--- Input for ANALYZER at 20250925_033550 ---
================================ 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) **10.03 VP**.
- **Key Counts**:
  - FooPlayer: 2.0 settlements, 0.0 cities, 0.0 roads, 0.0 dev cards.
  - Opponent: 2.7 settlements, 2.67 cities, 1.0 road, 0.0 dev cards.
  - **Total Turns**: 72.17 turns per game.

### VERDICT
**Poor**: FooPlayer lost all games with a VP difference of **-8.03**.

---

### LIKELY REASONS
1. **No Strategic Logic**:
   - The `decide` method in `foo_player.py` (lines 15–20) **always selects the first action** in `playable_actions` without evaluating its impact.
   - **Citation**: `return playable_actions[0]` (line 19).
   - **Corroborating Log**: The game_output.txt repeats `"Choosing First Action on Default"` for every turn, confirming no decision-making logic.

2. **No Lookahead or Heuristics**:
   - No use of `copy_game`, `execute_deterministic`, or value functions (e.g., `base_fn`) to evaluate future states.
   - **Citation**: Missing imports/usage of `adapters.py` helpers for state evaluation.

3. **No Handling of Chance or Robber**:
   - No policy for dice outcomes, robber placement, or development card usage.
   - **Corroborating Log**: The opponent (AlphaBetaPlayer) consistently scores 10+ VP, while FooPlayer stagnates at 2 VP, indicating no adaptive strategy.

4. **No Placement or Upgrade Logic**:
   - No logic for settling, building roads, or upgrading to cities.
   - **Corroborating Result**: FooPlayer averages **0.0 cities and 0.0 roads**, while the opponent builds 2.67 cities and 1.0 road.

---

### STRATEGIC FLAW
FooPlayer **blindly selects the first available action** without evaluating its long-term impact or adapting to the game state, resulting in a static, non-competitive strategy.

---
### NEXT STEP
Send to **Coder** to implement a **1-ply value lookahead** using `copy_game` and `base_fn` from `adapters.py`, and add **basic placement/upgrade logic**.

---
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 **7.83 VP** vs. AlphaBetaPlayer (RED) **8.0 VP**.
- **VP Difference**: **-0.17** (slightly below parity).
- **Key Counts**:
  - FooPlayer: 2.6 settlements, 2.1 cities, 0.37 roads, 0.0 armies, 0.3 dev cards.
  - Opponent: 3.23 settlements, 1.73 cities, 0.63 roads, 0.0 armies, 0.03 dev cards.
  - **Total Turns**: 73.43 turns per game.

### VERDICT
**Borderline**: FooPlayer is competitive but slightly underperforms, with a VP difference of **-0.17**.

---

### LIKELY REASONS
1. **Limited Action Sampling**:
   - The `MAX_ACTIONS_TO_EVAL` is set to **30** (line 22), and `SAMPLE_PER_ACTION_TYPE` is **2** (line 23). This may prune critical actions, especially in complex states.
   - **Corroborating Log**: No explicit errors, but the opponent's higher settlement count (3.23 vs. 2.6) suggests FooPlayer may miss optimal placement opportunities.

2. **No Robber or Chance Handling**:
   - The `decide` method does not explicitly handle **robber placement** or **development card usage**, which are critical for disrupting opponents and securing resources.
   - **Corroborating Result**: FooPlayer averages **0.0 armies** and **0.3 dev cards**, while the opponent leverages these better.

3. **Tie-Breaking Logic**:
   - The tie-break in `decide` (line 140) uses `repr(action)` lexicographically, which may not prioritize strategically superior actions (e.g., upgrading to a city vs. building a road).

4. **No Adaptive Value Function**:
   - Uses the default `base_fn` without tuning or dynamic adjustments for game phases (e.g., early expansion vs. late-game optimization).

---

### STRATEGIC FLAW
FooPlayer's **1-ply lookahead is too shallow and lacks adaptive heuristics**, leading to suboptimal action selection, especially in robber placement, development card usage, and tie-breaking.

---
### NEXT STEP
Send to **Strategizer** to refine **action sampling limits**, add **robber/chance handling**, and tune the **value function** for phase-specific strategies.

---
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** (17 wins, 13 losses).
- **VP Comparison**: FooPlayer (BLUE) averaged **8.13 VP** vs. AlphaBetaPlayer (RED) **7.47 VP**.
- **VP Difference**: **+0.66** (above parity).
- **Key Counts**:
  - FooPlayer: 2.57 settlements, 2.33 cities, 0.4 roads, 0.03 armies, 0.03 dev cards.
  - Opponent: 2.4 settlements, 1.9 cities, 0.6 roads, 0.0 armies, 0.07 dev cards.
  - **Total Turns**: 71.47 turns per game.

---
### VERDICT
**Good**: FooPlayer outperforms the opponent with a **VP difference of +0.66** and a **57% win rate**.

---
### STRATEGIC FLAW
While FooPlayer is now competitive, its **1-ply lookahead lacks depth for long-term planning**, particularly in **robber placement, development card usage, and road-building strategies**. This is evident in the lower average roads (0.4 vs. 0.6) and minimal army/dev card usage.

---
### LIKELY REASONS FOR SUBOPTIMAL PERFORMANCE
1. **Shallow Lookahead**:
   - The 1-ply lookahead (lines 140–200) evaluates actions **only one turn ahead**, missing multi-turn synergies (e.g., road-building for Longest Road).
   - **Corroborating Result**: Opponent builds more roads (0.6 vs. 0.4), suggesting better long-term positioning.

2. **Robber and Chance Handling**:
   - While `_is_robber_or_chance` (lines 70–90) identifies robber/dev card actions, the **expected value calculation** (lines 110–140) caps outcomes at `SPECTRUM_MAX_OUTCOMES=8`, potentially oversimplifying probabilistic decisions.
   - **Corroborating Result**: FooPlayer averages only **0.03 armies** and **0.03 dev cards**, indicating underutilization.

3. **Action Sampling Limits**:
   - `MAX_ACTIONS_TO_EVAL=60` and `SAMPLE_PER_ACTION_TYPE=3` (lines 22–23) may **prune high-value actions** in complex states.
   - **Corroborating Log**: No explicit errors, but the opponent’s higher road count suggests FooPlayer may miss critical expansion opportunities.

