--- Input for CODER at 20250925_053230 ---
================================ 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 =================================

- 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.
================================== 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
================================ Human Message =================================

- META THOUGHTS:
The Strategizer has proposed a comprehensive plan to address the current limitations of the FooPlayer. The focus on **3-4 ply Expectimax search**, **custom heuristics**, and **search optimizations** directly targets the issues identified by the Analyzer. The use of iterative deepening, move ordering, and caching will help manage computational complexity, while specialized heuristics for robber placement, development cards, and expansion will improve strategic decision-making. The pseudocode provided is clear and actionable, ensuring the Coder can implement the changes effectively.

- META GOAL:
Develop a **deeper search strategy** (3-4 ply) with **custom heuristics** and **search optimizations** to consistently outperform the AlphaBeta opponent in Catan.

- CHOSEN AGENT: CODER

- AGENT OBJECTIVE:
Implement the **3-4 ply Expectimax search** with **custom heuristics** and **search optimizations** as proposed by the Strategizer. Specifically:
1. **Extend Search Depth**:
   - Use **iterative deepening** to gradually increase the search depth from 1 to 3-4 plies.
   - Implement **move ordering** to prioritize high-value actions (e.g., building settlements, playing development cards).
2. **Integrate Custom Heuristics**:
   - Implement `custom_heuristic` to evaluate robber placement, development cards, and road/settlement 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 to avoid redundant computations.
4. **Update the `decide` Method**:
   - Replace the current 2-ply search with the new 3-4 ply Expectimax.
   - Ensure robust error handling and debugging prints for traceability.

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

CODER

I rewrote foo_player.py to implement the Strategizer's recommendations:

Summary of changes:
- Implemented iterative-deepening Expectimax (up to 3-4 plies; default max_depth=3).
- Added a transposition cache to reuse evaluations across the search.
- Implemented a node budget to limit computation per decide() call.
- Added move ordering to prioritize likely-strong actions (builds, dev cards).
- Integrated prune_robber_actions to focus robber moves.
- Used execute_spectrum for probabilistic outcomes, with execute_deterministic as a fallback.
- Implemented a conservative custom_heuristic that augments base_fn with a small production bonus via value_production.
- Added numerous print debug statements for traceability and error handling.
- Constrained MAX_DEPTH to [1..4] to balance performance.

New/modified functions:
- custom_heuristic (inner function) to safely wrap base_fn and add a small production bonus.
- expectimax (inner function) now supports caching, node budgeting, robber pruning, and move ordering.
- Iterative deepening loop in decide that reuses cache and respects node budget.

Notes / caveats:
- I used repr(g.state) as the cache key when possible; if Game exposes a deterministic hash method, replacing _state_key with that will improve caching accuracy.
- value_production usage is best-effort; if the game state shape differs, the call will silently be ignored.
- This keeps strictly to adapters.py functions; no direct catanatron imports present.
- The node budget and max depth are configurable through FooPlayer constructor parameters.

What I want for the next iteration:
- Feedback on acceptable time/node budgets and desired default max_depth (3 vs 4).
- If there's a canonical game state hash method, provide its name so I can use it for cache keys.
- Whether to add opponent-specific value functions (contender_fn) or model opponent's heuristic explicitly.

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

CODER

I rewrote foo_player.py to implement the Strategizer's recommendations:

Summary of changes:
- Implemented iterative-deepening Expectimax (up to 3-4 plies; default max_depth=3).
- Added a transposition cache to reuse evaluations across the search.
- Implemented a node budget to limit computation per decide() call.
- Added move ordering to prioritize likely-strong actions (builds, dev cards).
- Integrated prune_robber_actions to focus robber moves.
- Used execute_spectrum for probabilistic outcomes, with execute_deterministic as a fallback.
- Implemented a conservative custom_heuristic that augments base_fn with a small production bonus via value_production.
- Added numerous print debug statements for traceability and error handling.
- Constrained MAX_DEPTH to [1..4] to balance performance.

New/modified functions:
- custom_heuristic (inner function) to safely wrap base_fn and add a small production bonus.
- expectimax (inner function) now supports caching, node budgeting, robber pruning, and move ordering.
- Iterative deepening loop in decide that reuses cache and respects node budget.

