--- Input for ANALYZER at 20250925_044051 ---
================================ Human Message =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

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

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: Loss (0 wins out of 30 games).
- **VP Comparison**: FooPlayer (2.0 VP) vs. Opponent (10.0 VP), **VP Diff: -8.0**.
- **Key Counts**:
  - Settlements: 2.0
  - Cities: 0.0
  - Roads: 0.0
  - Dev Cards: 0.0
  - Total Turns: 62.53

---
### **VERDICT**
**Poor**: FooPlayer lost all games with a significant VP deficit and no meaningful progress in infrastructure or development.

---
### **LIKELY REASONS**
1. **No Action Evaluation**:
   - The player always selects the first action in `playable_actions` without any evaluation or strategy (Line 20, `foo_player.py`).
   - **Log Evidence**: Repeated lines of `"Choosing First Action on Default"` in `game_output.txt`.

2. **Lack of Lookahead or Heuristics**:
   - No use of `copy_game`, `execute_deterministic`, or value functions to assess action quality.
   - No handling of chance outcomes (dice, robber, dev cards).

3. **No Placement or Development Strategy**:
   - No logic for placing settlements, cities, roads, or acquiring dev cards.
   - **Result**: FooPlayer ends with only 2 settlements and no other infrastructure.

4. **No Adaptation to Game State**:
   - The player does not respond to opponent moves or changing game conditions.

---
### **STRATEGIC FLAW**
The player's logic is purely reactive and naive, always selecting the first available action without any evaluation, planning, or adaptation to the game state.

---
### **NEXT STEP**
Send to **Coder** to implement a **1-ply value lookahead** or a **basic heuristic for action selection** using `copy_game` and `execute_deterministic`.

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


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

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

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

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

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

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

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

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

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

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

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

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


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

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

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Good** (16 wins, 14 losses).
- **VP Comparison**: FooPlayer (8.27 VP) vs. Opponent (7.57 VP), **VP Diff: +0.7**.
- **Key Counts**:
  - Settlements: 2.5
  - Cities: 1.93
  - Roads: 0.63
  - Dev Cards: 0.07
  - Total Turns: 77.4

---
### **VERDICT**
**Good**: FooPlayer won 16/30 games with a VP lead of +0.7. The player demonstrates improved infrastructure and long-term planning.

---
### **LIKELY REASONS FOR SUCCESS**
1. **Effective Lookahead Depth**:
   - The **depth-3 lookahead** allows the player to evaluate long-term consequences of actions, such as building roads and settlements.
   - **Log Evidence**: Actions like `BUILD_ROAD` and `BUILD_SETTLEMENT` are prioritized and evaluated with high expected values:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(19, 46)) score=2100000180585772.2 node_expansions=45
     ```

2. **Infrastructure Prioritization**:
   - The player successfully prioritizes infrastructure actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) over maritime trades, leading to higher VP gains.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 15 infrastructure actions over 16 total
     ```

3. **Robber and Knight Handling**:
   - The player uses `prune_robber_actions` to reduce branching and focus on impactful robber placements.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 18 actions
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), <Color.RED: 'RED'>, None)) score=2100000152807995.5 node_expansions=116
     ```

---
### **CRITICAL ISSUES**
1. **Filtering Errors**:
   - The player encounters repeated errors when filtering maritime trades due to incorrect use of `filter` objects.
   - **Log Evidence**:
     ```
     FooPlayer._evaluate_node: Error filtering maritime trades: object of type 'filter' has no len()
     FooPlayer.decide: Exception while evaluating action Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.PLAY_KNIGHT_CARD: 'PLAY_KNIGHT_CARD'>, value=None): 'filter' object is not subscriptable
     ```
   - **Location**: Lines 100–105 in `foo_player.py` (filtering logic in `_evaluate_node`).

2. **Excessive Node Expansions**:
   - Some decisions (e.g., `BUILD_ROAD`) result in **excessive node expansions** (e.g., 1261 expansions for a single action), slowing down performance.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(22, 23)) score=2700000136145730.5 node_expansions=1261
     ```

3. **Maritime Trade Overvaluation**:
   - Despite prioritizing infrastructure, the player still occasionally selects maritime trades over higher-value actions.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WHEAT', 'WHEAT', 'WHEAT', 'WHEAT', 'BRICK')) score=2100000180585769.2 node_expansions=87
     ```

---
### **STRATEGIC FLAW**
The player's **filtering logic for maritime trades and robber actions is unstable**, causing runtime errors and inefficient node expansions. This undermines the otherwise effective depth-3 lookahead and infrastructure prioritization.

---
### **NEXT STEP**
Send to **Coder** to **fix the filtering logic for maritime trades and robber actions** in `_evaluate_node` and `decide` methods.

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


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

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

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Poor** (4 wins, 26 losses).
- **VP Comparison**: FooPlayer (5.6 VP) vs. Opponent (9.4 VP), **VP Diff: -3.8**.
- **Key Counts**:
  - Settlements: 2.2
  - Cities: 1.17
  - Roads: 0.3
  - Dev Cards: 0.1
  - Total Turns: 74.37

---
### **VERDICT**
**Poor**: FooPlayer lost 26/30 games with a significant VP deficit of -3.8. The player is underperforming in infrastructure development and long-term strategy.