4. **Tie-Breaking Logic**:
   - The tie-break (line 180) uses `repr(action)` lexicographically, which may not prioritize **strategically superior actions** (e.g., city upgrades over roads).

---
### NEXT STEP
Send to **Strategizer** to **increase lookahead depth to 2-ply** and refine **robber/dev card policies** for long-term dominance.

---
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:

---
### ERROR SUMMARY
- **First Error Line**:
  `AttributeError: 'FooPlayer' object has no attribute '_evaluate_action'`
- **Exception Type**: `AttributeError`
- **File**: `/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/agents/agentEvolver_v2/foo_player.py`
- **Line Number**: 505
- **Exact Code Line**:
  `res = self._evaluate_action(game, a, self.color)`

---
### LIKELY CAUSE
- The `_evaluate_action` method is **referenced but not defined** in the `decide` method (line 505).
- This is likely due to a **refactoring oversight** where the method was either renamed, removed, or not implemented.

---
### QUICK FIX FOCUS
- **Inspect Line 505** in `foo_player.py`:
  - The `decide` method calls `self._evaluate_action(game, a, self.color)`, but this method is **missing**.
  - **Solution**: Either:
    1. **Implement `_evaluate_action`** to evaluate actions and return `(score, vp_delta)` as expected by the logic.
    2. **Replace the call** with an existing method (e.g., `_simulate_and_evaluate`) or a fallback logic.

---
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:

---
### ERROR SUMMARY
- **First Error Line**:
  `AttributeError: 'FooPlayer' object has no attribute '_evaluate_action'`
- **Exception Type**: `AttributeError`
- **File**: `/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/agents/agentEvolver_v2/foo_player.py`
- **Line Number**: 505
- **Exact Code Line**:
  `res = self._evaluate_action(game, a, self.color)`

---
### LIKELY CAUSE
- The `_evaluate_action` method is **referenced but not defined** in the `FooPlayer` class.
- This is likely due to a **refactoring oversight** where the method was either:
  - **Renamed** (e.g., to `_simulate_and_evaluate`).
  - **Removed** or **not implemented** during updates.

---
### QUICK FIX FOCUS
- **Inspect Line 505** in `foo_player.py`:
  - The `decide` method calls `self._evaluate_action(game, a, self.color)`, but this method is **missing**.
  - **Solution**:
    1. **Implement `_evaluate_action`** to evaluate actions and return `(score, vp_delta)` as expected by the logic.
    2. **Replace the call** with an existing method (e.g., `_simulate_and_evaluate`) or a fallback logic.

---
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": 72.16666666666667,
    "full_game_log_path": "game_20250925_030949_fg/game_output.txt",
    "json_game_results_path": "game_20250925_030949_fg/game_20250925_030949_fg.json",
    "cur_foo_player_path": "game_20250925_030949_fg/foo_player.py",
    "cli_run_id": "game_20250925_030949_fg",
    "timestamp": "2025-09-25 03:10:25"
  },
  "Evolution 1": {
    "wins": 14,
    "avg_score": 7.833333333333333,
    "avg_turns": 73.43333333333334,
    "full_game_log_path": "game_20250925_031330_fg/game_output.txt",
    "json_game_results_path": "game_20250925_031330_fg/game_20250925_031330_fg.json",
    "cur_foo_player_path": "game_20250925_031330_fg/foo_player.py",
    "cli_run_id": "game_20250925_031330_fg",
    "timestamp": "2025-09-25 03:14:22"
  },
  "Evolution 2": {
    "wins": 17,
    "avg_score": 8.133333333333333,
    "avg_turns": 71.46666666666667,
    "full_game_log_path": "game_20250925_031827_fg/game_output.txt",
    "json_game_results_path": "game_20250925_031827_fg/game_20250925_031827_fg.json",
    "cur_foo_player_path": "game_20250925_031827_fg/foo_player.py",
    "cli_run_id": "game_20250925_031827_fg",
    "timestamp": "2025-09-25 03:19:20"
  },
  "Evolution 3": {
    "wins": 0,
    "avg_score": 0,
    "avg_turns": 0,
    "full_game_log_path": "game_20250925_032424_fg/game_output.txt",
    "json_game_results_path": "None",
    "cur_foo_player_path": "game_20250925_032424_fg/foo_player.py",
    "cli_run_id": "game_20250925_032424_fg",
    "timestamp": "2025-09-25 03:24:29"
  },
  "Evolution 4": {
    "wins": 8,
    "avg_score": 6.766666666666667,
    "avg_turns": 75.76666666666667,
    "full_game_log_path": "game_20250925_032853_fg/game_output.txt",
    "json_game_results_path": "game_20250925_032853_fg/game_20250925_032853_fg.json",
    "cur_foo_player_path": "game_20250925_032853_fg/foo_player.py",
    "cli_run_id": "game_20250925_032853_fg",
    "timestamp": "2025-09-25 03:29:58"
  },
  "Evolution 5": {
    "wins": 12,
    "avg_score": 7.766666666666667,
    "avg_turns": 73.96666666666667,
    "full_game_log_path": "game_20250925_033505_fg/game_output.txt",
    "json_game_results_path": "game_20250925_033505_fg/game_20250925_033505_fg.json",
    "cur_foo_player_path": "game_20250925_033505_fg/foo_player.py",
    "cli_run_id": "game_20250925_033505_fg",
    "timestamp": "2025-09-25 03:35:50"
  }
}
================================ Human Message =================================

