a 2-ply Expectimax algorithm initially showed promise, but the latest results indicate a decline in performance (9 wins, 21 losses) compared to the previous 1-ply lookahead. The Analyzer has identified several critical issues:
1. **Shallow Search Depth**: The 2-ply depth is insufficient for long-term strategic planning in Catan.
2. **Lack of Specialized Strategies**: The player does not optimize for robber/knight usage, development cards, or adaptive heuristics.
3. **Generic Heuristic**: The reliance on `base_fn` may not capture nuanced opportunities like road networks or settlement expansion.

The current approach is too limited to compete effectively against the AlphaBeta opponent. A deeper search strategy (e.g., 3-4 ply) combined with custom heuristics for key actions (robber, development cards, road/settlement expansion) is necessary. Additionally, the Strategizer should explore ways to optimize the search space and improve the heuristic evaluation.

- META GOAL:
Develop a **deeper search strategy** (3-4 ply) with **custom heuristics** for robber placement, development cards, and adaptive road/settlement expansion to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Design a **deeper search strategy** (3-4 ply) for the Expectimax algorithm, incorporating the following:
1. **Increased Search Depth**: Propose a method to extend the search depth to 3-4 plies while managing computational complexity (e.g., iterative deepening, move ordering, or pruning).
2. **Custom Heuristics**: Develop specialized heuristics for:
   - Robber placement to disrupt opponents and optimize resource denial.
   - Development card usage (e.g., prioritizing "MONOPOLY" or "ROAD_BUILDING" based on game state).
   - Adaptive road and settlement expansion to maximize long-term value.
3. **Search Optimization**: Suggest techniques to reduce the search space (e.g., pruning non-promising actions, caching intermediate results).
4. **Adaptive Opponent Modeling**: If feasible, propose a way to model the opponent's strategy to improve decision-making.

Provide a **clear, actionable plan** for the Coder to implement, focusing on algorithmic solutions using `adapters.py` functions. Avoid simple rule-based heuristics.
================================== Ai Message ==================================

STRATEGIZER
To address the performance decline and improve the `foo_player.py`, we will implement a **3-4 ply Expectimax search** with **custom heuristics** and **search optimizations**. Below is the actionable plan:

---

### **Proposed Strategy: Deeper Expectimax with Custom Heuristics**
#### **Key Features**
1. **Increased Search Depth (3-4 Ply)**:
   - Use **iterative deepening** to gradually increase depth and manage computational cost.
   - Implement **move ordering** to prioritize high-value actions (e.g., building settlements, playing development cards).

2. **Custom Heuristics**:
   - **Robber Placement**: Use `prune_robber_actions` to focus on high-impact placements that disrupt opponents or secure resources.
   - **Development Cards**: Prioritize cards like "MONOPOLY" or "ROAD_BUILDING" based on the current game state and resource availability.
   - **Road/Settlement Expansion**: Use a heuristic that evaluates long-term value, such as resource diversity and expansion potential.

3. **Search Optimization**:
   - **Pruning**: Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - **Caching**: Cache intermediate results of the `expectimax` function to avoid redundant computations.

4. **Adaptive Opponent Modeling**:
   - Assume the opponent will maximize their own value, and adjust the search tree accordingly.

---

### **Implementation Plan**
#### **Step 1: Extend Search Depth to 3-4 Ply**
- Modify the `expectimax` function to support **iterative deepening**:
  - Start with a depth of 1 and gradually increase to 3-4.
  - Use a time limit to ensure the search completes within a reasonable timeframe.

#### **Step 2: Implement Custom Heuristics**
- **Robber Placement**:
  - Use `prune_robber_actions` to filter robber actions to only the most impactful ones.
  - Prioritize placements that block opponents' high-value resource tiles.
- **Development Cards**:
  - Prioritize playing "MONOPOLY" or "ROAD_BUILDING" based on the current resource state.
  - Use `value_production` to evaluate the potential impact of each card.
- **Road/Settlement Expansion**:
  - Use a heuristic that evaluates the long-term value of building roads and settlements, such as access to diverse resources and expansion potential.

