new env runs locally

environments/game_environments/gymnasium/blackjack_env_no_thinking.py

@@ -1,5 +1,6 @@
 import logging
 from typing import Dict, List, Optional, Tuple
+import json
 
 import gymnasium as gym
 import random
@@ -7,6 +8,7 @@ import random
 from atroposlib.envs.base import BaseEnv, BaseEnvConfig, OpenaiConfig, ScoredDataItem
 from atroposlib.type_definitions import Item, Message
 from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
+from atroposlib.utils.tool_call_parser import parse_tool_call
 
 logger = logging.getLogger(__name__)
 
@@ -42,6 +44,39 @@ class BlackjackEnvNoThinking(BaseEnv):
         self.episode_outcomes_buffer: List[float] = []
         self.eval_metrics_custom: List[Tuple[str, float]] = []
 
+        # Define tools available to the LLM
+        self.tools = [
+            {
+                "type": "function",
+                "function": {
+                    "name": "take_action",
+                    "description": "Choose to 'hit' or 'stick' in Blackjack.",
+                    "parameters": {
+                        # Parameters are implicitly defined by the arguments of the function call
+                        # For this simple case, let's assume the LLM will provide arguments.action
+                        # based on the prompt. A more robust schema would define 'action' here.
+                        "type": "object",
+                        "properties": {
+                            "action": {"type": "string", "enum": ["hit", "stick"]}
+                        },
+                        "required": ["action"],
+                    },
+                },
+            }
+        ]
+
+        tools_json = json.dumps(self.tools)
+        # Updated system prompt for tool calling
+        self.system_prompt = (
+            "You are an AI agent playing Blackjack. "
+            "You need to decide whether to hit or stick based on your current hand and the dealer's showing card.\n\n"
+            f"<tools>\n{tools_json}\n</tools>\n\n"
+            "For your function call, return a JSON object with function name and arguments "
+            "within <tool_call> </tool_call> tags with the following schema:\n"
+            '<tool_call>\n{"arguments": {"action": "hit"}, "name": "take_action"}\n</tool_call>\n\n'
+            "Your full answer format should be (NO THINKING BLOCK):\n"
+            '<tool_call>\n{"arguments": {"action": "stick"}, "name": "take_action"}\n</tool_call>\n'
+        )
 
     @classmethod
     def config_init(cls) -> Tuple[BlackjackEnvNoThinkingConfig, List[OpenaiConfig]]:
@@ -76,12 +111,45 @@ class BlackjackEnvNoThinking(BaseEnv):
         )
 
     def _parse_action_from_llm(self, llm_response: str) -> Optional[int]:
-        """Parses 'hit' or 'stick' from the LLM response."""
-        action_str = llm_response.strip().lower()
-        if action_str in ACTION_STR_TO_INT:
-            return ACTION_STR_TO_INT[action_str]
-        logger.warning(f"Could not parse action from LLM response: '{llm_response}'")
-        return None
+        """Parses the action from the LLM's tool_call response."""
+        if not llm_response:
+            logger.warning(
+                "Attempted to parse an empty LLM response. Returning invalid action (None)."
+            )
+            return None
+
+        parsed_name, parsed_args, is_error = parse_tool_call(
+            llm_response, self.tools, ["tool_call"] # Expecting <tool_call>
+        )
+
+        if is_error:
+            error_detail = (
+                str(parsed_name) # Error message is in parsed_name if is_error
+                if parsed_name 
+                else "Parser indicated error, but no specific message was returned."
+            )
+            logger.warning(
+                f"Failed to parse tool call. Full response: '{llm_response}'. Error: {error_detail}"
+            )
+            return None
+
+        if parsed_name != "take_action":
+            logger.warning(
+                f"Expected tool call name 'take_action', but got '{parsed_name}'. Response: '{llm_response}'"
+            )
+            return None
+
+        action_str = parsed_args.get("action", "").lower()
+        if action_str == "hit":
+            return ACTION_HIT
+        elif action_str == "stick":
+            return ACTION_STICK
+        else:
+            logger.warning(
+                f"Successfully parsed tool call '{parsed_name}', but action argument is invalid. Action: '{action_str}'. "
+                f"Full response: '{llm_response}'. Parsed args: {parsed_args}"
+            )
+            return None
 
     async def collect_trajectory(
         self, item: Item
@@ -109,10 +177,8 @@ class BlackjackEnvNoThinking(BaseEnv):
             env.close()
             return None, []
 
-        system_prompt = (
-            "You are playing Blackjack. Respond with either 'hit' or 'stick'."
-        )
-        messages.append({"role": "system", "content": system_prompt})
+        # Use the class system_prompt
+        messages.append({"role": "system", "content": self.system_prompt})
 
         current_obs_str = self._format_observation(obs)
         messages.append({"role": "user", "content": current_obs_str})
@@ -126,7 +192,7 @@ class BlackjackEnvNoThinking(BaseEnv):
                     logger.warning(f"[Seed: {seed}] Max token length reached, truncating episode.")
                     break
 