This is the current game_output.txt file

Playing 30 games...                                          ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False) ━━━━━━━━━━━━━━━━━━━━━━━━                  60%        
FooPlayer:BLUE                                               ━━━━━━━━━━━━━━━━                          40%        
                    Last 10 Games                    
     ╷          ╷       ╷        ╷         ╷         
   # │ SEATING  │ TURNS │ RED VP │ BLUE VP │ WINNER  
╶────┼──────────┼───────┼────────┼─────────┼────────╴
  21 │ BLUE,RED │    91 │     10 │       6 │ RED     
  22 │ BLUE,RED │    62 │      3 │      10 │ BLUE    
  23 │ RED,BLUE │   126 │     11 │       7 │ RED     
  24 │ RED,BLUE │    76 │     10 │       4 │ RED     
  25 │ BLUE,RED │    44 │      4 │      10 │ BLUE    
  26 │ RED,BLUE │    73 │      8 │      10 │ BLUE    
  27 │ BLUE,RED │   120 │      8 │      10 │ BLUE    
  28 │ RED,BLUE │    70 │     10 │       6 │ RED     
  29 │ RED,BLUE │    45 │      4 │      10 │ BLUE    
  30 │ RED,BLUE │    72 │     10 │       9 │ 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) │   18 │   8.23 │        2.43 │       2.30 │     0.60 │     0.00 │       0.00  
  FooPlayer:BLUE                                               │   12 │   7.77 │        3.03 │       1.77 │     0.40 │     0.03 │       0.33  
                                                               ╵      ╵        ╵             ╵            ╵          ╵          ╵             
              Game Summary              
            ╷           ╷               
  AVG TICKS │ AVG TURNS │ AVG DURATION  
╶───────────┼───────────┼──────────────╴
     211.33 │     73.97 │   1.450 secs  
            ╵           ╵
================================ Human Message =================================

This is the current game_results json file

{
  "Player Summary": {
    "AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)": {
      "WINS": 18,
      "AVG VP": 8.233333333333333,
      "AVG SETTLES": 2.433333333333333,
      "AVG CITIES": 2.3,
      "AVG ROAD": 0.6,
      "AVG ARMY": 0.0,
      "AVG DEV VP": 0.0
    },
    "FooPlayer:BLUE": {
      "WINS": 12,
      "AVG VP": 7.766666666666667,
      "AVG SETTLES": 3.033333333333333,
      "AVG CITIES": 1.7666666666666666,
      "AVG ROAD": 0.4,
      "AVG ARMY": 0.03333333333333333,
      "AVG DEV VP": 0.3333333333333333
    }
  },
  "Game Summary": {
    "AVG TICKS": 211.33333333333334,
    "AVG TURNS": 73.96666666666667,
    "AVG DURATION": 1.4504021803538005
  }
}
================================ Human Message =================================

This is the current foo_player.py file

import random
import traceback
from typing import Iterable, List, Optional, Tuple, Dict, Any

# Must import adapters via the provided thin wrapper. Do NOT import catanatron directly.
from .adapters import (
    Game,
    Player,
    Color,
    ActionType,
    copy_game,
    execute_deterministic,
    execute_spectrum,
    expand_spectrum,
    base_fn,
    list_prunned_actions,
)


class FooPlayer(Player):
    """A player that uses a selective 2-ply adversarial lookahead built on adapters.

    Key features:
    - Phase-aware 1-ply sampling/pruning to keep runtime bounded.
    - Chance-aware expected values using execute_spectrum/expand_spectrum when available.
    - Selective 2-ply adversarial (min over opponent responses) evaluation for top-K 1-ply
      candidates to improve robustness against counters.
    - Deterministic sampling/tie-breaking via seeded RNG.

    All interactions with the engine use only the adapters surface.
    Set self.debug = True on the instance to enable diagnostic printing.
    """

    # Tunable class defaults (STRATEGIZER recommendations)
    MAX_ACTIONS_TO_EVAL: int = 80  # increased from 60
    SAMPLE_PER_ACTION_TYPE: int = 4  # increased from 3
    SPECTRUM_MAX_OUTCOMES: int = 8
    EARLY_TURN_THRESHOLD: int = 30

    # New selective 2-ply parameters (disabled by default per stabilization plan)
    TOP_K_1PLY: int = 0  # disable 2-ply lookahead for now
    OP_MAX_ACTIONS: int = 20
    OP_SAMPLE_PER_ACTION_TYPE: int = 2

    # reserved/compat
    TOP_K_DEEP: int = 0  # disabled by default
    RNG_SEED: int = 0

    def __init__(self, name: Optional[str] = None):
        # Initialize as BLUE by default (preserve original behavior)
        super().__init__(Color.BLUE, name)
        # Toggle to True to get per-turn diagnostic prints
        self.debug: bool = False
        # Pre-create the value function from adapters.base_fn factory if possible.
        # base_fn returns a callable: (game, color) -> float.
        try:
            self._value_fn = base_fn()
        except Exception:
            # If the factory has a different signature, lazily resolve in evaluation.
            self._value_fn = None

        # Diagnostic counters to help debug evaluation failures and fallbacks
        self._diag = {
            "n_candidates": 0,
            "n_eval_attempts": 0,
            "n_eval_success": 0,
            "n_spectrum_calls": 0,
            "n_spectrum_success": 0,
            "n_det_calls": 0,
            "n_det_success": 0,
            "n_skipped": 0,
            "n_fallbacks_to_first_action": 0,
        }

    # ------------------ Helper methods ------------------
    def _stable_color_hash(self, color: Color) -> int:
        """Stable small hash for a Color used to seed RNG deterministically.

        We keep this deterministic across runs by summing character ordinals of the color's
        string representation. This avoids relying on Python's randomized hash().
        """
        try:
            return sum(ord(c) for c in str(color)) & 0xFFFFFFFF
        except Exception:
            return 0

    def _action_type_key(self, action) -> str:
        """Return a stable grouping key for an action.

        Prefer action.action_type, then other attributes, then class name or string.
        """
        k = getattr(action, "action_type", None)
        if k is not None:
            return str(k)
        for attr in ("type", "name"):
            k = getattr(action, attr, None)
            if k is not None:
                return str(k)
        try:
            return action.__class__.__name__
        except Exception:
            return str(action)

    def _is_build_or_upgrade(self, action) -> bool:
        """Detect actions that build or upgrade (settlement, city, road, upgrade).

        This function is defensive: it checks action_type when available and falls back
        to class name matching so grouping remains robust.
        """
        at = getattr(action, "action_type", None)
        try:
            return at in {
                ActionType.BUILD_SETTLEMENT,
                ActionType.BUILD_CITY,
                ActionType.BUILD_ROAD,
            }
        except Exception:
            name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
            name_str = str(name).lower()
            return any(k in name_str for k in ("build", "settle", "city", "road", "upgrade"))

    def _is_robber_or_chance(self, action) -> bool:
        """Detect robber placement or development-card (chance) actions.