#### **Step 3: Optimize Search Space**
- **Pruning**:
  - Use `list_prunned_actions` to reduce the action space.
  - For robber actions, use `prune_robber_actions` to further reduce the search space.
- **Caching**:
  - Cache intermediate results of the `expectimax` function to avoid redundant computations.

#### **Step 4: Update the `decide` Method**
- Replace the current 2-ply search with the new 3-4 ply Expectimax.
- Integrate the custom heuristics and search optimizations.

---

### **Pseudocode for Updated Expectimax**
```python
def expectimax(node_game: Game, depth: int, alpha: float, beta: float, cache: dict) -> float:
    """Return the expectimax value of node_game from the perspective of self.color."""
    # Check cache for previously computed results
    cache_key = (node_game.state_hash(), depth)
    if cache_key in cache:
        return cache[cache_key]

    # Terminal check
    winner = node_game.winning_color()
    if winner is not None or depth == 0:
        value = custom_heuristic(node_game, self.color)
        cache[cache_key] = value
        return value

    # Get pruned actions
    node_actions = list_prunned_actions(node_game)
    if not node_actions:
        value = custom_heuristic(node_game, self.color)
        cache[cache_key] = value
        return value

    # Determine if this is a MAX or MIN node
    node_color = node_actions[0].color
    is_max_node = (node_color == self.color)

    if is_max_node:
        best_value = -math.inf
        for act in node_actions:
            # Apply move ordering to prioritize high-value actions
            if act.action_type == ActionType.PLAY_DEV_CARD:
                act = prioritize_dev_cards(act, node_game)

            outcomes = execute_spectrum(node_game, act)
            expected = 0.0
            for (g_after, prob) in outcomes:
                val = expectimax(g_after, depth - 1, alpha, beta, cache)
                expected += prob * val
                if expected > best_value:
                    best_value = expected
                    alpha = max(alpha, best_value)
                    if beta <= alpha:
                        break  # Alpha-beta pruning
        cache[cache_key] = best_value
        return best_value
    else:
        worst_value = math.inf
        for act in node_actions:
            outcomes = execute_spectrum(node_game, act)
            expected = 0.0
            for (g_after, prob) in outcomes:
                val = expectimax(g_after, depth - 1, alpha, beta, cache)
                expected += prob * val
                if expected < worst_value:
                    worst_value = expected
                    beta = min(beta, worst_value)
                    if beta <= alpha:
                        break  # Alpha-beta pruning
        cache[cache_key] = worst_value
        return worst_value

def custom_heuristic(game: Game, color: Color) -> float:
    """Evaluate the game state using custom heuristics."""
    base_value = base_fn()(game, color)
    robber_value = evaluate_robber_placement(game, color)
    dev_card_value = evaluate_dev_cards(game, color)
    expansion_value = evaluate_expansion(game, color)
    return base_value + robber_value + dev_card_value + expansion_value
```

---

### **Actionable Steps for the Coder**
1. **Implement Iterative Deepening**:
   - Modify the `decide` method to use iterative deepening for the Expectimax search.
   - Start with a depth of 1 and gradually increase to 3-4.

2. **Integrate Custom Heuristics**:
   - Implement `custom_heuristic` to evaluate robber placement, development cards, and expansion.
   - Use `prune_robber_actions` and `value_production` to inform heuristic decisions.

3. **Optimize Search Space**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - Cache intermediate results of the `expectimax` function.

4. **Test and Validate**:
   - Test the updated player against the AlphaBeta opponent.
   - Monitor performance and adjust heuristics or depth as needed.

