--- Input for ANALYZER at 20250925_052334 ---
================================ 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:BLUE averaged **2.0 VP** vs. AlphaBetaPlayer:RED's **10.0 VP** (VP diff: **-8.0**).
- **Key Counts**:
  - Settlements: 2.0 (FooPlayer) vs. 2.83 (Opponent)
  - Cities: 0.0 (FooPlayer) vs. 2.57 (Opponent)
  - Roads: 0.0 (FooPlayer) vs. 1.0 (Opponent)
  - Dev Cards: 0.0 (FooPlayer) vs. 0.03 (Opponent)
  - Total Turns: 59.8

---
### **VERDICT**
**Poor**: The player lost all games and scored significantly lower than the opponent.

---
### **LIKELY REASONS**
1. **No Strategic Decision-Making**:
   - The player always selects the first action in `playable_actions` without evaluating its potential outcomes or long-term rewards.
   - **Code Citation**: `return playable_actions[0]` (Line 25, `foo_player.py`).
   - **Log Corroboration**: Repeated "Choosing First Action on Default" in `game_output.txt`.

2. **Lack of Game State Evaluation**:
   - No use of `adapters.py` utilities (e.g., `copy_game`, `execute_deterministic`, `base_fn`) to simulate or evaluate actions.
   - **Code Citation**: No calls to `copy_game` or value functions in `foo_player.py`.

3. **No Lookahead or Chance Handling**:
   - The player does not simulate future game states or account for probabilistic outcomes (e.g., dice rolls, dev cards, robber placement).

4. **No Placement or Development Strategy**:
   - The player does not prioritize building settlements, cities, or roads, nor does it handle development cards or the robber.

---
### **STRATEGIC FLAW**
The player's logic is entirely reactive and naive, always choosing the first available action without any evaluation of its impact on the game state or long-term strategy.

---
### **NEXT STEP**
Send to **Coder** to implement a **1-ply value lookahead** using `copy_game` and `base_fn` to evaluate actions strategically.

