--- Input for ANALYZER at 20250925_042558 ---
================================ 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 =================================

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"
  }
}
================================ Human Message =================================

This is the current game_output.txt file

811481.2 node_expansions=28
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999927809382.8 action_type=ActionType.DISCARD
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002810282.8 action_type=ActionType.DISCARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) score=1200000002810282.8 node_expansions=3
FooPlayer.decide: Pruned robber actions from 18 to 1
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810272.8 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002810276.2 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 1, 1), <Color.RED: 'RED'>, None)) score=1200000002810276.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810262.8 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002810286.2 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000002810286.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810297.2 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1500000027806748.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1500000027806748.2 node_expansions=20
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810303.2 action_type=ActionType.END_TURN
  quick_score=1200000002810282.8 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Fully evaluating 2 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 19 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000002810316.8 action_type=ActionType.END_TURN
  Action 1: expected_value=1200000002810296.2 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=1200000002810316.8 node_expansions=10
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809205.2 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000027809185.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000027809185.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809204.2 action_type=ActionType.END_TURN
  quick_score=1200000002809196.2 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Fully evaluating 2 top actions with lookahead depth=3
  Action 0: expected_value=1200000002809205.2 action_type=ActionType.END_TURN
  Action 1: expected_value=1200000002809192.2 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=1200000002809205.2 node_expansions=12
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809205.2 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1500000027806814.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1500000027806814.2 node_expansions=16
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809206.2 action_type=ActionType.END_TURN
  quick_score=1200000002809203.2 action_type=ActionType.BUY_DEVELOPMENT_CARD
  quick_score=1200000002809183.2 action_type=ActionType.MARITIME_TRADE
  quick_score=1200000002809180.8 action_type=ActionType.MARITIME_TRADE
  quick_score=1200000002809170.8 action_type=ActionType.MARITIME_TRADE
  quick_score=1200000002809170.8 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Fully evaluating 6 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 19 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000002809219.8 action_type=ActionType.END_TURN
  Action 1: expected_value=1200000002809216.8 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer._evaluate_node: Branching 17 -> 5 via cheap pre-eval
  Action 2: expected_value=1200000002811284.8 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1200000002809180.8 action_type=ActionType.MARITIME_TRADE
FooPlayer._evaluate_node: Branching 17 -> 5 via cheap pre-eval
  Action 4: expected_value=1200000002811269.2 action_type=ActionType.MARITIME_TRADE
  Action 5: expected_value=1200000002809182.8 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WHEAT', 'WHEAT', 'WHEAT', 'WHEAT', 'WOOD')) score=1200000002811284.8 node_expansions=61
FooPlayer.decide: Prioritizing 16 infrastructure actions over 17 total
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811284.8 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810729.2 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810562.5 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810451.5 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810284.8 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810173.8 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Fully evaluating 6 top actions with lookahead depth=3
  Action 0: expected_value=1200000002811293.2 action_type=ActionType.BUILD_ROAD
  Action 1: expected_value=1200000002810725.2 action_type=ActionType.BUILD_ROAD
  Action 2: expected_value=1200000002810562.5 action_type=ActionType.BUILD_ROAD
  Action 3: expected_value=1200000002810449.5 action_type=ActionType.BUILD_ROAD
  Action 4: expected_value=1200000002810284.8 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1200000002810182.2 action_type=ActionType.BUILD_ROAD
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
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811284.8 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002811280.8 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000002811280.8 node_expansions=8
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811282.8 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 9 -> 5 via cheap pre-eval
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000002813114.0 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000002813114.0 node_expansions=29
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811282.8 action_type=ActionType.DISCARD
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000005589059.0 action_type=ActionType.DISCARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) score=1200000005589059.0 node_expansions=3
FooPlayer.decide: Pruned robber actions from 18 to 1
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589059.0 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000005589069.5 action_type=ActionType.MOVE_ROBBER
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
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589059.0 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000022255724.8 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000022255724.8 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589091.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 17 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000005590883.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000005590883.8 node_expansions=20