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

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

This is the current performance history

{
  "Evolution 0": {
    "wins": 0,
    "avg_score": 2.0,
    "avg_turns": 59.8,
    "full_game_log_path": "game_20250925_051626_fg/game_output.txt",
    "json_game_results_path": "game_20250925_051626_fg/game_20250925_051626_fg.json",
    "cur_foo_player_path": "game_20250925_051626_fg/foo_player.py",
    "cli_run_id": "game_20250925_051626_fg",
    "timestamp": "2025-09-25 05:17:01"
  },
  "Evolution 1": {
    "wins": 14,
    "avg_score": 8.233333333333333,
    "avg_turns": 75.03333333333333,
    "full_game_log_path": "game_20250925_051839_fg/game_output.txt",
    "json_game_results_path": "game_20250925_051839_fg/game_20250925_051839_fg.json",
    "cur_foo_player_path": "game_20250925_051839_fg/foo_player.py",
    "cli_run_id": "game_20250925_051839_fg",
    "timestamp": "2025-09-25 05:19:33"
  },
  "Evolution 2": {
    "wins": 9,
    "avg_score": 7.166666666666667,
    "avg_turns": 74.96666666666667,
    "full_game_log_path": "game_20250925_052153_fg/game_output.txt",
    "json_game_results_path": "game_20250925_052153_fg/game_20250925_052153_fg.json",
    "cur_foo_player_path": "game_20250925_052153_fg/foo_player.py",
    "cli_run_id": "game_20250925_052153_fg",
    "timestamp": "2025-09-25 05:23:34"
  },
  "Evolution 3": {
    "wins": 14,
    "avg_score": 7.366666666666666,
    "avg_turns": 72.13333333333334,
    "full_game_log_path": "game_20250925_052630_fg/game_output.txt",
    "json_game_results_path": "game_20250925_052630_fg/game_20250925_052630_fg.json",
    "cur_foo_player_path": "game_20250925_052630_fg/foo_player.py",
    "cli_run_id": "game_20250925_052630_fg",
    "timestamp": "2025-09-25 05:30:57"
  }
}
================================ Human Message =================================

This is the current foo_player.py file

from .adapters import (
    Game,
    Player,
    Color,
    Action,
    ActionType,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    expand_spectrum,
    list_prunned_actions,
    prune_robber_actions,
    base_fn,
    value_production,
    get_value_fn,
)

import math
import time
from typing import List, Tuple


class FooPlayer(Player):
    def __init__(self, name=None, max_depth: int = 3, node_budget: int = 8000):
        """
        FooPlayer uses an iterative-deepening Expectimax search with simple move ordering,
        robber-action pruning, and a transposition cache to control search cost.

        Args:
            name: Optional player name
            max_depth: Maximum plies to search (iterative deepening will grow to this)
            node_budget: Hard limit on number of recursive nodes to evaluate per decide() call
        """
        # Initialize as the BLUE player by default (keeps compatibility with previous versions)
        super().__init__(Color.BLUE, name)
        # Configurable search parameters
        self.max_depth = max_depth
        self.node_budget = node_budget

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

        Key features implemented:
        - Iterative deepening from depth=1 up to self.max_depth (inclusive).
        - Transposition cache (simple dict keyed by game.state repr and depth) to avoid
          re-evaluating identical subtrees.
        - Move ordering to explore promising actions first (helps pruning & early good bounds).
        - Robber action pruning via adapters.prune_robber_actions when robber moves appear.
        - Uses execute_spectrum for probabilistic outcomes; falls back to execute_deterministic.
        - Evaluates leaf nodes with base_fn() (can be swapped with get_value_fn if desired).

        Notes on safety and adapters usage:
        - Only calls functions exposed by adapters.py (no direct catanatron imports).
        - Attempts to be robust to adapter failures by catching exceptions and using
          reasonable fallbacks.