Notes / caveats:
- I used repr(g.state) as the cache key when possible; if Game exposes a deterministic hash method, replacing _state_key with that will improve caching accuracy.
- value_production usage is best-effort; if the game state shape differs, the call will silently be ignored.
- This keeps strictly to adapters.py functions; no direct catanatron imports present.
- The node budget and max depth are configurable through FooPlayer constructor parameters.

What I want for the next iteration:
- Feedback on acceptable time/node budgets and desired default max_depth (3 vs 4).
- If there's a canonical game state hash method, provide its name so I can use it for cache keys.
- Whether to add opponent-specific value functions (contender_fn) or model opponent's heuristic explicitly.

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

GAME RESULTS:

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 16,
      "AVG VP": 8.066666666666666,
      "AVG SETTLES": 2.4,
      "AVG CITIES": 2.2666666666666666,
      "AVG ROAD": 0.5666666666666667,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 14,
      "AVG VP": 7.366666666666666,
      "AVG SETTLES": 2.6,
      "AVG CITIES": 1.9,
      "AVG ROAD": 0.43333333333333335,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.1
    }
  },
  "Game Summary": {
    "AVG TICKS": 208.33333333333334,
    "AVG TURNS": 72.13333333333334,
    "AVG DURATION": 8.85061228275299
  }
}
================================ 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 **7.37 VP** vs. AlphaBetaPlayer:RED's **8.07 VP** (VP diff: **-0.7**).
- **Key Counts**:
  - Settlements: 2.60 (FooPlayer) vs. 2.40 (Opponent)
  - Cities: 1.90 (FooPlayer) vs. 2.27 (Opponent)
  - Roads: 0.43 (FooPlayer) vs. 0.57 (Opponent)
  - Dev Cards: 0.10 (FooPlayer) vs. 0.00 (Opponent)
  - Total Turns: 72.13

---
### **VERDICT**
**Borderline**: The player is competitive but still underperforms the opponent in VP and win rate.

---
### **LIKELY REASONS**
1. **Insufficient Search Depth and Heuristic**:
   - The player uses a **3-ply Expectimax search**, which is better than previous iterations but still lacks deeper strategic foresight.
   - **Code Citation**: `MAX_DEPTH = max(1, min(4, self.max_depth))` (Line 35, `foo_player.py`).
   - **Log Corroboration**: Actions are evaluated up to depth 3, but deeper planning is needed for long-term strategies (e.g., "FooPlayer.decide: Iterative deepening at depth 3").

2. **Generic Heuristic**:
   - The player relies on `base_fn` and a small production bonus (`custom_heuristic`), which may not capture nuanced strategic opportunities like road networks, settlement expansion, or opponent disruption.
   - **Code Citation**: `value_fn = base_fn()` (Line 38, `foo_player.py`).
   - **Log Corroboration**: Actions are chosen based on generic expected values (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.PLAY_YEAR_OF_PLENTY: 'PLAY_YEAR_OF_PLENTY'>, value=('WOOD', 'SHEEP')) with value 1199999930809222.5").

3. **Limited Robber/Knight Strategy**:
   - While robber actions are pruned for efficiency, the player lacks a targeted strategy to disrupt the opponent or optimize resource denial.
   - **Log Corroboration**: Robber actions are evaluated but lack strategic depth (e.g., "FooPlayer: Depth 3 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), None, None)) => expected value 899999944462766.1").

4. **No Adaptive Development Card Strategy**:
   - The player evaluates development card actions but does not prioritize or strategically use them (e.g., "MONOPOLY", "ROAD_BUILDING").
   - **Log Corroboration**: Development card actions are considered but lack strategic prioritization (e.g., "FooPlayer: Depth 3 Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.PLAY_YEAR_OF_PLENTY: 'PLAY_YEAR_OF_PLENTY'>, value=('WOOD', 'SHEEP')) => expected value 1199999930809222.5").

---
### **STRATEGIC FLAW**
The player's **3-ply search depth** and **generic heuristic** limit its ability to plan strategically for long-term goals like city upgrades, road networks, and targeted opponent disruption.

---
### **NEXT STEP**
Send to **Strategizer** to design a **deeper search strategy** (e.g., 4-5 ply) and **custom heuristics** for adaptive road/settlement expansion, robber placement, and development card optimization.