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589072.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 10 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000005589070.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000005589070.5 node_expansions=7
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589071.5 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002811305.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000002811305.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589068.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000005589069.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000005589069.5 node_expansions=9
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589049.5 action_type=ActionType.DISCARD
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000022255712.8 action_type=ActionType.DISCARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) score=1200000022255712.8 node_expansions=5
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900031226.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002809892.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000002809892.8 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900031216.0 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 10 -> 5 via cheap pre-eval
  Action 0: expected_value=1199999900031239.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1199999900031239.5 node_expansions=9
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900030116.5 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1199999900030117.5 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1199999900030117.5 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900030116.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1199999900030117.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1199999900030117.5 node_expansions=4
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900030141.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 19 -> 5 via cheap pre-eval
  Action 0: expected_value=1499999936141080.5 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1499999936141080.5 node_expansions=11
FooPlayer.decide: Prioritizing 18 infrastructure actions over 19 total
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141080.5 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1499999919472414.0 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1499999908360580.8 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1499999902803913.8 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1199999900032505.5 action_type=ActionType.BUILD_ROAD
  quick_score=1199999900031949.8 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Fully evaluating 6 top actions with lookahead depth=3
  Action 0: expected_value=1499999936141081.5 action_type=ActionType.BUILD_SETTLEMENT
  Action 1: expected_value=1499999919472448.5 action_type=ActionType.BUILD_SETTLEMENT
  Action 2: expected_value=1499999908360604.2 action_type=ActionType.BUILD_SETTLEMENT
  Action 3: expected_value=1499999902803927.2 action_type=ActionType.BUILD_SETTLEMENT
  Action 4: expected_value=1199999900032521.0 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1199999900031949.8 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_SETTLEMENT: 'BUILD_SETTLEMENT'>, value=49) score=1499999936141081.5 node_expansions=18
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141080.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1499999936141081.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1499999936141081.5 node_expansions=7
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141096.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1500000027807762.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1500000027807762.8 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141105.0 action_type=ActionType.MARITIME_TRADE
  quick_score=1499999936141096.0 action_type=ActionType.END_TURN
  quick_score=1499999936141095.0 action_type=ActionType.MARITIME_TRADE
  quick_score=1499999936141092.5 action_type=ActionType.MARITIME_TRADE
  quick_score=1499999936141082.5 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Fully evaluating 5 top actions with lookahead depth=3
  Action 0: expected_value=1499999936141105.0 action_type=ActionType.MARITIME_TRADE
FooPlayer._evaluate_node: Branching 10 -> 5 via cheap pre-eval
  Action 1: expected_value=1499999936141119.5 action_type=ActionType.END_TURN
  Action 2: expected_value=1499999936141096.0 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1499999936141127.0 action_type=ActionType.MARITIME_TRADE
  Action 4: expected_value=1499999936141082.5 action_type=ActionType.MARITIME_TRADE
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
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141092.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 14 -> 5 via cheap pre-eval
  Action 0: expected_value=1499999936141106.0 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1499999936141106.0 node_expansions=7
Playing 30 games...                                          ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸       87%        
FooPlayer:BLUE                                               ━━━━━                                     13%        
                    Last 10 Games                    
     ╷          ╷       ╷        ╷         ╷         
   # │ SEATING  │ TURNS │ RED VP │ BLUE VP │ WINNER  
╶────┼──────────┼───────┼────────┼─────────┼────────╴
  21 │ BLUE,RED │   103 │     10 │       8 │ RED     
  22 │ BLUE,RED │    67 │     11 │       6 │ RED     
  23 │ RED,BLUE │    74 │     11 │       7 │ RED     
  24 │ RED,BLUE │    96 │     10 │       8 │ RED     
  25 │ BLUE,RED │    59 │     10 │       5 │ RED     
  26 │ RED,BLUE │    46 │     10 │       2 │ RED     
  27 │ RED,BLUE │    82 │     10 │       5 │ RED     
  28 │ RED,BLUE │    56 │     10 │       3 │ RED     
  29 │ RED,BLUE │    76 │     10 │       4 │ RED     
  30 │ RED,BLUE │    90 │     10 │       5 │ 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) │   26 │ 9.40 │ 2.33 │ 2.83 │  0.70 │ 0.00 │  0.00  
  FooPlayer:BLUE                                               │    4 │ 5.60 │ 2.20 │ 1.17 │  0.30 │ 0.10 │  0.27  
                                                               ╵      ╵      ╵      ╵      ╵       ╵      ╵        
              Game Summary              
            ╷           ╷               
  AVG TICKS │ AVG TURNS │ AVG DURATION  
╶───────────┼───────────┼──────────────╴
     212.77 │     74.37 │   2.553 secs  
            ╵           ╵
================================ Human Message =================================