        Args:
            game: Current Game state (read-only from caller's perspective)
            playable_actions: Iterable of legal actions to choose from
        Returns:
            An Action chosen from playable_actions or None if no actions available
        """
        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

        # Parameters for search
        MAX_DEPTH = max(1, min(4, self.max_depth))  # cap to [1..4] to avoid runaway costs
        NODE_BUDGET = max(100, self.node_budget)

        # Value function factory (primary heuristic)
        value_fn = base_fn()

        # Transposition cache: maps (state_repr, depth) -> value
        cache = {}

        # Node evaluation counter and timing
        node_count = 0
        start_time = time.time()

        # Small helper to generate a cache key for a game state
        def _state_key(g: Game) -> str:
            # Game implementations typically expose a serializable .state; using repr as a fallback
            try:
                return repr(g.state)
            except Exception:
                try:
                    return repr(g)
                except Exception:
                    return str(id(g))

        # Quick move-ordering heuristic to try promising moves first. This helps the search find
        # good solutions early which improves the utility of iterative deepening.
        def _action_priority(act: Action) -> float:
            # Higher values are explored first.
            try:
                at = act.action_type
                # Prioritize playing dev cards, building settlements/cities, then roads.
                if at == ActionType.PLAY_DEV_CARD:
                    return 100.0
                if at == ActionType.BUILD_SETTLEMENT:
                    return 90.0
                if at == ActionType.BUILD_CITY:
                    return 95.0
                if at == ActionType.BUILD_ROAD:
                    return 50.0
                if at == ActionType.BUY_DEV_CARD:
                    return 45.0
                if at == ActionType.MOVE_ROBBER:
                    return 30.0
                if at == ActionType.TRADE:
                    return 20.0
                if at == ActionType.ROLL:
                    return 10.0
            except Exception:
                pass
            return 0.0

        # Custom heuristic wrapper that augments base_fn with small additional signals.
        # We avoid heavy rule-based logic; additional terms are small nudges to prefer
        # productive states. This remains conservative and primarily relies on base_fn.
        def custom_heuristic(g: Game) -> float:
            try:
                base_val = value_fn(g, self.color)
            except Exception as e:
                print(f'FooPlayer.custom_heuristic: base_fn failed: {e}')
                base_val = -1e9

            # Small bonus for production (best-effort). We attempt to call value_production if possible.
            prod_bonus = 0.0
            try:
                # value_production expects a sample and player_name; many adapters expose this utility
                # but we don't know the exact sample shape; call with g.state if available.
                sample = getattr(g, 'state', g)
                prod = value_production(sample, getattr(self, 'name', 'P0'), include_variety=True)
                # Scale down production so it doesn't overwhelm base_fn
                prod_bonus = 0.01 * float(prod)
            except Exception:
                # If unavailable, silently ignore.
                prod_bonus = 0.0

            return base_val + prod_bonus

        # Expectimax implementation with a node budget and caching.
        def expectimax(node_game: Game, depth: int) -> float:
            nonlocal node_count

            # Enforce node budget
            node_count += 1
            if node_count > NODE_BUDGET:
                # Budget exhausted; return a heuristic estimate of current node to stop deep recursion
                print('FooPlayer.expectimax: node budget exhausted; returning heuristic')
                return custom_heuristic(node_game)

            # Check cache
            key = (_state_key(node_game), depth)
            if key in cache:
                return cache[key]

            # Terminal / winner check
            try:
                winner = None
                try:
                    winner = node_game.winning_color()
                except Exception:
                    winner = None
                if winner is not None:
                    val = custom_heuristic(node_game)
                    cache[key] = val
                    return val
            except Exception as e:
                print(f'FooPlayer.expectimax: winner check failed: {e}')

            # Depth limit -> evaluate
            if depth == 0:
                val = custom_heuristic(node_game)
                cache[key] = val
                return val

            # Get pruned action list
            try:
                node_actions = list_prunned_actions(node_game)
            except Exception as e:
                print(f'FooPlayer.expectimax: list_prunned_actions failed: {e}')
                node_actions = []

            if not node_actions:
                val = custom_heuristic(node_game)
                cache[key] = val
                return val