---
### **LIKELY REASONS**
1. **Over-Prioritization of Maritime Trades**:
   - The player frequently selects `MARITIME_TRADE` actions, even when higher-value infrastructure actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', None, None, 'ORE')) score=1499999936141127.0 node_expansions=19
     ```
   - This suggests the **value function (`contender_fn`)** may still overvalue short-term resource gains over long-term infrastructure.

2. **Ineffective Robber/Knight Strategy**:
   - The player does not prioritize `MOVE_ROBBER` or `PLAY_KNIGHT` actions effectively, missing opportunities to disrupt opponents or secure resources.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 1, -1), <Color.RED: 'RED'>, None)) score=1200000005589069.5 node_expansions=3
     ```
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber.

3. **Insufficient Infrastructure Development**:
   - The player builds **fewer roads (0.3 avg) and cities (1.17 avg)** compared to the opponent, limiting expansion and VP gains.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 16 infrastructure actions over 17 total
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(40, 44)) score=1200000002811293.2 node_expansions=18
     ```
   - Despite prioritizing infrastructure, the player still underperforms in execution.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', None, None, 'ORE')) score=1499999936141127.0 node_expansions=19
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`contender_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in infrastructure and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`contender_fn`)** to prioritize infrastructure and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

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

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (8 wins, 22 losses).
- **VP Comparison**: FooPlayer (6.9 VP) vs. Opponent (8.83 VP), **VP Diff: -1.93**.
- **Key Counts**:
  - Settlements: 2.93
  - Cities: 1.2
  - Roads: 0.47
  - Dev Cards: 0.43
  - Total Turns: 80.73

---
### **VERDICT**
**Borderline**: FooPlayer won 8/30 games but lost with a VP deficit of -1.93. The player shows some improvement in infrastructure but still struggles with long-term strategy and resource management.

---
### **LIKELY REASONS**
1. **Overvaluation of Maritime Trades**:
   - The player frequently selects `MARITIME_TRADE` actions, even when higher-value infrastructure actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'BRICK')) score=899999997236041.5 node_expansions=36
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'BRICK')) score=1199999997241593.5 node_expansions=32
     ```
   - This suggests the **value function (`contender_fn`)** may still overvalue short-term resource gains over long-term infrastructure.

2. **Ineffective Robber/Knight Strategy**:
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber or play knights.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer._evaluate_node: Pruned robber actions from 18 to 1
     ```
   - This limits the player's ability to disrupt opponents or secure resources.

3. **Insufficient Infrastructure Development**:
   - The player builds **fewer roads (0.47 avg) and cities (1.2 avg)** compared to the opponent, limiting expansion and VP gains.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 9 infrastructure actions over 10 total
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(22, 23)) score=1199999997241608.0 node_expansions=24
     ```
   - Despite prioritizing infrastructure, the player still underperforms in execution.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
       Action 0: expected_value=1199999997241621.0 action_type=ActionType.END_TURN
       Action 1: expected_value=1199999997241604.5 action_type=ActionType.MARITIME_TRADE
       Action 2: expected_value=1199999997241642.5 action_type=ActionType.MARITIME_TRADE
       Action 3: expected_value=1199999997241617.0 action_type=ActionType.MARITIME_TRADE
       Action 4: expected_value=1199999975019385.8 action_type=ActionType.MARITIME_TRADE
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'BRICK')) score=1199999975019421.8 node_expansions=19
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`contender_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in infrastructure and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`contender_fn`)** to prioritize infrastructure and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".

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

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Borderline** (13 wins, 17 losses).
- **VP Comparison**: FooPlayer (7.17 VP) vs. Opponent (8.37 VP), **VP Diff: -1.2**.
- **Key Counts**:
  - Settlements: 2.13
  - Cities: 1.77
  - Roads: 0.37
  - Dev Cards: 0.37
  - Total Turns: 72.4

---
### **VERDICT**
**Borderline**: FooPlayer won 13/30 games but lost with a VP deficit of -1.2. The player shows improvement in city upgrades and settlements but still struggles with road expansion and long-term resource management.

---
### **LIKELY REASONS**
1. **Overvaluation of Maritime Trades**:
   - The player frequently selects `MARITIME_TRADE` actions, even when higher-value infrastructure actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'WHEAT')) score=1500000052814658.0 node_expansions=15
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WHEAT')) score=1800000075036888.2 node_expansions=40
     ```
   - This suggests the **value function (`contender_fn`)** may still overvalue short-term resource gains over long-term infrastructure.

2. **Insufficient Road Expansion**:
   - The player builds **fewer roads (0.37 avg)** compared to the opponent (0.63 avg), limiting expansion and resource access.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 4 infrastructure actions over 5 total
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_SETTLEMENT: 'BUILD_SETTLEMENT'>, value=18) score=2400000116693371.0 node_expansions=12
     ```
   - The player prioritizes settlements and cities but neglects road expansion, which is critical for early-game resource access.

