new env runs locally

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())