            # If robber actions exist, prune them to focus on impactful robber moves
            try:
                if any((getattr(a, 'action_type', None) == ActionType.MOVE_ROBBER) for a in node_actions):
                    node_actions = prune_robber_actions(self.color, node_game, node_actions)
            except Exception as e:
                # If pruning fails, continue with unpruned actions
                print(f'FooPlayer.expectimax: prune_robber_actions failed: {e}')

            # Move ordering: sort by priority descending
            try:
                node_actions.sort(key=_action_priority, reverse=True)
            except Exception:
                pass

            # Determine node type: MAX if acting color is our color, else MIN
            try:
                node_color = node_actions[0].color
                is_max = (node_color == self.color)
            except Exception:
                # Fallback: assume opponent node to be conservative
                is_max = False

            if is_max:
                best_value = -math.inf
                for act in node_actions:
                    # Expand outcomes
                    try:
                        outcomes = execute_spectrum(node_game, act)
                    except Exception:
                        try:
                            outcomes = execute_deterministic(node_game, act)
                        except Exception as e:
                            print(f'FooPlayer.expectimax: action execution failed for {act}: {e}')
                            continue

                    # If no outcomes, skip
                    if not outcomes:
                        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 for outcome: {e}')
                            val = -1e9
                        expected += prob * val

                    if expected > best_value:
                        best_value = expected

                    # Small optimization: if best_value already extremely high, we could break early
                    # but we avoid aggressive pruning to keep semantics correct.

                cache[key] = best_value
                return best_value
            else:
                # MIN node: adversarial opponent minimizing our value
                worst_value = math.inf
                for act in node_actions:
                    try:
                        outcomes = execute_spectrum(node_game, act)
                    except Exception:
                        try:
                            outcomes = execute_deterministic(node_game, act)
                        except Exception as e:
                            print(f'FooPlayer.expectimax: action execution failed for {act}: {e}')
                            continue

                    if not outcomes:
                        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 for outcome: {e}')
                            val = 1e9
                        expected += prob * val

                    if expected < worst_value:
                        worst_value = expected

                cache[key] = worst_value
                return worst_value

        # Iterative deepening over increasing depths; keep best action found at each depth.
        best_action = None
        best_value = -math.inf

        # Preserve results across depths using the same cache to accelerate deeper searches
        for depth in range(1, MAX_DEPTH + 1):
            # Reset node counter for each depth iteration to enforce per-depth budget
            node_count = 0
            print(f'FooPlayer.decide: Iterative deepening at depth {depth}')

            # Evaluate each top-level action
            for idx, action in enumerate(actions):
                expected_value = -math.inf
                try:
                    # Work on a copy to avoid side-effects from adapters
                    game_copy = copy_game(game)

                    # Expand top-level action outcomes
                    try:
                        outcomes = execute_spectrum(game_copy, action)
                    except Exception as e:
                        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, depth - 1)
                            except Exception as e:
                                print(f'FooPlayer.decide: expectimax error on outcome of action {action}: {e}')
                                val = -1e9
                            expected_value += prob * val

                    print(f'FooPlayer: Depth {depth} 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

                # Update best action when we find a better expected value
                if expected_value > best_value:
                    best_value = expected_value
                    best_action = action

                # If node budget exhausted, break early
                if node_count > NODE_BUDGET:
                    print('FooPlayer.decide: node budget exceeded during evaluations; breaking depth loop')
                    break

            # If node budget exhausted at this depth, stop iterative deepening
            if node_count > NODE_BUDGET:
                break

        # Fallback: if evaluations all 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} (search depth up to {depth})')
        return best_action

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

This is the current adapters.py file

"""Unified adapter for Catanatron agents.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

- META THOUGHTS:
The latest iteration of the FooPlayer, with a 3-ply Expectimax search and custom heuristics, has shown improvement but remains borderline competitive (14 wins, 16 losses). The Analyzer has identified key areas for further enhancement:
1. **Insufficient Search Depth**: The current 3-ply depth is better but still lacks the foresight needed for long-term strategic planning.
2. **Generic Heuristic**: The reliance on `base_fn` and a small production bonus does not fully capture nuanced opportunities like road networks, settlement expansion, or opponent disruption.
3. **Limited Robber/Knight and Development Card Strategies**: These actions are evaluated but lack targeted, adaptive strategies.

