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| """ | |
| Hackathon-compliant inference script for RevLogiXenv. | |
| Outputs structured stdout logs for Meta OpenEnv Hackathon Phase 2 validation. | |
| Required Environment Variables: | |
| - API_BASE_URL: OpenAI-compatible endpoint. | |
| - MODEL_NAME: LLM identifier (e.g., gpt-4o-mini). | |
| - HF_TOKEN: API key for the LLM. | |
| - TASKS: Comma-separated list of tasks (easy,medium,hard). | |
| Format: | |
| [START] task=<task> env=<benchmark> model=<model> | |
| [STEP] step=<n> action=<action> reward=<0.00> done=<true|false> error=<msg|null> | |
| [END] success=<true|false> steps=<n> score=<0.00> rewards=<r1,r2,...> | |
| """ | |
| from __future__ import annotations | |
| import os | |
| import re | |
| from dotenv import load_dotenv | |
| from openai import OpenAI | |
| from RevLogiXenv_v0 import AutonomousReturnsEnv | |
| from RevLogiXenv_v0.models import DispositionAction, ReturnsAction, ReturnsObservation | |
| from RevLogiXenv_v0.prompting import ( | |
| MINIMAL_SYSTEM_PROMPT, | |
| observation_to_prompt_inference as observation_to_prompt, | |
| parse_action_inference as parse_action, | |
| ) | |
| load_dotenv() | |
| API_BASE_URL = os.getenv("API_BASE_URL", "https://api.openai.com/v1") | |
| MODEL_NAME = os.getenv("MODEL_NAME", "gpt-4.1-mini") | |
| HF_TOKEN = os.getenv("HF_TOKEN") | |
| if not HF_TOKEN: | |
| raise ValueError("HF_TOKEN environment variable is required") | |
| TASKS = os.getenv("TASKS", "easy,medium,hard") | |
| BENCHMARK = os.getenv("BENCHMARK", "revlogixenv_v0") | |
| MAX_STEPS = 100 | |
| SUCCESS_SCORE_THRESHOLD = 0.5 | |
| TEMPERATURE = 0.15 | |
| MAX_TOKENS = 64 | |
| # Bounds that keep every per-step reward strictly within (0, 1). | |
| # The hackathon validator derives the task score from the rewards list; | |
| # keeping every value in (0.01, 0.99) guarantees any aggregation | |
| # (average, sum-normalised, etc.) stays within the required open interval. | |
| _REWARD_MIN = 0.01 | |
| _REWARD_MAX = 0.99 | |
| def log_start(task: str, env: str, model: str) -> None: | |
| print(f"[START] task={task} env={env} model={model}", flush=True) | |
| def log_step(step: int, action: str, reward: float, done: bool, error: str | None) -> None: | |
| # Spec: done is lowercase boolean string | |
| done_str = "true" if done else "false" | |
| error_value = error if error else "null" | |
| print( | |
| f"[STEP] step={step} action={action} reward={reward:.2f} " | |
| f"done={done_str} error={error_value}", | |
| flush=True, | |
| ) | |
| def log_end(success: bool, steps: int, score: float, rewards: list[float]) -> None: | |
| """Final task log — matches reference repo format exactly.""" | |
| rewards_str = ",".join(f"{r:.2f}" for r in rewards) | |
| print(f"[END] success={str(success).lower()} steps={steps} score={score:.2f} rewards={rewards_str}", flush=True) | |
| def _clamp_reward(r: float) -> float: | |
| """Ensure every per-step reward is strictly within (0, 1).""" | |
| return max(_REWARD_MIN, min(_REWARD_MAX, r)) | |
| def get_model_response(client: OpenAI, prompt: str) -> str: | |
| try: | |
| completion = client.chat.completions.create( | |
| model=MODEL_NAME, | |
| messages=[ | |
| {"role": "system", "content": MINIMAL_SYSTEM_PROMPT}, | |
| {"role": "user", "content": prompt}, | |
| ], | |
| temperature=TEMPERATURE, | |
| max_tokens=MAX_TOKENS + 1024, | |
| stream=False, | |
| extra_body={"reasoning_split": True}, | |
| ) | |
| content = (completion.choices[0].message.content or "").strip() | |
| if content: | |
| return content | |
| reasoning_details = getattr(completion.choices[0].message, "reasoning_details", None) | |
| if isinstance(reasoning_details, list): | |
| parts: list[str] = [] | |
| for chunk in reasoning_details: | |
| if isinstance(chunk, dict) and "text" in chunk: | |
| parts.append(chunk["text"] or "") | |
| return "".join(parts) | |
| except Exception: | |
| return "wait" | |
| return "wait" | |
| def run_episode(client: OpenAI, task_name: str) -> tuple[bool, int, list[float]]: | |
| rewards: list[float] = [] | |
| steps_taken = 0 | |
| success = False | |
| log_start(task=task_name, env=BENCHMARK, model=MODEL_NAME) | |
| env = AutonomousReturnsEnv(task_name) | |
| try: | |
| obs = env.reset(seed=42) | |
| for step in range(1, MAX_STEPS + 1): | |
| if obs.done: | |
| break | |
| prompt = observation_to_prompt(obs) | |
| message = get_model_response(client, prompt) | |
| action = parse_action(message, obs) | |
| obs = env.step(action) | |
| # Clamp to (0.01, 0.99) so the rewards CSV never contains 0.0 or 1.0, | |
| # keeping the validator's derived task score strictly within (0, 1). | |
| raw_reward = float(obs.reward) if obs.reward is not None else _REWARD_MIN | |
| reward = _clamp_reward(raw_reward) | |
| done = bool(obs.done) | |
| error = None | |
| if isinstance(obs.metadata, dict): | |
| raw_error = obs.metadata.get("last_action_error") | |
| error = raw_error if isinstance(raw_error, str) and raw_error else None | |
| rewards.append(reward) | |
| steps_taken = step | |
| log_step(step=step, action=action.action.value, reward=reward, done=done, error=error) | |
| if done: | |
| break | |
| score = sum(rewards) / len(rewards) if rewards else 0.0 | |
| score = max(1e-6, min(score, 1 - 1e-6)) # strictly within (0, 1) — matches reference | |
| success = score >= SUCCESS_SCORE_THRESHOLD | |
| finally: | |
| try: | |
| env.close() | |
| except Exception: | |
| pass | |
| log_end(success=success, steps=steps_taken, score=score, rewards=rewards) | |
| return success, steps_taken, rewards | |
| def main() -> None: | |
| client = OpenAI(base_url=API_BASE_URL, api_key=HF_TOKEN) | |
| task_names = [t.strip() for t in TASKS.split(",") if t.strip()] | |
| for task_name in task_names: | |
| run_episode(client, task_name) | |
| if __name__ == "__main__": | |
| main() | |