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

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

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

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

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

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

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

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

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

This is the current performance history

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

This is the current game_output.txt file

_ROBBER'>, value=((-2, 0, 2), <Color.RED: 'RED'>, None)) => expected value 1500000001696322.5
FooPlayer: Top-level Action #12 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 1, 1), None, None)) => expected value 1499999980585558.5
FooPlayer: Top-level Action #13 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 2, 0), None, None)) => expected value 1499999938918891.8
FooPlayer: Top-level Action #14 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 2, -2), None, None)) => expected value 1499999938918003.0
FooPlayer: Top-level Action #15 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((1, 1, -2), <Color.RED: 'RED'>, None)) => expected value 1500000006696322.5
FooPlayer: Top-level Action #16 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((2, 0, -2), None, None)) => expected value 1500000005585558.5
FooPlayer: Top-level Action #17 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((2, -1, -1), <Color.RED: 'RED'>, None)) => expected value 1500000001696322.5
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, -1, 1), <Color.RED: 'RED'>, None)) with value 1500000053362989.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000111141129.5
FooPlayer: Top-level Action #1 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'SHEEP')) => expected value 1500000111141122.0
FooPlayer: Top-level Action #2 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'WHEAT')) => expected value 1500000111141119.5
FooPlayer: Top-level Action #3 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'ORE')) => expected value 1500000111141119.5
FooPlayer: Top-level Action #4 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('BRICK', 'BRICK', 'BRICK', 'BRICK', 'WOOD')) => expected value 1500000111141122.0
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) with value 1500000111141129.5
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) => expected value 1525000113224717.0
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) with value 1525000113224717.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) => expected value 1499999966695802.0
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) with value 1499999966695802.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), None, None)) => expected value 1500000005585555.0
FooPlayer: Top-level Action #1 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((1, -1, 0), <Color.RED: 'RED'>, None)) => expected value 1499999985029297.0
FooPlayer: Top-level Action #2 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-1, 0, 1), <Color.RED: 'RED'>, None)) => expected value 1499999973362630.5
FooPlayer: Top-level Action #3 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-1, 1, 0), None, None)) => expected value 1499999891694888.2
FooPlayer: Top-level Action #4 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 1, -1), <Color.RED: 'RED'>, None)) => expected value 1499999966695786.0
FooPlayer: Top-level Action #5 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((1, 0, -1), <Color.RED: 'RED'>, None)) => expected value 1499999980029297.0
FooPlayer: Top-level Action #6 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((2, -2, 0), <Color.RED: 'RED'>, None)) => expected value 1499999978362630.5
FooPlayer: Top-level Action #7 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((1, -2, 1), <Color.RED: 'RED'>, None)) => expected value 1499999986140408.0
FooPlayer: Top-level Action #8 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, -2, 2), <Color.RED: 'RED'>, None)) => expected value 1499999993918186.0
FooPlayer: Top-level Action #9 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-1, -1, 2), <Color.RED: 'RED'>, None)) => expected value 1499999992807074.8
FooPlayer: Top-level Action #10 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 0, 2), <Color.RED: 'RED'>, None)) => expected value 1499999978362630.5
FooPlayer: Top-level Action #11 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 1, 1), None, None)) => expected value 1499999980585555.0
FooPlayer: Top-level Action #12 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 2, 0), None, None)) => expected value 1499999938918888.2
FooPlayer: Top-level Action #13 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-1, 2, -1), None, None)) => expected value 1499999861139332.8
FooPlayer: Top-level Action #14 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 2, -2), None, None)) => expected value 1499999938917999.5
FooPlayer: Top-level Action #15 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((1, 1, -2), <Color.RED: 'RED'>, None)) => expected value 1499999980029297.0
FooPlayer: Top-level Action #16 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((2, 0, -2), None, None)) => expected value 1500000005585555.0
FooPlayer: Top-level Action #17 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((2, -1, -1), <Color.RED: 'RED'>, None)) => expected value 1499999978362630.5
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), None, None)) with value 1500000005585555.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000005585561.0
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) with value 1500000005585561.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) => expected value 1525000007669153.5
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) with value 1525000007669153.5
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000005585574.0
FooPlayer: Top-level Action #1 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WHEAT')) => expected value 1500000005585575.5
FooPlayer: Top-level Action #2 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WHEAT')) => expected value 1500000005585570.0
FooPlayer: Top-level Action #3 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'ORE')) => expected value 1500000005585567.5
FooPlayer: Top-level Action #4 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WOOD')) => expected value 1500000005587778.2
FooPlayer: Top-level Action #5 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'SHEEP')) => expected value 1500000005585570.0
FooPlayer: Top-level Action #6 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'BRICK')) => expected value 1500000005585557.5
FooPlayer: Top-level Action #7 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WOOD')) => expected value 1500000005587770.8
FooPlayer: Top-level Action #8 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'BRICK')) => expected value 1500000005585557.5
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('SHEEP', 'SHEEP', 'SHEEP', 'SHEEP', 'WOOD')) with value 1500000005587778.2
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000005585571.5
FooPlayer: Top-level Action #1 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(45, 46)) => expected value 1500000005587389.2
FooPlayer: Top-level Action #2 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(3, 4)) => expected value 1500000005586556.0