3. **Aggressive Robber Pruning**:
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber or play knights.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer._evaluate_node: Pruned robber actions from 18 to 1
     ```
   - This limits the player's ability to disrupt opponents or secure resources.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
       Action 0: expected_value=1500000052814629.0 action_type=ActionType.END_TURN
       Action 1: expected_value=1500000052814635.5 action_type=ActionType.MARITIME_TRADE
       Action 2: expected_value=1500000052814615.5 action_type=ActionType.MARITIME_TRADE
       Action 3: expected_value=1500000052814623.5 action_type=ActionType.MARITIME_TRADE
       Action 4: expected_value=1500000052814658.0 action_type=ActionType.MARITIME_TRADE
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'WHEAT')) score=1500000052814658.0 node_expansions=15
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`contender_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in road expansion and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`contender_fn`)** to prioritize road expansion and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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

This is the current performance history

{
  "Evolution 0": {
    "wins": 0,
    "avg_score": 2.0,
    "avg_turns": 62.53333333333333,
    "full_game_log_path": "game_20250925_041340_fg/game_output.txt",
    "json_game_results_path": "game_20250925_041340_fg/game_20250925_041340_fg.json",
    "cur_foo_player_path": "game_20250925_041340_fg/foo_player.py",
    "cli_run_id": "game_20250925_041340_fg",
    "timestamp": "2025-09-25 04:14:15"
  },
  "Evolution 1": {
    "wins": 11,
    "avg_score": 7.1,
    "avg_turns": 76.46666666666667,
    "full_game_log_path": "game_20250925_041638_fg/game_output.txt",
    "json_game_results_path": "game_20250925_041638_fg/game_20250925_041638_fg.json",
    "cur_foo_player_path": "game_20250925_041638_fg/foo_player.py",
    "cli_run_id": "game_20250925_041638_fg",
    "timestamp": "2025-09-25 04:17:50"
  },
  "Evolution 2": {
    "wins": 16,
    "avg_score": 8.266666666666667,
    "avg_turns": 77.4,
    "full_game_log_path": "game_20250925_042045_fg/game_output.txt",
    "json_game_results_path": "game_20250925_042045_fg/game_20250925_042045_fg.json",
    "cur_foo_player_path": "game_20250925_042045_fg/foo_player.py",
    "cli_run_id": "game_20250925_042045_fg",
    "timestamp": "2025-09-25 04:22:54"
  },
  "Evolution 3": {
    "wins": 4,
    "avg_score": 5.6,
    "avg_turns": 74.36666666666666,
    "full_game_log_path": "game_20250925_042440_fg/game_output.txt",
    "json_game_results_path": "game_20250925_042440_fg/game_20250925_042440_fg.json",
    "cur_foo_player_path": "game_20250925_042440_fg/foo_player.py",
    "cli_run_id": "game_20250925_042440_fg",
    "timestamp": "2025-09-25 04:25:58"
  },
  "Evolution 4": {
    "wins": 8,
    "avg_score": 6.9,
    "avg_turns": 80.73333333333333,
    "full_game_log_path": "game_20250925_042833_fg/game_output.txt",
    "json_game_results_path": "game_20250925_042833_fg/game_20250925_042833_fg.json",
    "cur_foo_player_path": "game_20250925_042833_fg/foo_player.py",
    "cli_run_id": "game_20250925_042833_fg",
    "timestamp": "2025-09-25 04:30:44"
  },
  "Evolution 5": {
    "wins": 13,
    "avg_score": 7.166666666666667,
    "avg_turns": 72.4,
    "full_game_log_path": "game_20250925_043426_fg/game_output.txt",
    "json_game_results_path": "game_20250925_043426_fg/game_20250925_043426_fg.json",
    "cur_foo_player_path": "game_20250925_043426_fg/foo_player.py",
    "cli_run_id": "game_20250925_043426_fg",
    "timestamp": "2025-09-25 04:36:27"
  },
  "Evolution 6": {
    "wins": 9,
    "avg_score": 6.966666666666667,
    "avg_turns": 76.7,
    "full_game_log_path": "game_20250925_043838_fg/game_output.txt",
    "json_game_results_path": "game_20250925_043838_fg/game_20250925_043838_fg.json",
    "cur_foo_player_path": "game_20250925_043838_fg/foo_player.py",
    "cli_run_id": "game_20250925_043838_fg",
    "timestamp": "2025-09-25 04:40:51"
  }
}
================================ Human Message =================================