        Uses action_type when available; otherwise checks common name tokens.
        """
        at = getattr(action, "action_type", None)
        try:
            return at in {
                ActionType.PLAY_DEV_CARD,
                ActionType.PLACE_ROBBER,
                ActionType.DRAW_DEV_CARD,
            }
        except Exception:
            name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
            name_str = str(name).lower()
            return any(k in name_str for k in ("robber", "dev", "development", "draw"))

    def _get_visible_vp(self, game: Game, my_color: Color) -> int:
        """Try to extract a visible/observable victory point count for my_color.

        This is intentionally defensive: if no visible metric exists, return 0.
        """
        try:
            vp_map = getattr(game, "visible_vp", None)
            if isinstance(vp_map, dict):
                return int(vp_map.get(my_color, 0))
        except Exception:
            pass
        try:
            vp_map = getattr(game, "visible_victory_points", None)
            if isinstance(vp_map, dict):
                return int(vp_map.get(my_color, 0))
        except Exception:
            pass
        return 0

    def _is_road_action(self, action) -> bool:
        """Detect road-building actions."""
        at = getattr(action, "action_type", None)
        try:
            return at == ActionType.BUILD_ROAD
        except Exception:
            name = getattr(action, "name", None) or getattr(action, "type", None) or action.__class__.__name__
            return "road" in str(name).lower()

    def _sample_actions(self, playable_actions: Iterable, game: Game) -> List:
        """Phase-aware sampling: prioritize builds early, roads mid-game, VP actions late.

        Returns a deterministic, pruned list of candidate actions up to MAX_ACTIONS_TO_EVAL.
        """
        actions = list(playable_actions)
        n = len(actions)
        if n <= self.MAX_ACTIONS_TO_EVAL:
            return actions

        # Determine phase using available heuristics on game. Use tick or current_turn if present.
        current_turn = getattr(game, "current_turn", None)
        if current_turn is None:
            current_turn = getattr(game, "tick", 0)
        early_game = (current_turn <= self.EARLY_TURN_THRESHOLD)
        mid_game = (self.EARLY_TURN_THRESHOLD < current_turn <= 2 * self.EARLY_TURN_THRESHOLD)

        # Group actions by stable key
        groups: Dict[str, List] = {}
        for a in actions:
            key = self._action_type_key(a)
            groups.setdefault(key, []).append(a)

        # Deterministic RNG seeded with a combination of RNG_SEED and player's color
        color_seed = self._stable_color_hash(self.color)
        rng = random.Random(self.RNG_SEED + color_seed)

        sampled: List = []
        # Iterate through groups in a stable order to keep behavior deterministic
        for key in sorted(groups.keys()):
            group = list(groups[key])
            # Determine how many to sample from this group, with phase-aware bias
            sample_count = self.SAMPLE_PER_ACTION_TYPE
            try:
                if early_game and any(self._is_build_or_upgrade(a) for a in group):
                    sample_count += 1
                elif mid_game and any(self._is_road_action(a) for a in group):
                    sample_count += 1
                elif not early_game and any(
                    getattr(a, "action_type", None) in {ActionType.BUILD_CITY, ActionType.BUILD_SETTLEMENT}
                    for a in group
                ):
                    sample_count += 1
            except Exception:
                pass

            # Deterministic shuffle and pick
            rng.shuffle(group)
            take = min(sample_count, len(group))
            sampled.extend(group[:take])
            if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
                break

        # If under budget, fill deterministically from remaining actions
        if len(sampled) < self.MAX_ACTIONS_TO_EVAL:
            for a in actions:
                if a not in sampled:
                    sampled.append(a)
                    if len(sampled) >= self.MAX_ACTIONS_TO_EVAL:
                        break

        if self.debug:
            phase = "early" if early_game else ("mid" if mid_game else "late")
            print(f"_sample_actions: phase={phase}, pruned {n} -> {len(sampled)} actions (cap={self.MAX_ACTIONS_TO_EVAL})")
        return sampled

    def _sample_opponent_actions(self, playable_actions: Iterable, game: Game, opponent_color: Color) -> List:
        """Opponent-specific sampling that respects OP_SAMPLE_PER_ACTION_TYPE and OP_MAX_ACTIONS.

        Uses a deterministic RNG seeded with opponent color so opponent sampling is reproducible.
        """
        actions = list(playable_actions)
        n = len(actions)
        if n <= self.OP_MAX_ACTIONS:
            return actions

        # Phase detection reused from our own sampling
        current_turn = getattr(game, "current_turn", None)
        if current_turn is None:
            current_turn = getattr(game, "tick", 0)
        early_game = (current_turn <= self.EARLY_TURN_THRESHOLD)

        groups: Dict[str, List] = {}
        for a in actions:
            key = self._action_type_key(a)
            groups.setdefault(key, []).append(a)

        color_seed = self._stable_color_hash(opponent_color)
        rng = random.Random(self.RNG_SEED + color_seed)

        sampled: List = []
        for key in sorted(groups.keys()):
            group = list(groups[key])
            # opponent sampling budget
            sample_count = self.OP_SAMPLE_PER_ACTION_TYPE
            try:
                if early_game and any(self._is_build_or_upgrade(a) for a in group):
                    sample_count += 1
            except Exception:
                pass
            rng.shuffle(group)
            take = min(sample_count, len(group))
            sampled.extend(group[:take])
            if len(sampled) >= self.OP_MAX_ACTIONS:
                break

        if len(sampled) < self.OP_MAX_ACTIONS:
            for a in actions:
                if a not in sampled:
                    sampled.append(a)
                    if len(sampled) >= self.OP_MAX_ACTIONS:
                        break

        if self.debug:
            print(f"_sample_opponent_actions: pruned {n} -> {len(sampled)} actions (cap={self.OP_MAX_ACTIONS})")
        return sampled

    def _normalize_and_cap_spectrum(self, spectrum: Iterable, cap: int) -> List[Tuple[Game, float]]:
        """Normalize spectrum outcomes and cap to `cap` entries.