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

- META THOUGHTS:
The Strategizer has provided a detailed and actionable plan to address the current limitations of the FooPlayer. The proposed **4-5 ply Expectimax search** with **specialized heuristics** for road/settlement expansion, robber placement, and development card optimization directly targets the issues identified in the analysis. The use of iterative deepening, move ordering, and caching will help manage computational complexity, while the specialized heuristics will improve strategic decision-making. The pseudocode and clear implementation steps ensure the Coder can effectively translate this plan into code.

- META GOAL:
Implement 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: CODER

- AGENT OBJECTIVE:
Implement the **4-5 ply Expectimax search** with **specialized heuristics** as proposed by the Strategizer. Specifically:
1. **Extend Search Depth**:
   - Use **iterative deepening** to gradually increase the search depth up to 5 plies.
   - Enforce a **node budget** (e.g., 10,000 nodes) to manage computational cost.
2. **Integrate Specialized Heuristics**:
   - Replace the generic `custom_heuristic` with **three specialized components**:
     - **Expansion Heuristic**: Evaluate long-term value using `value_production` and resource diversity.
     - **Robber Heuristic**: Target high-impact placements to disrupt opponents or secure resources.
     - **Development Card Heuristic**: Prioritize cards like "MONOPOLY" or "ROAD_BUILDING" based on game state.
   - Combine these heuristics into a **weighted sum** for evaluation.
3. **Optimize Search Space**:
   - Use `list_prunned_actions` and `prune_robber_actions` to reduce the action space.
   - Cache intermediate results of `expectimax` to avoid redundant computations.
4. **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.

Provide a clear and concise implementation that adheres to the proposed strategy. Include robust error handling and debugging prints for traceability.
================================ 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,
    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 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_053352 ---
================================== Ai Message ==================================
Tool Calls:
  write_foo (call_RdIeyw1VMcFjrld8to5GBL0p)
 Call ID: call_RdIeyw1VMcFjrld8to5GBL0p
  Args:
    new_text: 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 = 10000):
        """
        FooPlayer uses iterative-deepening Expectimax with specialized heuristics
        and search optimizations (move ordering, robber pruning, caching).

        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 with BLUE by default to preserve compatibility
        super().__init__(Color.BLUE, name)
        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.

        Enhancements implemented based on Strategizer:
        - Iterative deepening up to min(5, self.max_depth)
        - Specialized heuristics: expansion, robber, development-card
        - Combined heuristic = base_fn + weighted specialized heuristics
        - Search optimizations: list_prunned_actions, prune_robber_actions, move ordering
        - Caching (transposition table) keyed by game state repr and depth
        - Global node budget enforced across iterative deepening

        Args:
            game: Current Game state
            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

        # Cap maximum search depth to [1..5]
        MAX_DEPTH = max(1, min(5, self.max_depth))
        NODE_BUDGET = max(100, self.node_budget)

        # Primary base value function
        base_value_fn = base_fn()

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

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

        # Helper to produce a reproducible cache key for a game state
        def _state_key(g: Game) -> str:
            try:
                return repr(g.state)
            except Exception:
                try:
                    return repr(g)
                except Exception:
                    return str(id(g))

        # Move ordering heuristic (higher = more promising)
        def _action_priority(act: Action) -> float:
            try:
                at = act.action_type
                if at == ActionType.PLAY_DEV_CARD:
                    return 120.0
                if at == ActionType.BUILD_CITY:
                    return 110.0
                if at == ActionType.BUILD_SETTLEMENT:
                    return 100.0
                if at == ActionType.BUILD_ROAD:
                    return 70.0
                if at == ActionType.BUY_DEV_CARD:
                    return 60.0
                if at == ActionType.MOVE_ROBBER:
                    return 50.0
                if at == ActionType.TRADE:
                    return 40.0
                if at == ActionType.ROLL:
                    return 10.0
            except Exception:
                pass
            return 0.0

        # Specialized heuristics as suggested by Strategizer.
        # Each returns a small float signal to be combined with the base value.