-                max_tokens_for_action = 10
+                max_tokens_for_action = 512
 
                 try:
                     chat_completions = await server.chat_completion(
@@ -136,6 +202,7 @@ class BlackjackEnvNoThinking(BaseEnv):
                         temperature=0.5,
                     )
                     llm_action_response = chat_completions.choices[0].message.content.strip()
+                    logger.info(f"[Seed: {seed}] LLM Raw Response: '{llm_action_response}'") # Log raw response
                 except Exception as e:
                     logger.error(f"[Seed: {seed}] LLM API error: {e}")
                     break

environments/game_environments/gymnasium/blackjack_env_thinking.py

@@ -27,13 +27,10 @@ from atroposlib.envs.base import (
     OpenaiConfig,
     ScoredDataGroup,
 )
-from atroposlib.utils import (
-    tokenize_for_trainer,
-    parse_tool_call,
-    truncate_thinking,
-    ensure_trajectory_token_limit,
-    select_best_index
-)
+from atroposlib.utils.tokenize_for_trainer import tokenize_for_trainer
+from atroposlib.utils.message_history_utils import truncate_thinking
+from atroposlib.utils.tool_call_parser import parse_tool_call
+from atroposlib.utils.best_of_n_selection import select_best_index
 
 logger = logging.getLogger(__name__)
 

environments/game_environments/gymnasium/blackjack_local_server_no_thinking.py

@@ -0,0 +1,121 @@
+import asyncio
+import logging
+import os
+import random
+from typing import Optional
+
+from dotenv import load_dotenv
+
+from atroposlib.envs.base import EvalHandlingEnum, OpenaiConfig, ScoredDataItem
+from environments.game_environments.gymnasium.blackjack_env_no_thinking import (
+    BlackjackEnvNoThinking,
+    BlackjackEnvNoThinkingConfig,
+)
+
+load_dotenv()
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+async def main():
+    logger.info(
+        "Starting Blackjack (No Thinking) environment local debug runner"
+    )
+
+    env_config = BlackjackEnvNoThinkingConfig(
+        tokenizer_name="NousResearch/DeepHermes-3-Llama-3-8B-Preview",
+        group_size=1,
+        use_wandb=False,
+        wandb_name="blackjack_no_thinking_local_debug",
+        max_num_workers=1,
+        rollout_server_url="http://localhost:8000",
+        total_steps=1,
+        batch_size=1,
+        steps_per_eval=0,
+        max_token_length=1024,
+        inference_weight=1.0,
+        data_path_to_save_groups=None,
+        eval_handling=EvalHandlingEnum.NONE,
+        eval_limit_ratio=0.0,
+        env_name="Blackjack-v1",
+        max_episode_turns=10,
+        eval_episodes=0,
+    )
+    server_configs = [
+        OpenaiConfig(
+            model_name="gpt-4.1-nano",
+            base_url="https://api.openai.com/v1",
+            api_key=os.getenv("OPENAI_API_KEY"),
+            num_requests_for_eval=0,
+        )
+    ]
+    logger.info("Using hardcoded debug configuration for No Thinking Blackjack.")
+    logger.debug(f"Env Config: {env_config}")
+    logger.debug(f"Server Configs: {server_configs}")
+
+    try:
+        env = BlackjackEnvNoThinking(
+            config=env_config,
+            server_configs=server_configs,
+            slurm=False,
+            testing=False,
+        )
+    except Exception as e:
+        logger.exception(f"Failed to initialize BlackjackEnvNoThinking: {e}")
+        return
+
+    logger.info("Running a single trajectory directly using collect_trajectory")
+    try:
+        await env.setup()
+        seed = random.randint(0, 1000000)
+        item_for_env = {"seed": seed}
+        logger.info(f"Using seed: {seed} for item: {item_for_env}")
+
+        result_tuple = await env.collect_trajectory(item_for_env)
+        
+        scored_data_item: Optional[ScoredDataItem] = None
+        if result_tuple and result_tuple[0]:
+            scored_data_item = result_tuple[0]
+            logger.info(
+                f"Trajectory collection complete. Score: {scored_data_item.get('scores')}"
+            )
+            if env_config.include_messages and scored_data_item.get('messages'):
+                logger.info("Collected Messages:")
+                for i, msg in enumerate(scored_data_item['messages']):
+                    logger.info(f"  {i}. Role: {msg['role']}, Content: '{str(msg['content'])[:150]}...'")
+            logger.info(f"Tokens ({len(scored_data_item.get('tokens', []))}): {str(scored_data_item.get('tokens'))[:100]}...")
+            logger.info(f"Masks ({len(scored_data_item.get('masks', []))}): {str(scored_data_item.get('masks'))[:100]}...")
+        else:
+            logger.error("Trajectory collection did not return a ScoredDataItem.")
+
+        episode_summary_reward = None
+        if env.episode_outcomes_buffer:
+            episode_summary_reward = env.episode_outcomes_buffer[-1]
+
+        if episode_summary_reward is not None:
+            logger.info("\n========== Episode Summary ==========")
+            logger.info(f"Seed: {seed}")
+            logger.info(
+                f"Final Environment reward (Score): {episode_summary_reward:.2f}"
+            )
+            outcome_str = "Draw"
+            if episode_summary_reward > 0:
+                outcome_str = "Win"
+            elif episode_summary_reward < 0:
+                outcome_str = "Loss"
+            logger.info(f"Game Outcome: {outcome_str}")
+            logger.info("=======================================")
+        else:
+            logger.error(
+                f"Could not get episode summary for seed {seed} from metrics buffer."
+            )
+
+    except Exception as e:
+        logger.exception(
+            f"An error occurred during trajectory collection or summary: {e}"
+        )
+
+
+if __name__ == "__main__":
+    asyncio.run(main())