The next step should focus on **deeper search** (4-5 ply) and **specialized heuristics** for road/settlement expansion, robber placement, and development card optimization. Additionally, the Strategizer should explore ways to further optimize the search space and improve the heuristic evaluation.

- META GOAL:
Develop a **4-5 ply Expectimax search** with **specialized heuristics** for road/settlement expansion, robber placement, and development card optimization to consistently outperform the AlphaBeta opponent.

- CHOSEN AGENT: STRATEGIZER

- AGENT OBJECTIVE:
Design a **4-5 ply Expectimax search** with the following enhancements:
1. **Increased Search Depth**:
   - Propose a method to extend the search depth to 4-5 plies while managing computational complexity (e.g., iterative deepening, move ordering, or pruning).
2. **Specialized Heuristics**:
   - Develop heuristics for:
     - **Road/Settlement Expansion**: Evaluate long-term value based on resource diversity and expansion potential.
     - **Robber Placement**: Target high-impact placements to disrupt opponents or secure resources.
     - **Development Cards**: Prioritize cards like "MONOPOLY" or "ROAD_BUILDING" based on game state and resource availability.
3. **Search Optimization**:
   - Suggest techniques to reduce the search space (e.g., pruning non-promising actions, caching intermediate results).
4. **Adaptive Opponent Modeling**:
   - If feasible, propose a way to model the opponent's strategy to improve decision-making.

Provide a **clear, actionable plan** for the Coder to implement, focusing on algorithmic solutions using `adapters.py` functions. Avoid simple rule-based heuristics.

--- Output from STRATEGIZER at 20250925_053217 ---
================================== Ai Message ==================================
Tool Calls:
  read_older_foo_file (GU9p573sO)
 Call ID: GU9p573sO
  Args:
    num: 1
================================= Tool Message =================================
Name: read_older_foo_file

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

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

STRATEGIZER
---
### **Analysis of Performance History**
- **Evolution 0**: Baseline performance (0 wins, 2.0 avg score).
- **Evolution 1**: Significant improvement with 1-ply lookahead (14 wins, 8.23 avg score).
- **Evolutions 2-3**: Decline or stagnation with deeper Expectimax (9-14 wins, 7.16-7.36 avg score).
  - The current 3-ply Expectimax is **not leveraging depth effectively** due to:
    - Generic heuristics (`base_fn` + small production bonus).
    - Lack of specialized strategies for robber, development cards, and expansion.
    - Inefficient search space management.

---
### **Proposed Strategy: 4-5 Ply Expectimax with Specialized Heuristics**
#### **Key Enhancements**
1. **Increased Search Depth (4-5 Ply)**:
   - Use **iterative deepening** with a **node budget** to manage computational cost.
   - Implement **move ordering** to prioritize high-value actions (e.g., settlements, development cards).

2. **Specialized Heuristics**:
   - **Road/Settlement Expansion**:
     - Evaluate long-term value using `value_production` and resource diversity.
     - Prioritize actions that expand toward high-value tiles (e.g., ports, high-probability resources).
   - **Robber Placement**:
     - Use `prune_robber_actions` to focus on high-impact placements.
     - Target opponents with the most resources or critical infrastructure (e.g., cities).
   - **Development Cards**:
     - Prioritize "MONOPOLY" or "ROAD_BUILDING" based on resource availability and game phase.
     - Use `value_production` to assess the impact of playing a card.