        def expansion_heuristic(g: Game, color: Color) -> float:
            """Estimate long-term expansion potential using value_production."""
            try:
                sample = getattr(g, 'state', g)
                player_name = getattr(self, 'name', 'P0')
                prod = value_production(sample, player_name, include_variety=True)
                # Scale down so it complements base_fn
                return 0.08 * float(prod)
            except Exception as e:
                # Be conservative on failures
                # Debug print could be noisy; comment unless needed
                # print(f'FooPlayer.expansion_heuristic failed: {e}')
                return 0.0

        def robber_heuristic(g: Game, color: Color) -> float:
            """Estimate impact of robber placement by measuring opponent production.
            We produce a negative value to prefer states that reduce opponent production.
            """
            try:
                sample = getattr(g, 'state', g)
                max_opponent_prod = 0.0
                # Iterate over known colors and measure production; skip our color
                for opp in list(Color):
                    if opp == color:
                        continue
                    try:
                        # Attempt to form a plausible player name; adapters may ignore unknown names
                        opp_name = getattr(self, 'name', f'P{opp.value}')
                        p = value_production(sample, opp_name, include_variety=False)
                        max_opponent_prod = max(max_opponent_prod, float(p))
                    except Exception:
                        continue
                # Negative because reducing opponent production is good for us
                return -0.12 * max_opponent_prod
            except Exception as e:
                # print(f'FooPlayer.robber_heuristic failed: {e}')
                return 0.0

        def dev_card_heuristic(g: Game, color: Color) -> float:
            """Prefer states where playing certain dev cards (MONOPOLY, ROAD_BUILDING)
            is likely to be impactful. This heuristic only nudges decisions slightly.
            """
            try:
                # Try common locations for dev card counts
                sample = getattr(g, 'state', None)
                player_name = getattr(self, 'name', 'P0')
                # Attempt multiple access patterns defensively
                devs = None
                if sample is None:
                    return 0.0

                # Common patterns: sample.get('dev_cards'), sample.dev_cards, or game.dev_cards
                if isinstance(sample, dict):
                    devs = sample.get('dev_cards')
                else:
                    devs = getattr(g, 'dev_cards', None) or getattr(sample, 'dev_cards', None)

                if not devs:
                    return 0.0

                # devs might be keyed by player name or color; try both
                count = 0
                try:
                    # player_name keyed
                    count = int(devs.get(player_name, {}).get('MONOPOLY', 0))
                except Exception:
                    try:
                        # color keyed
                        count = int(devs.get(color, {}).get('MONOPOLY', 0))
                    except Exception:
                        count = 0

                if count > 0:
                    return 0.25  # meaningful bonus for monopoly availability

                # ROAD_BUILDING check
                count_rb = 0
                try:
                    count_rb = int(devs.get(player_name, {}).get('ROAD_BUILDING', 0))
                except Exception:
                    try:
                        count_rb = int(devs.get(color, {}).get('ROAD_BUILDING', 0))
                    except Exception:
                        count_rb = 0

                if count_rb > 0:
                    return 0.15

            except Exception:
                pass
            return 0.0

        # Combined heuristic: base value + weighted specialized heuristics
        def combined_heuristic(g: Game, color: Color) -> float:
            try:
                base_val = base_value_fn(g, color)
            except Exception as e:
                print(f'FooPlayer.combined_heuristic: base_fn failed: {e}')
                base_val = -1e9

            exp_v = expansion_heuristic(g, color)
            rob_v = robber_heuristic(g, color)
            dev_v = dev_card_heuristic(g, color)

            # Weighted sum; keep specialized heuristics as modest nudges
            return base_val + exp_v + rob_v + dev_v

        # Expectimax with caching and node budget. Uses combined_heuristic at leaves.
        def expectimax(node_game: Game, depth: int) -> float:
            nonlocal node_count

            # Enforce node budget (global across iterative deepening)
            node_count += 1
            if node_count > NODE_BUDGET:
                print('FooPlayer.expectimax: node budget exhausted; returning heuristic')
                return combined_heuristic(node_game, self.color)

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

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

            # Depth cutoff
            if depth == 0:
                val = combined_heuristic(node_game, self.color)
                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 = combined_heuristic(node_game, self.color)
                cache[key] = val
                return val

            # If robber moves exist, prune them
            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:
                print(f'FooPlayer.expectimax: prune_robber_actions failed: {e}')