FooPlayer: Top-level Action #3 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(37, 38)) => expected value 1500000005587111.5
FooPlayer: Top-level Action #4 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(18, 40)) => expected value 1500000005587778.2
FooPlayer: Top-level Action #5 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(19, 20)) => expected value 1500000005585556.0
FooPlayer: Top-level Action #6 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(4, 5)) => expected value 1500000005586556.0
FooPlayer: Top-level Action #7 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(46, 48)) => expected value 1500000005586556.0
FooPlayer: Top-level Action #8 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(16, 21)) => expected value 1500000005585556.0
FooPlayer: Top-level Action #9 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(16, 18)) => expected value 1500000005585556.0
FooPlayer: Top-level Action #10 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(21, 43)) => expected value 1500000005587778.2
FooPlayer: Top-level Action #11 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(39, 41)) => expected value 1500000005587389.2
FooPlayer: Top-level Action #12 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(14, 15)) => expected value 1500000005585556.0
FooPlayer: Top-level Action #13 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WOOD')) => expected value 1500000005587767.8
FooPlayer: Top-level Action #14 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'SHEEP')) => expected value 1500000005587767.8
FooPlayer: Top-level Action #15 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'BRICK')) => expected value 1500000005587755.2
FooPlayer: Top-level Action #16 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WHEAT')) => expected value 1500000005587777.8
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(18, 40)) with value 1500000005587778.2
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000005587783.2
FooPlayer: Top-level Action #1 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WOOD')) => expected value 1500000005589574.0
FooPlayer: Top-level Action #2 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'SHEEP')) => expected value 1500000005587767.8
FooPlayer: Top-level Action #3 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'BRICK')) => expected value 1500000005587755.2
FooPlayer: Top-level Action #4 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WHEAT')) => expected value 1500000005587777.8
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('ORE', 'ORE', 'ORE', 'ORE', 'WOOD')) with value 1500000005589574.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000005587776.5
FooPlayer: Top-level Action #1 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(45, 46)) => expected value 1500000005589574.0
FooPlayer: Top-level Action #2 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(3, 4)) => expected value 1500000005588740.8
FooPlayer: Top-level Action #3 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(40, 42)) => expected value 1500000005588740.8
FooPlayer: Top-level Action #4 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(37, 38)) => expected value 1500000005589296.2
FooPlayer: Top-level Action #5 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(19, 20)) => expected value 1500000005587740.8
FooPlayer: Top-level Action #6 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(4, 5)) => expected value 1500000005588740.8
FooPlayer: Top-level Action #7 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(40, 44)) => expected value 1500000005588740.8
FooPlayer: Top-level Action #8 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(46, 48)) => expected value 1500000005588740.8
FooPlayer: Top-level Action #9 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(16, 21)) => expected value 1500000005587740.8
FooPlayer: Top-level Action #10 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(16, 18)) => expected value 1500000005587740.8
FooPlayer: Top-level Action #11 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(21, 43)) => expected value 1500000005588574.0
FooPlayer: Top-level Action #12 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(39, 41)) => expected value 1500000005588740.8
FooPlayer: Top-level Action #13 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(14, 15)) => expected value 1500000005587740.8
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_ROAD: 'BUILD_ROAD'>, value=(45, 46)) with value 1500000005589574.0
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1500000005589585.2
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) with value 1500000005589585.2
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) => expected value 1499999878736092.2
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) with value 1499999878736092.2
FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) => expected value 1499999861143349.5
FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) with value 1499999861143349.5
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 │ BLUE,RED │    87 │     10 │       6 │ RED     
  22 │ BLUE,RED │    75 │     10 │       5 │ RED     
  23 │ BLUE,RED │    89 │     11 │       4 │ RED     
  24 │ RED,BLUE │    62 │     10 │       6 │ RED     
  25 │ RED,BLUE │    72 │     10 │       6 │ RED     
  26 │ BLUE,RED │    80 │      4 │      10 │ BLUE    
  27 │ RED,BLUE │   114 │     10 │       9 │ RED     
  28 │ BLUE,RED │    93 │     10 │       8 │ RED     
  29 │ RED,BLUE │   101 │      7 │      10 │ BLUE    
  30 │ RED,BLUE │    64 │     10 │       5 │ RED     
     ╵          ╵       ╵        ╵         ╵         
                                                                Player Summary                                                                
                                                               ╷      ╷        ╷             ╷            ╷          ╷          ╷             
                                                               │ WINS │ AVG VP │ AVG SETTLES │ AVG CITIES │ AVG ROAD │ AVG ARMY │ AVG DEV VP  
╶──────────────────────────────────────────────────────────────┼──────┼────────┼─────────────┼────────────┼──────────┼──────────┼────────────╴
  AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False) │   21 │   8.87 │        2.80 │       2.53 │     0.50 │     0.00 │       0.00  
  FooPlayer:BLUE                                               │    9 │   7.17 │        2.63 │       1.77 │     0.50 │     0.00 │       0.00  
                                                               ╵      ╵        ╵             ╵            ╵          ╵          ╵             
              Game Summary              
            ╷           ╷               
  AVG TICKS │ AVG TURNS │ AVG DURATION  
╶───────────┼───────────┼──────────────╴
     216.80 │     74.97 │   3.269 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": 8.866666666666667,
      "AVG SETTLES": 2.8,
      "AVG CITIES": 2.533333333333333,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 9,
      "AVG VP": 7.166666666666667,
      "AVG SETTLES": 2.6333333333333333,
      "AVG CITIES": 1.7666666666666666,
      "AVG ROAD": 0.5,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    }
  },
  "Game Summary": {
    "AVG TICKS": 216.8,
    "AVG TURNS": 74.96666666666667,
    "AVG DURATION": 3.2687970797220864
  }
}
================================ Human Message =================================