This is the current game_results json file

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 26,
      "AVG VP": 9.4,
      "AVG SETTLES": 2.3333333333333335,
      "AVG CITIES": 2.8333333333333335,
      "AVG ROAD": 0.7,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 4,
      "AVG VP": 5.6,
      "AVG SETTLES": 2.2,
      "AVG CITIES": 1.1666666666666667,
      "AVG ROAD": 0.3,
      "AVG ARMY": 0.1,
      "AVG DEV VP": 0.26666666666666666
    }
  },
  "Game Summary": {
    "AVG TICKS": 212.76666666666668,
    "AVG TURNS": 74.36666666666666,
    "AVG DURATION": 2.553160214424133
  }
}
================================ 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 contender_fn (with DEFAULT_WEIGHTS) as the primary heuristic to bias
      evaluation toward infrastructure. Falls back to base_fn on failure.
    - 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.

    Notes about this update (bugs fixed / performance improvements):
    - Fixed bugs caused by using Python's filter() without materializing into
      a list. All filtering uses list comprehensions so len() and indexing work.
    - Added a cheap "quick" heuristic pre-evaluation to rank actions and only
      fully expand the top-k candidate actions. This reduces node expansions
      drastically while preserving the depth-3 lookahead on the most
      promising moves.
    - 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)

        # Prefer contender_fn to bias toward infrastructure.
        try:
            self.value_fn = contender_fn(DEFAULT_WEIGHTS)
            print('FooPlayer.__init__: Using contender_fn with DEFAULT_WEIGHTS')
        except Exception as e:
            print(f'FooPlayer.__init__: contender_fn failed, falling back to base_fn: {e}')
            try:
                self.value_fn = base_fn()
                print('FooPlayer.__init__: Using base_fn as fallback')
            except Exception as inner:
                print(f'FooPlayer.__init__: base_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

        # Tunable pruning parameters to limit branching and reduce node expansions.
        # Keep conservative defaults so we don't lose good actions.
        self.max_root_expansions = 6  # number of candidate actions to fully expand at root
        self.max_child_expansions = 5  # number of actions to expand at inner nodes when branching is large

    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.
        - Perform a cheap pre-evaluation (quick heuristic) of actions and only
          fully search the top-k to reduce node expansions.
        """
        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 over maritime trades and other low
        # value actions. If we have any infrastructure actions, focus on them.
        try:
            infrastructure_types = {ActionType.BUILD_SETTLEMENT, ActionType.BUILD_ROAD, ActionType.BUILD_CITY}
            infrastructure_actions = [a for a in actions if getattr(a, 'action_type', None) in infrastructure_types]
            if 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 (to avoid repeatedly choosing trades).
                if len(actions) > 6:
                    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}')

        # If there are still many actions, use a cheap pre-evaluation to select
        # the top-k candidate actions to fully evaluate with lookahead.
        try:
            candidate_actions = actions
            # Quick scoring: evaluate the immediate resulting states with the heuristic
            quick_scores = []  # list of (action, score)
            for action in candidate_actions:
                try:
                    game_copy = copy_game(game)
                    outcomes = execute_deterministic(game_copy, action)
                    quick_value = 0.0
                    for (outcome_game, prob) in outcomes:
                        try:
                            quick_value += prob * float(self.value_fn(outcome_game, self.color))
                        except Exception:
                            quick_value += prob * 0.0
                    quick_scores.append((action, quick_value))
                except Exception as e:
                    # If quick evaluation fails, push a very low score so it is deprioritized
                    print(f'FooPlayer.decide: quick evaluation failed for action {action}: {e}')
                    quick_scores.append((action, float('-inf')))