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

            # Determine node type: MAX if acting color == our color
            try:
                node_color = node_actions[0].color
                is_max = (node_color == self.color)
            except Exception:
                is_max = False

            if is_max:
                best_value = -math.inf
                for act in node_actions:
                    # Expand probabilistic 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 (max) 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 (max) for outcome: {e}')
                            val = -1e9
                        expected += prob * val

                    if expected > best_value:
                        best_value = expected

                    # Early stopping if node budget exhausted
                    if node_count > NODE_BUDGET:
                        break

                cache[key] = best_value
                return best_value
            else:
                # MIN node: model opponent as adversarial minimizing our heuristic
                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 (min) 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 (min) for outcome: {e}')
                            val = 1e9
                        expected += prob * val

                    if expected < worst_value:
                        worst_value = expected

                    if node_count > NODE_BUDGET:
                        break

                cache[key] = worst_value
                return worst_value

        # Iterative deepening. Use a global node budget across all depths.
        best_action = None
        best_value = -math.inf
        depth_reached = 0

        for depth in range(1, MAX_DEPTH + 1):
            print(f'FooPlayer.decide: Iterative deepening at depth {depth}')
            depth_reached = depth

            # Evaluate top-level actions in move-ordered sequence to get good bounds early
            ordered_actions = sorted(actions, key=_action_priority, reverse=True)

            for idx, action in enumerate(ordered_actions):
                if node_count > NODE_BUDGET:
                    print('FooPlayer.decide: Global node budget reached; stopping search')
                    break

                expected_value = -math.inf
                try:
                    game_copy = copy_game(game)
                    try:
                        outcomes = execute_spectrum(game_copy, action)
                    except Exception as e:
                        # Fallback to deterministic
                        # print(f'FooPlayer.decide: execute_spectrum failed for top-level action {action}: {e}')
                        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:
                        # No outcomes; skip this action
                        # 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 found so far (across depths)
                if expected_value > best_value:
                    best_value = expected_value
                    best_action = action

                # Respect global budget
                if node_count > NODE_BUDGET:
                    break

            # Stop deepening if budget exhausted
            if node_count > NODE_BUDGET:
                break

        if best_action is None and len(actions) > 0:
            print('FooPlayer: All evaluations failed or were skipped; defaulting to first action')
            best_action = actions[0]

        elapsed = time.time() - start_time
        print(f'FooPlayer: Chosen action = {best_action} with value {best_value} (depth reached {depth_reached}, nodes {node_count}, time {elapsed:.3f}s)')
        return best_action
================================= Tool Message =================================
Name: write_foo

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

CODER

I updated foo_player.py to implement the Strategizer's recommendations. Summary of changes written to foo_player.py:

- Extended iterative-deepening Expectimax to support depths up to 5 (capped).
- Implemented specialized heuristics:
  - expansion_heuristic using value_production to estimate long-term expansion potential.
  - robber_heuristic to penalize opponent production (encourages disruptive robber moves).
  - dev_card_heuristic to reward availability of MONOPOLY and ROAD_BUILDING dev cards.
  - combined_heuristic that adds these small nudges to base_fn evaluation.
- Search optimizations:
  - list_prunned_actions and prune_robber_actions usage to reduce branching.
  - Move ordering via _action_priority to explore promising moves first.
  - Global node budget enforced across iterative deepening (default node_budget now 10000).
  - Transposition cache keyed by repr(game.state) and depth.
- Robustness:
  - Defensive coding with try/except around adapter calls.
  - Fallback to execute_deterministic when execute_spectrum fails.
  - Conservative scaling of heuristic components to avoid overwhelming base_fn.
- Debug prints added for traceability:
  - Iterative deepening progress, per-action expected values, node budget exhaustion, and final selection.

Report of modifications and notes:
- Created/modified functions:
  - expansion_heuristic, robber_heuristic, dev_card_heuristic, combined_heuristic (all inner functions inside decide).
  - expectimax (rewritten to include caching, budget, and specialized heuristics).
- Added print statements for debugging: iterative deepening, per-action evaluation, budget messages, and final selection.
- Caching key uses repr(g.state) if available; if a canonical hash exists in Game, using it would be better.
- The heuristics use value_production and attempt to access dev_cards defensively; if the game's state shape differs, these heuristics will safely return 0.
- Node budget and max_depth are configurable via FooPlayer constructor. Default node_budget was increased to 10000 to allow deeper search.
- No direct imports from catanatron are present; adapters surface is used exclusively.

Questions / Next steps you can include in the next OBJECTIVE:
- Provide a canonical deterministic game-state hashing method if available (to improve caching).
- Confirm acceptable default node budget and target max_depth (3, 4, or 5) for time constraints.
- Optionally allow opponent-specific value functions (contender_fn) to better model MIN nodes.
- Decide whether to add iterative deepening time cap in addition to node budget.

END CODER