        Accepts iterables like those returned by execute_spectrum or expand_spectrum entry lists.
        Returns a list of (game, prob) with probabilities summing to 1.
        """
        try:
            lst = list(spectrum)
            if not lst:
                return []
            # Sort by probability descending when possible, then cap
            try:
                sorted_lst = sorted(lst, key=lambda x: float(x[1]) if len(x) > 1 else 0.0, reverse=True)
            except Exception:
                sorted_lst = lst
            capped = sorted_lst[:cap]
            probs = []
            games = []
            for entry in capped:
                try:
                    g, p = entry
                except Exception:
                    # Unexpected shape: skip
                    continue
                games.append(g)
                probs.append(float(p))
            if not games:
                return []
            total = sum(probs)
            if total > 0.0:
                normalized = [(g, p / total) for g, p in zip(games, probs)]
            else:
                n = len(games)
                normalized = [(g, 1.0 / n) for g in games]
            return normalized
        except Exception:
            if self.debug:
                print("_normalize_and_cap_spectrum: failed to normalize spectrum")
                traceback.print_exc()
            return []

    def _determine_opponent_color(self, game: Game, my_color: Color) -> Color:
        """Try to determine the opponent's color from the game state.

        This is defensive: it checks common attributes and falls back to a two-player assumption.
        """
        try:
            cur = getattr(game, "current_player", None)
            if cur is not None:
                # If cur is a Player instance, extract its color attribute when possible
                try:
                    if cur != my_color:
                        return cur
                except Exception:
                    pass
        except Exception:
            pass

        # As a simple fallback, assume a two-player game and pick a different color deterministically
        try:
            colors = [c for c in list(Color)]
            if len(colors) >= 2:
                for c in colors:
                    if c != my_color:
                        return c
        except Exception:
            pass
        # Last resort: return my_color (harmless, though less correct)
        return my_color

    def _derive_opponent_actions(self, game: Game, opponent_color: Color) -> List:
        """Obtain a list of opponent actions with several fallbacks.

        Order:
        1) adapters.list_prunned_actions(game)
        2) game.playable_actions() if present
        3) empty list (conservative)
        """
        try:
            # Preferred: adapters-provided pruned action list (designed for search)
            pruned = list_prunned_actions(game)
            if pruned:
                return pruned
        except Exception:
            if self.debug:
                print("_derive_opponent_actions: list_prunned_actions failed")
                traceback.print_exc()

        try:
            pa = getattr(game, "playable_actions", None)
            if callable(pa):
                res = pa()
                if res:
                    return list(res)
        except Exception:
            if self.debug:
                print("_derive_opponent_actions: game.playable_actions() failed")
                traceback.print_exc()

        # As a conservative fallback, return empty list so we evaluate the post-action state directly
        return []

    def _safe_eval_base_fn(self, g: Game, color: Color) -> Optional[float]:
        """Safely call the adapters' base value function in its possible forms.

        Tries self._value_fn(g,color) if available; otherwise attempts base_fn()(g,color) and
        finally base_fn(g,color). Returns None on failure and logs when debug=True.
        """
        try:
            if self._value_fn is not None:
                return float(self._value_fn(g, color))
        except Exception:
            if self.debug:
                print("_safe_eval_base_fn: self._value_fn failed")
                traceback.print_exc()
        # Try factory form
        try:
            vf = base_fn()
            try:
                return float(vf(g, color))
            except Exception:
                if self.debug:
                    print("_safe_eval_base_fn: vf(g,color) failed")
                    traceback.print_exc()
        except Exception:
            # Maybe base_fn itself accepts (g,color)
            pass
        try:
            return float(base_fn(g, color))
        except Exception:
            if self.debug:
                print("_safe_eval_base_fn: all attempts to call base_fn failed")
                traceback.print_exc()
            return None

    def _simulate_and_evaluate(self, game: Game, action, my_color: Color) -> Optional[float]:
        """Simulate `action` from `game` and return a numeric expected score for my_color.

        If action is None, simply evaluate the provided game state.
        This function handles spectrum (chance) outcomes when available and falls back to
        deterministic execution. Returns None on failure for the given simulation.
        """
        # Copy the game to avoid mutating caller's state
        try:
            game_copy = copy_game(game)
        except Exception as e:
            if self.debug:
                print("_simulate_and_evaluate: copy_game failed:", e)
                traceback.print_exc()
            return None

        # If action is None, just evaluate the provided state
        if action is None:
            return self._safe_eval_base_fn(game_copy, my_color)

        # Chance-aware path
        if self._is_robber_or_chance(action):
            try:
                spec = None
                try:
                    spec = execute_spectrum(game_copy, action)
                except Exception:
                    # Try expand_spectrum single-action expansion
                    try:
                        spec_map = expand_spectrum(game_copy, [action])
                        if isinstance(spec_map, dict):
                            spec = spec_map.get(action, None)
                    except Exception:
                        spec = None

                if spec:
                    outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
                    if not outcomes:
                        # Fall through to deterministic
                        pass
                    else:
                        total_score = 0.0
                        for og, prob in outcomes:
                            sc = self._safe_eval_base_fn(og, my_color)
                            if sc is None:
                                # If any outcome can't be evaluated reliably, abort spectrum path
                                total_score = None
                                break
                            total_score += prob * sc
                        if total_score is None:
                            if self.debug:
                                print("_simulate_and_evaluate: spectrum had unscorable outcomes; falling back")
                        else:
                            return float(total_score)
            except Exception as e:
                if self.debug:
                    print("_simulate_and_evaluate: execute_spectrum/expand_spectrum failed:", e)
                    traceback.print_exc()
                # fall through to deterministic

        # Deterministic fallback
        try:
            outcomes = execute_deterministic(game_copy, action)
        except Exception as e:
            if self.debug:
                print("_simulate_and_evaluate: execute_deterministic failed:", e)
                traceback.print_exc()
            return None

        try:
            if not outcomes:
                if self.debug:
                    print("_simulate_and_evaluate: execute_deterministic returned no outcomes")
                return None
            first = outcomes[0]
            if isinstance(first, (list, tuple)) and len(first) >= 1:
                resultant_game = first[0]
            else:
                resultant_game = first
        except Exception:
            resultant_game = game_copy

        return self._safe_eval_base_fn(resultant_game, my_color)

    # ------------------ NEW missing method: _evaluate_action ------------------
    def _evaluate_action(self, game: Game, action, my_color: Color) -> Optional[Tuple[float, float]]:
        """Evaluate a candidate action and return (score, vp_delta) or None on failure.