            # Sort candidate actions by quick score descending
            quick_scores.sort(key=lambda x: x[1], reverse=True)

            # Determine how many to fully expand at root
            max_expand = min(len(quick_scores), self.max_root_expansions)
            top_actions = [a for (a, _) in quick_scores[:max_expand]]

            # Debug: print quick scores for transparency
            print('FooPlayer.decide: Quick action scores (top->low):')
            for a, s in quick_scores[:max_expand]:
                print(f'  quick_score={s} action_type={getattr(a, "action_type", None)}')

        except Exception as e:
            # If quick pre-eval fails for any reason, just evaluate all actions (safe fallback)
            print(f'FooPlayer.decide: Quick pre-evaluation failed: {e}')
            top_actions = actions

        best_action = None
        best_score = float('-inf')

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

        # Evaluate the top candidate actions with full lookahead
        for idx, action in enumerate(top_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

                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 includes a lightweight child-pruning mechanism: when the
        branching factor is large we do a cheap heuristic evaluation of the
        children and only recurse into the best few. This reduces node
        expansions while still searching the most promising 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)
        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}')

        # If branching is large, do a cheap pre-evaluation of children and keep top-k
        try:
            branching_limit = 8
            if len(actions) > branching_limit:
                child_scores = []
                for a in actions:
                    try:
                        gc = copy_game(game)
                        outcomes = execute_deterministic(gc, a)
                        q = 0.0
                        for (og, prob) in outcomes:
                            try:
                                q += prob * float(self.value_fn(og, self.color))
                            except Exception:
                                q += prob * 0.0
                        child_scores.append((a, q))
                    except Exception as e:
                        child_scores.append((a, float('-inf')))

                # Keep only the top child actions to recurse into
                child_scores.sort(key=lambda x: x[1], reverse=True)
                max_children = min(len(child_scores), self.max_child_expansions)
                top_children = [a for (a, _) in child_scores[:max_children]]
                print(f'FooPlayer._evaluate_node: Branching {len(actions)} -> {len(top_children)} via cheap pre-eval')
                actions = top_children
        except Exception as e:
            print(f'FooPlayer._evaluate_node: Error during child pre-eval pruning: {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_042626 ---
================================== Ai Message ==================================
Tool Calls:
  read_local_file (M4fsLdDBy)
 Call ID: M4fsLdDBy
  Args:
    rel_path: game_20250925_042440_fg/game_output.txt
================================= Tool Message =================================
Name: read_local_file