3. **Search Optimization**:
   - **Pruning**:
     - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - **Caching**:
     - Cache intermediate results of `expectimax` to avoid redundant computations.

4. **Adaptive Opponent Modeling**:
   - Assume the opponent will maximize their own value, and adjust the search tree accordingly.

---
### **Implementation Plan**
#### **Step 1: Extend Search Depth to 4-5 Ply**
- Modify the `expectimax` function to support **iterative deepening** up to 5 plies.
- Use a **node budget** (e.g., 10,000 nodes) to cap computational cost.

#### **Step 2: Implement Specialized Heuristics**
- Replace the generic `custom_heuristic` with **three specialized components**:
  - **Expansion Heuristic**:
    ```python
    def expansion_heuristic(game: Game, color: Color) -> float:
        try:
            sample = getattr(game, 'state', game)
            prod = value_production(sample, getattr(self, 'name', 'P0'), include_variety=True)
            return 0.1 * float(prod)  # Scale to avoid overwhelming base_fn
        except Exception:
            return 0.0
    ```
  - **Robber Heuristic**:
    ```python
    def robber_heuristic(game: Game, color: Color) -> float:
        try:
            # Prioritize robbing opponents with high resource production
            opponents = [c for c in Color if c != color]
            max_opponent_prod = max(
                value_production(game.state, f"P{opponent.value}", include_variety=False)
                for opponent in opponents
            )
            return -0.2 * max_opponent_prod  # Negative to disrupt opponents
        except Exception:
            return 0.0
    ```
  - **Development Card Heuristic**:
    ```python
    def dev_card_heuristic(game: Game, color: Color) -> float:
        try:
            # Prioritize playing cards like MONOPOLY or ROAD_BUILDING
            dev_cards = getattr(game, 'dev_cards', {})
            if dev_cards.get(color, {}).get('MONOPOLY', 0) > 0:
                return 0.3  # Bonus for playing MONOPOLY
            if dev_cards.get(color, {}).get('ROAD_BUILDING', 0) > 0:
                return 0.2  # Bonus for playing ROAD_BUILDING
        except Exception:
            pass
        return 0.0
    ```

#### **Step 3: Combine Heuristics**
- Replace `custom_heuristic` with a **weighted sum** of the specialized heuristics:
  ```python
  def combined_heuristic(game: Game, color: Color) -> float:
      base_val = base_fn()(game, color)
      expansion_val = expansion_heuristic(game, color)
      robber_val = robber_heuristic(game, color)
      dev_card_val = dev_card_heuristic(game, color)
      return base_val + expansion_val + robber_val + dev_card_val
  ```

#### **Step 4: Optimize Search Space**
- **Pruning**:
  - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
- **Caching**:
  - Cache intermediate results of `expectimax` to avoid redundant computations.

#### **Step 5: Update the `decide` Method**
- Replace the current `expectimax` call with the new **4-5 ply search** and **combined heuristic**.
- Ensure the node budget is respected and iterative deepening is used.

---
### **Actionable Steps for the Coder**
1. **Implement Iterative Deepening**:
   - Modify the `decide` method to use iterative deepening up to 5 plies.
   - Enforce a node budget (e.g., 10,000 nodes) to cap computational cost.

2. **Integrate Specialized Heuristics**:
   - Replace `custom_heuristic` with `combined_heuristic`.
   - Implement `expansion_heuristic`, `robber_heuristic`, and `dev_card_heuristic`.

3. **Optimize Search Space**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - Cache intermediate results of `expectimax`.

4. **Test and Validate**:
   - Test the updated player against the AlphaBeta opponent.
   - Monitor performance and adjust heuristics or depth as needed.

---
**Expected Outcome**:
This strategy will enable the player to evaluate deeper sequences of actions while leveraging specialized heuristics for robber placement, development cards, and expansion, leading to improved decision-making and higher win rates.

END STRATEGIZER