        This method unifies spectrum-based chance evaluation and deterministic execution
        and returns both the numeric score (from base_fn) and the visible VP delta (after - before).
        It is defensive to adapter signature differences and logs traces when self.debug is True.
        """
        # Diagnostic: attempt counter
        self._diag["n_eval_attempts"] = self._diag.get("n_eval_attempts", 0) + 1

        # Helper: safe eval using existing wrapper
        def safe_eval(g: Game) -> Optional[float]:
            return self._safe_eval_base_fn(g, my_color)

        # Helper: visible vp extraction (use existing helper)
        def get_vp(g: Game) -> float:
            try:
                return float(self._get_visible_vp(g, my_color))
            except Exception:
                if self.debug:
                    print("_evaluate_action: _get_visible_vp failed")
                    traceback.print_exc()
                return 0.0

        # Step A: copy game
        try:
            game_copy = copy_game(game)
        except Exception:
            if self.debug:
                print("_evaluate_action: copy_game failed:")
                traceback.print_exc()
            self._diag["n_skipped"] = self._diag.get("n_skipped", 0) + 1
            return None

        # original visible vp
        try:
            vp_orig = get_vp(game)
        except Exception:
            vp_orig = 0.0

        # Step B: if chance-like, try spectrum expansion
        if self._is_robber_or_chance(action):
            try:
                self._diag["n_spectrum_calls"] = self._diag.get("n_spectrum_calls", 0) + 1
                spec = None
                try:
                    spec = execute_spectrum(game_copy, action)
                except Exception:
                    try:
                        spec_map = expand_spectrum(game_copy, [action])
                        if isinstance(spec_map, dict):
                            spec = spec_map.get(action, None)
                    except Exception:
                        spec = None

                if spec:
                    outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
                    if outcomes:
                        weighted_score = 0.0
                        weighted_vp_delta = 0.0
                        any_scored = False
                        for og, prob in outcomes:
                            sc = safe_eval(og)
                            if sc is None:
                                # skip unscorable outcomes
                                continue
                            any_scored = True
                            vp_out = get_vp(og)
                            weighted_score += prob * sc
                            weighted_vp_delta += prob * (vp_out - vp_orig)
                        if any_scored:
                            self._diag["n_spectrum_success"] = self._diag.get("n_spectrum_success", 0) + 1
                            return (float(weighted_score), float(weighted_vp_delta))
                        # else fall through to deterministic
            except Exception:
                if self.debug:
                    print("_evaluate_action: spectrum evaluation failed:")
                    traceback.print_exc()
                # fall through

        # Step C: deterministic execution fallback
        try:
            self._diag["n_det_calls"] = self._diag.get("n_det_calls", 0) + 1
            res = execute_deterministic(game_copy, action)
        except Exception:
            if self.debug:
                print("_evaluate_action: execute_deterministic failed:")
                traceback.print_exc()
            self._diag["n_skipped"] = self._diag.get("n_skipped", 0) + 1
            return None

        try:
            # normalize to a single resultant game
            resultant_game = None
            if res is None:
                resultant_game = game_copy
            elif isinstance(res, (list, tuple)):
                first = res[0]
                if isinstance(first, tuple) and len(first) >= 1:
                    resultant_game = first[0]
                else:
                    resultant_game = first
            else:
                # could be a single game object
                resultant_game = res if hasattr(res, "state") or hasattr(res, "current_player") else game_copy

            score = safe_eval(resultant_game)
            if score is None:
                self._diag["n_skipped"] = self._diag.get("n_skipped", 0) + 1
                return None
            vp_after = get_vp(resultant_game)
            vp_delta = float(vp_after - vp_orig)
            # success counters
            self._diag["n_eval_success"] = self._diag.get("n_eval_success", 0) + 1
            self._diag["n_det_success"] = self._diag.get("n_det_success", 0) + 1
            return (float(score), float(vp_delta))
        except Exception:
            if self.debug:
                print("_evaluate_action: normalize/eval failed:")
                traceback.print_exc()
            self._diag["n_skipped"] = self._diag.get("n_skipped", 0) + 1
            return None

    # ------------------ Decision method (public) ------------------
    def decide(self, game: Game, playable_actions: Iterable):
        """Choose an action using selective 2-ply adversarial lookahead.

        Flow:
        1) Run phase-aware 1-ply sampling and evaluation across candidates.
        2) Keep top TOP_K_1PLY candidates by 1-ply score and deepen each with opponent modeling.
        3) For each candidate, compute expected adversarial value = E_outcomes[min_opponent_response(score)].
        4) Pick candidate maximizing (expected_value, 1-ply vp_delta, repr action tie-break).

        All adapter calls are protected with try/except. On catastrophic failure we fall back to
        returning the best 1-ply candidate or the first playable action as a last resort.
        """
        actions = list(playable_actions)

        if not actions:
            if self.debug:
                print("decide: no playable_actions provided")
            return None

        if len(actions) == 1:
            if self.debug:
                print("decide: single playable action, returning it")
            return actions[0]

        # reset diagnostics for this decision
        self._diag = {k: 0 for k in self._diag}

        # Stage 1: 1-ply evaluation
        candidates = self._sample_actions(actions, game)
        self._diag["n_candidates"] = len(candidates)
        if self.debug:
            print(f"decide: sampled {len(candidates)} candidates from {len(actions)} actions")

        one_ply_results: List[Tuple[Any, float, float]] = []  # (action, score, vp_delta)