811481.2 node_expansions=28
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999927809382.8 action_type=ActionType.DISCARD
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002810282.8 action_type=ActionType.DISCARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) score=1200000002810282.8 node_expansions=3
FooPlayer.decide: Pruned robber actions from 18 to 1
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810272.8 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002810276.2 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MOVE_ROBBER: 'MOVE_ROBBER'>, value=((-2, 1, 1), <Color.RED: 'RED'>, None)) score=1200000002810276.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810262.8 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002810286.2 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000002810286.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810297.2 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1500000027806748.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1500000027806748.2 node_expansions=20
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002810303.2 action_type=ActionType.END_TURN
  quick_score=1200000002810282.8 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Fully evaluating 2 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 19 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000002810316.8 action_type=ActionType.END_TURN
  Action 1: expected_value=1200000002810296.2 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=1200000002810316.8 node_expansions=10
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809205.2 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000027809185.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000027809185.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809204.2 action_type=ActionType.END_TURN
  quick_score=1200000002809196.2 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer.decide: Fully evaluating 2 top actions with lookahead depth=3
  Action 0: expected_value=1200000002809205.2 action_type=ActionType.END_TURN
  Action 1: expected_value=1200000002809192.2 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=1200000002809205.2 node_expansions=12
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809205.2 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1500000027806814.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1500000027806814.2 node_expansions=16
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002809206.2 action_type=ActionType.END_TURN
  quick_score=1200000002809203.2 action_type=ActionType.BUY_DEVELOPMENT_CARD
  quick_score=1200000002809183.2 action_type=ActionType.MARITIME_TRADE
  quick_score=1200000002809180.8 action_type=ActionType.MARITIME_TRADE
  quick_score=1200000002809170.8 action_type=ActionType.MARITIME_TRADE
  quick_score=1200000002809170.8 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Fully evaluating 6 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 19 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000002809219.8 action_type=ActionType.END_TURN
  Action 1: expected_value=1200000002809216.8 action_type=ActionType.BUY_DEVELOPMENT_CARD
FooPlayer._evaluate_node: Branching 17 -> 5 via cheap pre-eval
  Action 2: expected_value=1200000002811284.8 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1200000002809180.8 action_type=ActionType.MARITIME_TRADE
FooPlayer._evaluate_node: Branching 17 -> 5 via cheap pre-eval
  Action 4: expected_value=1200000002811269.2 action_type=ActionType.MARITIME_TRADE
  Action 5: expected_value=1200000002809182.8 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.MARITIME_TRADE: 'MARITIME_TRADE'>, value=('WHEAT', 'WHEAT', 'WHEAT', 'WHEAT', 'WOOD')) score=1200000002811284.8 node_expansions=61
FooPlayer.decide: Prioritizing 16 infrastructure actions over 17 total
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811284.8 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810729.2 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810562.5 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810451.5 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810284.8 action_type=ActionType.BUILD_ROAD
  quick_score=1200000002810173.8 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Fully evaluating 6 top actions with lookahead depth=3
  Action 0: expected_value=1200000002811293.2 action_type=ActionType.BUILD_ROAD
  Action 1: expected_value=1200000002810725.2 action_type=ActionType.BUILD_ROAD
  Action 2: expected_value=1200000002810562.5 action_type=ActionType.BUILD_ROAD
  Action 3: expected_value=1200000002810449.5 action_type=ActionType.BUILD_ROAD
  Action 4: expected_value=1200000002810284.8 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1200000002810182.2 action_type=ActionType.BUILD_ROAD
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
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811284.8 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002811280.8 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000002811280.8 node_expansions=8
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811282.8 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 9 -> 5 via cheap pre-eval
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000002813114.0 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000002813114.0 node_expansions=29
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000002811282.8 action_type=ActionType.DISCARD
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000005589059.0 action_type=ActionType.DISCARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) score=1200000005589059.0 node_expansions=3
FooPlayer.decide: Pruned robber actions from 18 to 1
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589059.0 action_type=ActionType.MOVE_ROBBER
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000005589069.5 action_type=ActionType.MOVE_ROBBER
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
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589059.0 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000022255724.8 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000022255724.8 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589091.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 17 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000005590883.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000005590883.8 node_expansions=20
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589072.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 10 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000005589070.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000005589070.5 node_expansions=7