This is the current foo_player.py file

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

import math
from typing import List


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

                        if expected > best_value:
                            best_value = expected

                    return best_value

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

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

                        if expected < worst_value:
                            worst_value = expected

                    return worst_value

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

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

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

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

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

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

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

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

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

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

================================ 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_052355 ---
================================== Ai Message ==================================
Tool Calls:
  read_local_file (AOPIVx3wF)
 Call ID: AOPIVx3wF
  Args:
    rel_path: game_20250925_052153_fg/foo_player.py
================================= Tool Message =================================
Name: read_local_file

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

import math
from typing import List


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

                        if expected > best_value:
                            best_value = expected

                    return best_value

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

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

                        if expected < worst_value:
                            worst_value = expected

                    return worst_value

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

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

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

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

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

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

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

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

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

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

================================== 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:BLUE averaged **7.17 VP** vs. AlphaBetaPlayer:RED's **8.87 VP** (VP diff: **-1.7**).
- **Key Counts**:
  - Settlements: 2.63 (FooPlayer) vs. 2.80 (Opponent)
  - Cities: 1.77 (FooPlayer) vs. 2.53 (Opponent)
  - Roads: 0.50 (FooPlayer) vs. 0.50 (Opponent)
  - Dev Cards: 0.0 (FooPlayer) vs. 0.0 (Opponent)
  - Total Turns: 74.97

---
### **VERDICT**
**Poor**: The player is underperforming significantly, with a VP deficit of -1.7 and only 30% win rate.

---
### **LIKELY REASONS**
1. **Shallow Search Depth**:
   - The player uses a **2-ply Expectimax search**, which is insufficient for long-term strategic planning.
   - **Code Citation**: `MAX_DEPTH = 2` (Line 35, `foo_player.py`).
   - **Log Corroboration**: Actions are evaluated with limited foresight (e.g., "FooPlayer: Top-level Action #0 = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) => expected value 1499999878736092.2").

2. **No Specialized Robber/Knight Strategy**:
   - The player evaluates robber actions but lacks a targeted strategy to disrupt the opponent or optimize resource denial.
   - **Log Corroboration**: Robber actions are chosen based on generic expected value (e.g., "FooPlayer: Chosen action = Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((0, 0, 0), None, None)) with value 1500000005585555.0").

3. **No Development Card Optimization**:
   - The player does not prioritize or strategically use development cards (e.g., "MONOPOLY", "ROAD_BUILDING", "YEAR_OF_PLENTY").
   - **Log Corroboration**: No evidence of development card actions in logs.

4. **No Adaptive Heuristic**:
   - The player relies solely on the `base_fn` heuristic, which may not capture nuanced strategic opportunities (e.g., road placement, settlement expansion).
   - **Code Citation**: `value_fn = base_fn()` (Line 38, `foo_player.py`).

---
### **STRATEGIC FLAW**
The player's **2-ply search depth** and **generic heuristic** limit its ability to plan strategically, especially for long-term goals like city upgrades, road networks, and development card usage.

---
### **NEXT STEP**
Send to **Strategizer** to design a **deeper search strategy** (e.g., 3-4 ply) and **custom heuristics** for robber placement, development cards, and adaptive road/settlement expansion.

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