        # Resolve evaluator function robustly to avoid AttributeError
        eval_fn = getattr(self, "_evaluate_action", None) or getattr(self, "_simulate_and_evaluate", None)
        if eval_fn is None:
            if self.debug:
                print("decide: no evaluator method found; falling back to first action")
            self._diag["n_fallbacks_to_first_action"] = self._diag.get("n_fallbacks_to_first_action", 0) + 1
            return actions[0]

        for idx, a in enumerate(candidates, start=1):
            try:
                res = eval_fn(game, a, self.color)
            except Exception:
                if self.debug:
                    print("decide: evaluator raised exception for action", repr(a))
                    traceback.print_exc()
                res = None

            if self.debug:
                print(f"1-ply [{idx}/{len(candidates)}]: {repr(a)} -> {res}")

            if res is None:
                # count skipped attempts
                self._diag["n_skipped"] = self._diag.get("n_skipped", 0) + 1
                continue
            sc, vpd = res
            one_ply_results.append((a, float(sc), float(vpd)))

        if not one_ply_results:
            # Nothing evaluated successfully; fallback deterministically
            if self.debug:
                print("decide: no 1-ply evaluations succeeded; falling back to first playable action")
            self._diag["n_fallbacks_to_first_action"] = self._diag.get("n_fallbacks_to_first_action", 0) + 1
            return actions[0]

        # Stage 2: pick top-K 1-ply candidates
        one_ply_results.sort(key=lambda t: (t[1], t[2]), reverse=True)
        top_k = [t[0] for t in one_ply_results[: self.TOP_K_1PLY]]

        if self.debug:
            print("Top 1-ply candidates:")
            for a, s, v in one_ply_results[: self.TOP_K_1PLY]:
                print(f"  candidate: {repr(a)} score={s} vp_delta={v}")

        # If TOP_K_1PLY==0 this loop will be skipped and we will fall back to 1-ply
        best_action = None
        best_value = -float("inf")
        best_vp_delta = -float("inf")
        best_repr = None

        # Simulation budget guard: do not exceed too many simulated nodes
        sim_count = 0
        SIMULATION_HARD_LIMIT = 10000  # safety cap to protect time; adjustable

        try:
            for a in top_k:
                if sim_count >= SIMULATION_HARD_LIMIT:
                    if self.debug:
                        print("decide: reached simulation hard limit; stopping deepening")
                    break

                # Simulate our action a to produce outcome branches
                try:
                    game_copy = copy_game(game)
                except Exception as e:
                    if self.debug:
                        print("decide: copy_game failed for candidate", repr(a), e)
                        traceback.print_exc()
                    continue

                # Obtain outcome branches: prefer spectrum for chance actions
                outcomes: List[Tuple[Game, float]] = []
                try:
                    if self._is_robber_or_chance(a):
                        spec = None
                        try:
                            spec = execute_spectrum(game_copy, a)
                        except Exception:
                            try:
                                spec_map = expand_spectrum(game_copy, [a])
                                if isinstance(spec_map, dict):
                                    spec = spec_map.get(a, None)
                            except Exception:
                                spec = None

                        if spec:
                            outcomes = self._normalize_and_cap_spectrum(spec, self.SPECTRUM_MAX_OUTCOMES)
                    # Fallback to deterministic
                    if not outcomes:
                        det = execute_deterministic(game_copy, a)
                        if not det:
                            if self.debug:
                                print("decide: execute_deterministic returned empty for", repr(a))
                            continue
                        # det is list of (game, prob) often; take as provided
                        # normalize shape defensively
                        normalized = []
                        for entry in det[: self.SPECTRUM_MAX_OUTCOMES]:
                            try:
                                g, p = entry
                            except Exception:
                                g = entry
                                p = 1.0
                            normalized.append((g, float(p)))
                        # If probabilities not summing to 1, normalize
                        total_p = sum(p for _, p in normalized)
                        if total_p <= 0:
                            # assign uniform
                            n = len(normalized)
                            outcomes = [(g, 1.0 / n) for (g, _) in normalized]
                        else:
                            outcomes = [(g, p / total_p) for (g, p) in normalized]

                except Exception as e:
                    if self.debug:
                        print("decide: failed to obtain outcomes for candidate", repr(a), "error:", e)
                        traceback.print_exc()
                    continue

                # Cap outcomes just in case
                if len(outcomes) > self.SPECTRUM_MAX_OUTCOMES:
                    outcomes = outcomes[: self.SPECTRUM_MAX_OUTCOMES]

                if self.debug:
                    print(f"Candidate {repr(a)} produced {len(outcomes)} outcome(s) to evaluate")

                expected_value_a = 0.0
                # find 1-ply vp delta for tie-break usage
                one_ply_vp_delta = next((v for (act, s, v) in one_ply_results if act == a), 0.0)

                for og, p_i in outcomes:
                    if sim_count >= SIMULATION_HARD_LIMIT:
                        break
                    # Derive opponent color
                    opp_color = self._determine_opponent_color(og, self.color)
                    # Get opponent actions with robust fallbacks
                    try:
                        opp_actions = self._derive_opponent_actions(og, opp_color)
                    except Exception:
                        opp_actions = []

                    if not opp_actions:
                        # No opponent actions: evaluate the post-my-action state directly
                        val_i = self._simulate_and_evaluate(og, None, self.color)
                        if val_i is None:
                            # Skip this outcome in expectation if it couldn't be evaluated
                            continue
                        expected_value_a += p_i * val_i
                        sim_count += 1
                        continue

                    # Prune opponent actions deterministically and cap
                    opp_sampled = self._sample_opponent_actions(opp_actions, og, opp_color)[: self.OP_MAX_ACTIONS]

                    if self.debug:
                        print(f"  outcome p={p_i:.3f}: opp_actions={len(opp_actions)} -> sampled={len(opp_sampled)}")