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589071.5 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002811305.2 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000002811305.2 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589068.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 21 -> 5 via cheap pre-eval
  Action 0: expected_value=1200000005589069.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1200000005589069.5 node_expansions=9
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1200000005589049.5 action_type=ActionType.DISCARD
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000022255712.8 action_type=ActionType.DISCARD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.DISCARD: 'DISCARD'>, value=None) score=1200000022255712.8 node_expansions=5
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900031226.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1200000002809892.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1200000002809892.8 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900031216.0 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 10 -> 5 via cheap pre-eval
  Action 0: expected_value=1199999900031239.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1199999900031239.5 node_expansions=9
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900030116.5 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1199999900030117.5 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1199999900030117.5 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900030116.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1199999900030117.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1199999900030117.5 node_expansions=4
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1199999900030141.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 19 -> 5 via cheap pre-eval
  Action 0: expected_value=1499999936141080.5 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1499999936141080.5 node_expansions=11
FooPlayer.decide: Prioritizing 18 infrastructure actions over 19 total
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141080.5 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1499999919472414.0 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1499999908360580.8 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1499999902803913.8 action_type=ActionType.BUILD_SETTLEMENT
  quick_score=1199999900032505.5 action_type=ActionType.BUILD_ROAD
  quick_score=1199999900031949.8 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Fully evaluating 6 top actions with lookahead depth=3
  Action 0: expected_value=1499999936141081.5 action_type=ActionType.BUILD_SETTLEMENT
  Action 1: expected_value=1499999919472448.5 action_type=ActionType.BUILD_SETTLEMENT
  Action 2: expected_value=1499999908360604.2 action_type=ActionType.BUILD_SETTLEMENT
  Action 3: expected_value=1499999902803927.2 action_type=ActionType.BUILD_SETTLEMENT
  Action 4: expected_value=1199999900032521.0 action_type=ActionType.BUILD_ROAD
  Action 5: expected_value=1199999900031949.8 action_type=ActionType.BUILD_ROAD
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.BUILD_SETTLEMENT: 'BUILD_SETTLEMENT'>, value=49) score=1499999936141081.5 node_expansions=18
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141080.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1499999936141081.5 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1499999936141081.5 node_expansions=7
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141096.0 action_type=ActionType.ROLL
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
  Action 0: expected_value=1500000027807762.8 action_type=ActionType.ROLL
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.ROLL: 'ROLL'>, value=None) score=1500000027807762.8 node_expansions=3
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141105.0 action_type=ActionType.MARITIME_TRADE
  quick_score=1499999936141096.0 action_type=ActionType.END_TURN
  quick_score=1499999936141095.0 action_type=ActionType.MARITIME_TRADE
  quick_score=1499999936141092.5 action_type=ActionType.MARITIME_TRADE
  quick_score=1499999936141082.5 action_type=ActionType.MARITIME_TRADE
FooPlayer.decide: Fully evaluating 5 top actions with lookahead depth=3
  Action 0: expected_value=1499999936141105.0 action_type=ActionType.MARITIME_TRADE
FooPlayer._evaluate_node: Branching 10 -> 5 via cheap pre-eval
  Action 1: expected_value=1499999936141119.5 action_type=ActionType.END_TURN
  Action 2: expected_value=1499999936141096.0 action_type=ActionType.MARITIME_TRADE
  Action 3: expected_value=1499999936141127.0 action_type=ActionType.MARITIME_TRADE
  Action 4: expected_value=1499999936141082.5 action_type=ActionType.MARITIME_TRADE
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
FooPlayer.decide: Quick action scores (top->low):
  quick_score=1499999936141092.5 action_type=ActionType.END_TURN
FooPlayer.decide: Fully evaluating 1 top actions with lookahead depth=3
FooPlayer._evaluate_node: Branching 14 -> 5 via cheap pre-eval
  Action 0: expected_value=1499999936141106.0 action_type=ActionType.END_TURN
FooPlayer.decide: Chosen action=Action(color=<Color.BLUE: 'BLUE'>, action_type=<ActionType.END_TURN: 'END_TURN'>, value=None) score=1499999936141106.0 node_expansions=7
Playing 30 games...                                          ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸       87%        
FooPlayer:BLUE                                               ━━━━━                                     13%        
                    Last 10 Games                    
     ╷          ╷       ╷        ╷         ╷         
   # │ SEATING  │ TURNS │ RED VP │ BLUE VP │ WINNER  
╶────┼──────────┼───────┼────────┼─────────┼────────╴
  21 │ BLUE,RED │   103 │     10 │       8 │ RED     
  22 │ BLUE,RED │    67 │     11 │       6 │ RED     
  23 │ RED,BLUE │    74 │     11 │       7 │ RED     
  24 │ RED,BLUE │    96 │     10 │       8 │ RED     
  25 │ BLUE,RED │    59 │     10 │       5 │ RED     
  26 │ RED,BLUE │    46 │     10 │       2 │ RED     
  27 │ RED,BLUE │    82 │     10 │       5 │ RED     
  28 │ RED,BLUE │    56 │     10 │       3 │ RED     
  29 │ RED,BLUE │    76 │     10 │       4 │ RED     
  30 │ RED,BLUE │    90 │     10 │       5 │ 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) │   26 │ 9.40 │ 2.33 │ 2.83 │  0.70 │ 0.00 │  0.00  
  FooPlayer:BLUE                                               │    4 │ 5.60 │ 2.20 │ 1.17 │  0.30 │ 0.10 │  0.27  
                                                               ╵      ╵      ╵      ╵      ╵       ╵      ╵        
              Game Summary              
            ╷           ╷               
  AVG TICKS │ AVG TURNS │ AVG DURATION  
╶───────────┼───────────┼──────────────╴
     212.77 │     74.37 │   2.553 secs  
            ╵           ╵
================================== 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