This is the current game_output.txt file

olor=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1800000044476675.5 node_expansions=3
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1800000011136355.5 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1800000011136355.5 node_expansions=33
FooPlayer.decide: Prioritizing 15 infrastructure actions over 16 total
FooPlayer.decide: Fully evaluating 15 actions with lookahead depth=3
  Action 0: expected_value=1800000011134146.8 action_type=ActionType.BUILD_ROAD
  Action 1: expected_value=1800000011135258.0 action_type=ActionType.BUILD_ROAD
  Action 2: expected_value=1800000011135800.2 action_type=ActionType.BUILD_ROAD
  Action 3: expected_value=1800000011135968.8 action_type=ActionType.BUILD_ROAD
  Action 4: expected_value=1800000011136089.2 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1800000011134133.2 action_type=ActionType.BUILD_ROAD
  Action 6: expected_value=1800000011134133.2 action_type=ActionType.BUILD_ROAD
  Action 7: expected_value=1800000011136257.8 action_type=ActionType.BUILD_ROAD
  Action 8: expected_value=1800000011134133.2 action_type=ActionType.BUILD_ROAD
  Action 9: expected_value=1800000011134144.2 action_type=ActionType.BUILD_ROAD
  Action 10: expected_value=1800000011135699.8 action_type=ActionType.BUILD_ROAD
  Action 11: expected_value=1800000011134133.2 action_type=ActionType.BUILD_ROAD
  Action 12: expected_value=1800000011135689.0 action_type=ActionType.BUILD_ROAD
  Action 13: expected_value=1800000011135133.2 action_type=ActionType.BUILD_ROAD
  Action 14: expected_value=1800000011136370.0 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(13, 34)) score=1800000011136370.0 node_expansions=45
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1800000011136357.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1800000011136357.5 node_expansions=4
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=2100000030577358.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=2100000030577358.8 node_expansions=94
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1800000011136367.0 action_type=ActionType.END_TURN
  Action 1: expected_value=1800000011136357.5 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1800000011136367.0 node_expansions=6
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999930580801.0 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1799999930580801.0 node_expansions=3
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1799999986136367.0 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999986136345.5 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999986136367.0 node_expansions=6
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=2100000005577358.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=2100000005577358.8 node_expansions=94
FooPlayer.decide: Filtering out maritime trades from 10 to 2 actions
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1799999986133594.2 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999986138444.0 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUY_DEVELOPMENT_CARD: 'BUY_DEVELOPMENT_CARD'>, value=None) score=1799999986138444.0 node_expansions=54
FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
  Action 0: expected_value=1799999986136347.5 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999986136368.5 action_type=ActionType.MARITIME_TRADE
  Action 2: expected_value=1799999986138444.0 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1799999986138428.5 action_type=ActionType.MARITIME_TRADE
  Action 4: expected_value=1799999986136369.0 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'BRICK')) score=1799999986138444.0 node_expansions=77
FooPlayer.decide: Prioritizing 16 infrastructure actions over 17 total
FooPlayer.decide: Fully evaluating 16 actions with lookahead depth=3
  Action 0: expected_value=1799999986136333.0 action_type=ActionType.BUILD_ROAD
  Action 1: expected_value=1799999986137455.2 action_type=ActionType.BUILD_ROAD
  Action 2: expected_value=1799999986138000.0 action_type=ActionType.BUILD_ROAD
  Action 3: expected_value=1799999986138178.0 action_type=ActionType.BUILD_ROAD
  Action 4: expected_value=1799999986136900.0 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1799999986138189.8 action_type=ActionType.BUILD_ROAD
  Action 6: expected_value=1799999986136333.0 action_type=ActionType.BUILD_ROAD
  Action 7: expected_value=1799999986136346.5 action_type=ActionType.BUILD_ROAD
  Action 8: expected_value=1799999986138445.0 action_type=ActionType.BUILD_ROAD
  Action 9: expected_value=1799999986136333.0 action_type=ActionType.BUILD_ROAD
  Action 10: expected_value=1799999986136344.5 action_type=ActionType.BUILD_ROAD
  Action 11: expected_value=1799999986137888.5 action_type=ActionType.BUILD_ROAD
  Action 12: expected_value=1799999986137333.0 action_type=ActionType.BUILD_ROAD
  Action 13: expected_value=1799999986136346.5 action_type=ActionType.BUILD_ROAD
  Action 14: expected_value=1799999986137888.8 action_type=ActionType.BUILD_ROAD
  Action 15: expected_value=1799999986137344.5 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(14, 37)) score=1799999986138445.0 node_expansions=48
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986138444.0 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999986138444.0 node_expansions=7
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986140289.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1799999986140289.2 node_expansions=29
FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
  Action 0: expected_value=1799999986135689.8 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999986140300.2 action_type=ActionType.MARITIME_TRADE
  Action 2: expected_value=1799999986138492.0 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1799999986138469.5 action_type=ActionType.MARITIME_TRADE
  Action 4: expected_value=1799999986138479.5 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'BRICK')) score=1799999986140300.2 node_expansions=71
FooPlayer.decide: Prioritizing 17 infrastructure actions over 18 total
FooPlayer.decide: Fully evaluating 17 actions with lookahead depth=3
  Action 0: expected_value=1799999986138466.5 action_type=ActionType.BUILD_ROAD
  Action 1: expected_value=1799999986139577.0 action_type=ActionType.BUILD_ROAD
  Action 2: expected_value=1799999986140133.5 action_type=ActionType.BUILD_ROAD
  Action 3: expected_value=1799999986140024.2 action_type=ActionType.BUILD_ROAD
  Action 4: expected_value=1799999986140300.2 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1799999986139022.2 action_type=ActionType.BUILD_ROAD
  Action 6: expected_value=1799999986139466.5 action_type=ActionType.BUILD_ROAD
  Action 7: expected_value=1799999986138466.5 action_type=ActionType.BUILD_ROAD
  Action 8: expected_value=1799999986138466.5 action_type=ActionType.BUILD_ROAD
  Action 9: expected_value=1799999986138466.5 action_type=ActionType.BUILD_ROAD
  Action 10: expected_value=1799999986138466.5 action_type=ActionType.BUILD_ROAD
  Action 11: expected_value=1799999986140024.2 action_type=ActionType.BUILD_ROAD
  Action 12: expected_value=1799999986139477.5 action_type=ActionType.BUILD_ROAD
  Action 13: expected_value=1799999986138466.5 action_type=ActionType.BUILD_ROAD
  Action 14: expected_value=1799999986140022.5 action_type=ActionType.BUILD_ROAD
  Action 15: expected_value=1799999986139465.5 action_type=ActionType.BUILD_ROAD
  Action 16: expected_value=1799999986139490.0 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(18, 40)) score=1799999986140300.2 node_expansions=51
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986140314.8 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999986140314.8 node_expansions=7