                    # Adversarial opponent: they choose the action minimizing our final score
                    min_score_after_opp = float("inf")
                    for b in opp_sampled:
                        if sim_count >= SIMULATION_HARD_LIMIT:
                            break
                        val_after_b = self._simulate_and_evaluate(og, b, self.color)
                        sim_count += 1
                        if val_after_b is None:
                            continue
                        if val_after_b < min_score_after_opp:
                            min_score_after_opp = val_after_b

                    if min_score_after_opp == float("inf"):
                        # If no opponent simulation succeeded, evaluate the post-my-action state
                        min_score_after_opp = self._simulate_and_evaluate(og, None, self.color) or 0.0

                    expected_value_a += p_i * min_score_after_opp

                # Compare candidate expected value using deterministic tie-break
                if self.debug:
                    print(f"Candidate {repr(a)} expected_value={expected_value_a} (1-ply vp_delta={one_ply_vp_delta})")

                is_better = False
                if best_action is None:
                    is_better = True
                elif expected_value_a > best_value:
                    is_better = True
                elif expected_value_a == best_value:
                    if one_ply_vp_delta > best_vp_delta:
                        is_better = True
                    elif one_ply_vp_delta == best_vp_delta:
                        tie_repr = repr(a)
                        if best_repr is None or tie_repr < best_repr:
                            is_better = True

                if is_better:
                    best_action = a
                    best_value = expected_value_a
                    best_vp_delta = one_ply_vp_delta
                    best_repr = repr(a)

            # End loop over top_k
        except Exception:
            if self.debug:
                print("decide: unexpected error during 2-ply deepening")
                traceback.print_exc()
            # Fall back to 1-ply selection below

        # If 2-ply produced a valid selection, return it
        if best_action is not None:
            if self.debug:
                print("decide: selected (2-ply) action:", repr(best_action), "value:", best_value)
            return best_action

        # Otherwise, fall back to best 1-ply action using existing tie-break rules
        if self.debug:
            print("decide: falling back to best 1-ply action")
        best_action_1ply = None
        best_score = -float("inf")
        best_vp = -float("inf")
        best_repr = None
        for (a, s, v) in one_ply_results:
            tie_repr = repr(a)
            is_better = False
            if best_action_1ply is None:
                is_better = True
            elif s > best_score:
                is_better = True
            elif s == best_score:
                if v > best_vp:
                    is_better = True
                elif v == best_vp and (best_repr is None or tie_repr < best_repr):
                    is_better = True
            if is_better:
                best_action_1ply = a
                best_score = s
                best_vp = v
                best_repr = tie_repr

        if best_action_1ply is not None:
            if self.debug:
                print("decide: chosen action (1-ply fallback):", repr(best_action_1ply), "score:", best_score, "vp_delta:", best_vp)
                print("Diagnostics:", self._diag)
            return best_action_1ply

        # Last resort: return first playable action
        if self.debug:
            print("decide: no choice after fallbacks; returning first playable action")
            self._diag["n_fallbacks_to_first_action"] = self._diag.get("n_fallbacks_to_first_action", 0) + 1
            print("Diagnostics:", self._diag)
        return actions[0]

================================ 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_033600 ---
================================== Ai Message ==================================
Tool Calls:
  read_local_file (aHBxo9hXs)
 Call ID: aHBxo9hXs
  Args:
    rel_path: game_20250925_032424_fg/game_output.txt
================================= Tool Message =================================
Name: read_local_file

Playing 30 games...                                                                                     0% -:--:--
AlphaBetaPlayer:RED(depth=2,value_fn=base_fn,prunning=False)                                            0%        
FooPlayer:BLUE                                                                                          0%        
Traceback (most recent call last):
  File "/Users/nik/miniconda3/envs/strategy-game-agents/bin/catanatron-play", line 33, in <module>
    sys.exit(load_entry_point('catanatron-experimental', 'console_scripts', 'catanatron-play')())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/nik/miniconda3/envs/strategy-game-agents/lib/python3.11/site-packages/click/core.py", line 1130, in __call__
    return self.main(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/nik/miniconda3/envs/strategy-game-agents/lib/python3.11/site-packages/click/core.py", line 1055, in main
    rv = self.invoke(ctx)
         ^^^^^^^^^^^^^^^^
  File "/Users/nik/miniconda3/envs/strategy-game-agents/lib/python3.11/site-packages/click/core.py", line 1404, in invoke
    return ctx.invoke(self.callback, **ctx.params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/nik/miniconda3/envs/strategy-game-agents/lib/python3.11/site-packages/click/core.py", line 760, in invoke
    return __callback(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/catanatron/catanatron_experimental/catanatron_experimental/play.py", line 197, in simulate
    play_batch(
  File "/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/catanatron/catanatron_experimental/catanatron_experimental/play.py", line 330, in play_batch
    for i, game in enumerate(
  File "/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/catanatron/catanatron_experimental/catanatron_experimental/play.py", line 259, in play_batch_core
    game.play(accumulators)
  File "/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/catanatron/catanatron_core/catanatron/game.py", line 132, in play
    self.play_tick(decide_fn=decide_fn, accumulators=accumulators)
  File "/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/catanatron/catanatron_core/catanatron/game.py", line 153, in play_tick
    else player.decide(self, actions)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/agents/agentEvolver_v2/foo_player.py", line 505, in decide
    res = self._evaluate_action(game, a, self.color)
          ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'FooPlayer' object has no attribute '_evaluate_action'
================================== Ai Message ==================================

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

---
### ERROR SUMMARY
- **First Error Line**:
  `AttributeError: 'FooPlayer' object has no attribute '_evaluate_action'`
- **Exception Type**: `AttributeError`
- **File**: `/Users/nik/Library/Mobile Documents/com~apple~CloudDocs/Develop/NLP/strategy-game-agents/agents/agentEvolver_v2/foo_player.py`
- **Line Number**: 505
- **Exact Code Line**:
  `res = self._evaluate_action(game, a, self.color)`

---
### LIKELY CAUSE
- The `_evaluate_action` method is **referenced in the `decide` method** but **not implemented** in the `FooPlayer` class.
- This is likely due to a **refactoring oversight** where the method was either:
  - **Renamed** (e.g., to `_simulate_and_evaluate`).
  - **Removed** or **not implemented** during updates.

---
### QUICK FIX FOCUS
- **Inspect Line 505** in `foo_player.py`:
  - The `decide` method calls `self._evaluate_action(game, a, self.color)`, but this method is **missing**.
  - **Solution**:
    1. **Implement `_evaluate_action`** to evaluate actions and return `(score, vp_delta)` as expected by the logic.
    2. **Replace the call** with an existing method (e.g., `_simulate_and_evaluate`) or a fallback logic.

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

