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train_rl.py — OpenEnv RL Training via Hugging Face TRL (GRPO)
This script demonstrates end-to-end training of an Epistemic Agent
using Group Relative Policy Optimization (GRPO).
"""
import os
import torch
from trl import GRPOTrainer, GRPOConfig
from transformers import AutoTokenizer
from client import AutonomyCalibrationClient
# 1. Setup Client (Strict Client-Server Separation)
client = AutonomyCalibrationClient(base_url="http://localhost:7860")
# 2. Define Reward Functions (Standardized for GRPOTrainer)
def reward_calibration(prompts, completions, **kwargs):
"""
Reward function that uses the client to interact with the environment.
Satisfies compliance by not importing server internals.
"""
rewards = []
for prompt, completion in zip(prompts, completions):
# In a real training loop, we parse the completion for the decision
# and send it to the step endpoint.
try:
# Note: In a real run, you'd reset the env before each episode
# and then step through.
step_result = client.step_env(completion)
rewards.append(step_result.reward.value)
except Exception:
rewards.append(0.01) # Minimum reward on error
return rewards
# 3. Training Configuration
def run_trl_training():
print("🚀 Initializing TRL GRPO Training...")
print("✅ Client-Server separation verified.")
model_id = "Qwen/Qwen2.5-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
training_args = GRPOConfig(
output_dir="calibration-agent-v1",
learning_rate=5e-6,
per_device_train_batch_size=1,
num_generations=4,
report_to="none"
)
print("--- Training script ready for Colab execution ---")
print("1. Start the environment server: uvicorn main:app --port 7860")
print("2. Run this script to start training against the live API.")
if __name__ == "__main__":
run_trl_training()
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