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986140323.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1799999986140323.8 node_expansions=3
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986140299.2 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999986140299.2 node_expansions=11
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986141953.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1799999986141953.2 node_expansions=37
FooPlayer.decide: Prioritizing 17 infrastructure actions over 18 total
FooPlayer.decide: Fully evaluating 17 actions with lookahead depth=3
  Action 0: expected_value=1799999986140314.8 action_type=ActionType.BUILD_ROAD
  Action 1: expected_value=1799999986140856.8 action_type=ActionType.BUILD_ROAD
  Action 2: expected_value=1799999986141593.5 action_type=ActionType.BUILD_ROAD
  Action 3: expected_value=1799999986141967.8 action_type=ActionType.BUILD_ROAD
  Action 4: expected_value=1799999986141856.8 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1799999986140314.8 action_type=ActionType.BUILD_ROAD
  Action 6: expected_value=1799999986141300.2 action_type=ActionType.BUILD_ROAD
  Action 7: expected_value=1799999986140301.2 action_type=ActionType.BUILD_ROAD
  Action 8: expected_value=1799999986140314.8 action_type=ActionType.BUILD_ROAD
  Action 9: expected_value=1799999986141301.2 action_type=ActionType.BUILD_ROAD
  Action 10: expected_value=1799999986140303.2 action_type=ActionType.BUILD_ROAD
  Action 11: expected_value=1799999986141856.8 action_type=ActionType.BUILD_ROAD
  Action 12: expected_value=1799999986141301.2 action_type=ActionType.BUILD_ROAD
  Action 13: expected_value=1799999986140301.2 action_type=ActionType.BUILD_ROAD
  Action 14: expected_value=1799999986141855.8 action_type=ActionType.BUILD_ROAD
  Action 15: expected_value=1799999986141301.2 action_type=ActionType.BUILD_ROAD
  Action 16: expected_value=1799999986141300.2 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(19, 21)) score=1799999986141967.8 node_expansions=51
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999952808648.0 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999952808648.0 node_expansions=13
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999986142002.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1799999986142002.2 node_expansions=5
FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
  Action 0: expected_value=1799999952808647.0 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999986141966.2 action_type=ActionType.MARITIME_TRADE
  Action 2: expected_value=1799999986141979.8 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1799999986141963.2 action_type=ActionType.MARITIME_TRADE
  Action 4: expected_value=1799999986141990.2 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'WHEAT')) score=1799999986141990.2 node_expansions=39
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1799999986141988.2 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999986141953.2 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999986141988.2 node_expansions=15
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=2099999983364168.0 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=2099999983364168.0 node_expansions=48
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1799999925026430.5 action_type=ActionType.END_TURN
  Action 1: expected_value=1799999952808632.0 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUY_DEVELOPMENT_CARD: 'BUY_DEVELOPMENT_CARD'>, value=None) score=1799999952808632.0 node_expansions=20
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999952808631.0 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999952808631.0 node_expansions=12
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1799999902808643.0 action_type=ActionType.PLAY_KNIGHT_CARD
  Action 1: expected_value=1799999925030888.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1799999925030888.8 node_expansions=20
FooPlayer.decide: Fully evaluating 3 actions with lookahead depth=3
  Action 0: expected_value=1799999902808679.0 action_type=ActionType.PLAY_KNIGHT_CARD
  Action 1: expected_value=1799999897253121.0 action_type=ActionType.END_TURN
  Action 2: expected_value=1799999925030876.8 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUY_DEVELOPMENT_CARD: 'BUY_DEVELOPMENT_CARD'>, value=None) score=1799999925030876.8 node_expansions=23
FooPlayer.decide: Fully evaluating 2 actions with lookahead depth=3
  Action 0: expected_value=1799999902808653.5 action_type=ActionType.PLAY_KNIGHT_CARD
  Action 1: expected_value=1799999897253108.0 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.PLAY_KNIGHT_CARD: 'PLAY_KNIGHT_CARD'>, value=None) score=1799999902808653.5 node_expansions=17
FooPlayer.decide: Pruned robber actions from 18 to 1
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999902808677.0 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-1, -1, 2), <Color.RED: 'RED'>, None)) score=1799999902808677.0 node_expansions=3
FooPlayer.decide: Fully evaluating 1 actions with lookahead depth=3
  Action 0: expected_value=1799999902808653.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1799999902808653.5 node_expansions=3
Playing 30 games...                                          ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━              70%        
FooPlayer:BLUE                                               ━━━━━━━━━━━━                              30%        
                    Last 10 Games                    
     ╷          ╷       ╷        ╷         ╷         
   # │ SEATING  │ TURNS │ RED VP │ BLUE VP │ WINNER  
╶────┼──────────┼───────┼────────┼─────────┼────────╴
  21 │ RED,BLUE │    78 │     10 │       3 │ RED     
  22 │ BLUE,RED │    80 │      8 │      10 │ BLUE    
  23 │ RED,BLUE │    82 │     10 │       7 │ RED     
  24 │ BLUE,RED │    68 │      3 │      10 │ BLUE    
  25 │ BLUE,RED │    65 │     10 │       4 │ RED     
  26 │ RED,BLUE │    94 │     10 │       6 │ RED     
  27 │ RED,BLUE │    84 │     10 │       8 │ RED     
  28 │ BLUE,RED │   101 │     10 │       8 │ RED     
  29 │ RED,BLUE │    62 │     10 │       4 │ RED     
  30 │ RED,BLUE │    88 │     10 │       8 │ RED     
     ╵          ╵       ╵        ╵         ╵         
                                                  Player Summary                                                   
                                                               ╷      ╷      ╷      ╷      ╷       ╷      ╷        
                                                               │      │      │      │      │       │      │   AVG  
                                                               │      │  AVG │  AVG │  AVG │   AVG │  AVG │   DEV  
                                                               │ WINS │   VP │ SET… │ CIT… │  ROAD │ ARMY │    VP  
╶──────────────────────────────────────────────────────────────┼──────┼──────┼──────┼──────┼───────┼──────┼───────╴
  AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False) │   21 │ 9.00 │ 2.60 │ 2.70 │  0.50 │ 0.00 │  0.00  
  FooPlayer:BLUE                                               │    9 │ 6.97 │ 2.57 │ 1.33 │  0.50 │ 0.13 │  0.47  
                                                               ╵      ╵      ╵      ╵      ╵       ╵      ╵        
              Game Summary              
            ╷           ╷               
  AVG TICKS │ AVG TURNS │ AVG DURATION  
╶───────────┼───────────┼──────────────╴
     221.30 │     76.70 │   4.353 secs  
            ╵           ╵
================================ Human Message =================================

This is the current game_results json file

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 21,
      "AVG VP": 9.0,
      "AVG SETTLES": 2.6,
      "AVG CITIES": 2.7,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 9,
      "AVG VP": 6.966666666666667,
      "AVG SETTLES": 2.566666666666667,
      "AVG CITIES": 1.3333333333333333,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.13333333333333333,
      "AVG DEV VP": 0.4666666666666667
    }
  },
  "Game Summary": {
    "AVG TICKS": 221.3,
    "AVG TURNS": 76.7,
    "AVG DURATION": 4.352547558148702
  }
}
================================ Human Message =================================

This is the current foo_player.py file

import os
from typing import Iterable, List, Tuple

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


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

    Improvements over the previous version:
    - Default lookahead depth increased to 3 to capture longer-term infrastructure
      consequences (settlements, roads, cities).
    - Uses base_fn (with DEFAULT_WEIGHTS) as the primary heuristic to avoid
      maritime-trade overvaluation observed with contender_fn in prior evolutions.
      Falls back to contender_fn on failure, then a dumb lambda.
    - Uses prune_robber_actions to reduce robber move branching when robber
      actions are available.
    - Prioritizes infrastructure actions (BUILD_SETTLEMENT, BUILD_ROAD,
      BUILD_CITY) over maritime trades when possible, with explicit city
      prioritization but not exclusivity.

    Notes about this update (rollback and fixes):
    - Reverted aggressive pruning introduced earlier that limited root/child
      expansions. We fully evaluate prioritized infrastructure actions to
      restore the successful Evolution 2 behavior.
    - Fixed bugs caused by using Python's filter() without materializing into
      a list. All filtering uses list comprehensions so len() and indexing work.
    - Robust defensive error handling kept so any adapter failure falls back
      to heuristic evaluation instead of crashing the player.
    """

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

        # Use base_fn as the primary heuristic to avoid maritime trade overvaluation
        try:
            # base_fn accepts optional params; use DEFAULT_WEIGHTS for consistency
            self.value_fn = base_fn(DEFAULT_WEIGHTS)
            print('FooPlayer.__init__: Using base_fn with DEFAULT_WEIGHTS')
        except Exception as e:
            print(f'FooPlayer.__init__: base_fn failed, falling back to contender_fn: {e}')
            try:
                self.value_fn = contender_fn(DEFAULT_WEIGHTS)
                print('FooPlayer.__init__: Using contender_fn as fallback')
            except Exception as inner:
                print(f'FooPlayer.__init__: contender_fn also failed, using dumb fallback. {inner}')
                self.value_fn = lambda g, c: 0.0

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

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

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

        Strategy enhancements and bug fixes:
        - Materialize any iterables into lists (avoid filter iterator bugs).
        - Use prune_robber_actions when appropriate.
        - Prioritize infrastructure actions (BUILD_SETTLEMENT, BUILD_ROAD, BUILD_CITY)
          over maritime trades when possible.

        Note: aggressive root/child pruning was intentionally removed to restore
        Evolution 2 behavior that achieved high win rates.
        """
        try:
            actions = list(playable_actions)
        except Exception:
            # playable_actions could be any iterable; ensure we can iterate it.
            actions = [a for a in playable_actions]

        if not actions:
            print('FooPlayer.decide: No playable actions available, returning None')
            return None

        # Reset debug counters
        self._node_expansions = 0

        # Detect and prune robber actions (safe check using name contains 'ROBBER')
        try:
            has_robber = any(
                getattr(a, 'action_type', None) is not None and
                'ROBBER' in getattr(a.action_type, 'name', '')
                for a in actions
            )
        except Exception:
            has_robber = False

        if has_robber:
            try:
                pruned = prune_robber_actions(self.color, game, actions)
                # Ensure pruned is a list; adapters should return a list but be defensive
                pruned = list(pruned) if pruned is not None else pruned
                if pruned and len(pruned) < len(actions):
                    print(f'FooPlayer.decide: Pruned robber actions from {len(actions)} to {len(pruned)}')
                    actions = pruned
            except Exception as e:
                print(f'FooPlayer.decide: prune_robber_actions failed: {e}')

        # Prioritize infrastructure actions. Strongly prefer BUILD_CITY but do not
        # exclude other infrastructure — evaluate cities first but keep settlements
        # and roads available as fallbacks so the agent remains flexible.
        try:
            infrastructure_types = {ActionType.BUILD_SETTLEMENT, ActionType.BUILD_ROAD, ActionType.BUILD_CITY}

            # Find any infrastructure actions among current candidates.
            infrastructure_actions = [a for a in actions if getattr(a, 'action_type', None) in infrastructure_types]

            # Extract explicit city-upgrade actions so we can prioritize them (but not
            # make them exclusive). We will evaluate city actions first by ordering.
            city_actions = [a for a in infrastructure_actions if getattr(a, 'action_type', None) == ActionType.BUILD_CITY]

            if city_actions:
                # Re-order actions so city upgrades are evaluated first, followed by
                # other infrastructure actions. This strongly biases selection toward
                # city upgrades while still allowing settlements/roads to be chosen
                # if they evaluate higher during full lookahead.
                ordered_infra = city_actions + [a for a in infrastructure_actions if a not in city_actions]
                print(f'FooPlayer.decide: Prioritizing {len(city_actions)} city upgrade(s) among {len(infrastructure_actions)} infrastructure actions')
                actions = ordered_infra
            elif infrastructure_actions:
                print(f'FooPlayer.decide: Prioritizing {len(infrastructure_actions)} infrastructure actions over {len(actions)} total')
                actions = infrastructure_actions
            else:
                # If no infrastructure actions, try to deprioritize maritime trades
                # when there are many options (reverted to Evolution 2 threshold >8).
                if len(actions) > 8:
                    non_trade_actions = [a for a in actions if getattr(a, 'action_type', None) != ActionType.MARITIME_TRADE]
                    if non_trade_actions:
                        print(f'FooPlayer.decide: Filtering out maritime trades from {len(actions)} to {len(non_trade_actions)} actions')
                        actions = non_trade_actions
        except Exception as e:
            print(f'FooPlayer.decide: Error during action prioritization: {e}')

        # Full evaluation of all remaining actions with lookahead (no aggressive pruning)
        best_action = None
        best_score = float('-inf')

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

        # Evaluate all candidate actions with full lookahead
        for idx, action in enumerate(actions):
            try:
                game_copy = copy_game(game)
                outcomes = execute_deterministic(game_copy, action)

                expected_value = 0.0
                for (outcome_game, prob) in outcomes:
                    node_value = self._evaluate_node(outcome_game, self.lookahead_depth - 1)
                    expected_value += prob * node_value

                # Small explicit bonus for city upgrades to further bias selection toward
                # upgrading settlements to cities (helps restore Evolution 2 behavior).
                try:
                    if getattr(action, 'action_type', None) == ActionType.BUILD_CITY:
                        # Add a modest bonus (tunable). We use an additive bonus so the
                        # heuristic scale from adapters continues to drive major decisions.
                        city_bonus = 50.0
                        expected_value += city_bonus
                        print(f'  Action {idx}: applied city bonus (+{city_bonus})')
                except Exception:
                    pass

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

                if expected_value > best_score:
                    best_score = expected_value
                    best_action = action

            except Exception as e:
                print(f'FooPlayer.decide: Exception while evaluating action {action}: {e}')

        # Fallback to the first original action if something went wrong
        chosen = best_action if best_action is not None else actions[0]
        print(f'FooPlayer.decide: Chosen action={chosen} score={best_score} node_expansions={self._node_expansions}')
        return chosen

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

        This function intentionally avoids aggressive child-pruning. It will
        still use list_prunned_actions and prune_robber_actions to reduce
        obviously irrelevant moves, but will otherwise recurse into all
        remaining legal/pruned actions so the search can find strong
        infrastructure lines.
        """
        # Update expansion counter for debugging / profiling
        self._node_expansions += 1

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

        # Get a pruned list of actions for this game state to reduce branching.
        try:
            actions = list_prunned_actions(game)
            # Make sure we have a materialized list
            actions = list(actions) if actions is not None else []
        except Exception as e:
            print(f'FooPlayer._evaluate_node: list_prunned_actions failed: {e}')
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        if not actions:
            try:
                return float(self.value_fn(game, self.color))
            except Exception:
                return 0.0

        # If robber actions are present for the current actor, prune them.
        try:
            has_robber = any(
                getattr(a, 'action_type', None) is not None and
                'ROBBER' in getattr(a.action_type, 'name', '')
                for a in actions
            )
        except Exception:
            has_robber = False

        if has_robber:
            try:
                current_color = actions[0].color
                pruned = prune_robber_actions(current_color, game, actions)
                pruned = list(pruned) if pruned is not None else pruned
                if pruned:
                    # Only accept pruning if it meaningfully reduces branching
                    if len(pruned) < len(actions):
                        print(f'FooPlayer._evaluate_node: Pruned robber actions from {len(actions)} to {len(pruned)}')
                        actions = pruned
            except Exception as e:
                print(f'FooPlayer._evaluate_node: prune_robber_actions failed: {e}')

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

        best_value = float('-inf') if is_maximizing else float('inf')

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

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

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

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

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

        return best_value

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

This is the current adapters.py file

"""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 =================================


ANALYZER OBJECTIVE:

Start with: "After Running The New foo_player.py Player, Here is my analysis and findings:"

If the game failed to compile/run (no game_results JSON or score==0):
- ERROR SUMMARY:
  - First error line (verbatim), exception type, file, exact line number, and the exact code line (from game_output.txt).
- LIKELY CAUSE (1–2 bullets): short hypothesis based on the error/log text (e.g., unknown ActionType, bad import, attribute missing).
- QUICK FIX FOCUS: 1–2 bullets pointing to the specific function/line in foo_player.py (or adapters.py) to inspect.

If the game ran (game_results JSON present):
1) PERFORMANCE SUMMARY:
   - Outcome (Win/Loss), our VP vs opponent VP, VP diff.
   - Key counts: cities, settlements, roads, dev cards (if available), total turns.
2) VERDICT:
   - Good if Win OR VP diff ≥ +0.5
   - Borderline if −0.5 < VP diff < +0.5
   - Poor if Loss OR VP diff ≤ −0.5
3) IF BORDERLINE/POOR — LIKELY REASONS:
   - Briefly scan foo_player.py and list 2–4 concrete issues with short citations (line numbers/snippets), prioritizing:
     - Missing 1-ply value lookahead (no `copy_game` + `make_value_fn` usage).
     - No chance handling (dice/dev/robber), or robber/knight policy absent.
     - Placement helpers stubbed/always False (roads/settlements).
     - No end-turn policy or repeated random selection.
     - Illegal/unknown actions (e.g., trying to play `VICTORY_POINT`).
   - Pull 2–4 corroborating log lines from game_output.txt (e.g., "Unrecognized action type", "Defaulting to Random Action", stack traces).
4) NEXT STEP (one line):
   - Clear route like: "Send to Coder to add 1-ply value lookahead", or "Send to Strategizer to specify robber/placement policy", etc.

End with: "Let me know if you need anything else".


--- Output from ANALYZER at 20250925_044117 ---
================================== Ai Message ==================================

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

---
### **PERFORMANCE SUMMARY**
- **Outcome**: **Poor** (9 wins, 21 losses).
- **VP Comparison**: FooPlayer (6.97 VP) vs. Opponent (9.0 VP), **VP Diff: -2.03**.
- **Key Counts**:
  - Settlements: 2.57
  - Cities: 1.33
  - Roads: 0.5
  - Dev Cards: 0.47
  - Total Turns: 76.7

---
### **VERDICT**
**Poor**: FooPlayer lost 21/30 games with a significant VP deficit of -2.03. The player struggles with infrastructure development and long-term strategy.

---
### **LIKELY REASONS**
1. **Overvaluation of Maritime Trades**:
   - Despite prioritizing infrastructure, the player still selects `MARITIME_TRADE` actions frequently, even when higher-value actions (e.g., `BUILD_ROAD`, `BUILD_SETTLEMENT`) are available.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'BRICK')) score=1799999986138444.0 node_expansions=77
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'BRICK')) score=1799999986140300.2 node_expansions=71
     ```
   - This suggests the **value function (`base_fn`)** may still overvalue short-term resource gains.

2. **Insufficient Road Expansion**:
   - The player builds **fewer roads (0.5 avg)** compared to the opponent (0.5 avg), limiting expansion and resource access.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Prioritizing 17 infrastructure actions over 18 total
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(19, 21)) score=1799999986141967.8 node_expansions=51
     ```
   - While roads are prioritized, the player fails to execute them consistently.

3. **Aggressive Robber Pruning**:
   - The player prunes robber actions aggressively, reducing its ability to strategically place the robber or play knights.
   - **Log Evidence**:
     ```
     FooPlayer.decide: Pruned robber actions from 18 to 1
     FooPlayer._evaluate_node: Pruned robber actions from 18 to 1
     ```
   - This limits the player's ability to disrupt opponents or secure resources.

4. **Excessive Node Expansions for Low-Impact Actions**:
   - The player spends excessive computational effort evaluating low-impact actions (e.g., `MARITIME_TRADE`).
   - **Log Evidence**:
     ```
     FooPlayer.decide: Fully evaluating 5 actions with lookahead depth=3
       Action 0: expected_value=1799999952808647.0 action_type=ActionType.END_TURN
       Action 1: expected_value=1799999986141966.2 action_type=ActionType.MARITIME_TRADE
       Action 2: expected_value=1799999986141979.8 action_type=ActionType.MARITIME_TRADE
       Action 3: expected_value=1799999986141963.2 action_type=ActionType.MARITIME_TRADE
       Action 4: expected_value=1799999986141990.2 action_type=ActionType.MARITIME_TRADE
     FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WOOD', 'WOOD', 'WOOD', 'WOOD', 'WHEAT')) score=1799999986141990.2 node_expansions=39
     ```

---
### **STRATEGIC FLAW**
The player's **value function (`base_fn`)** and **action prioritization logic** are misaligned, leading to excessive focus on short-term resource trades and insufficient investment in road expansion and disruptive actions (e.g., robber/knight).

---
### **NEXT STEP**
Send to **Strategizer** to **retune the value function (`base_fn`)** to prioritize road expansion and robber/knight actions over maritime trades, and **adjust the pruning logic** to avoid over-aggressive filtering of robber actions.

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

