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| """ | |
| register.py | |
| =========== | |
| Register the HealthcareRoutingEnv with Gymnasium so it can be created | |
| with gym.make("healthcare-routing-v1"). | |
| Also provides the OpenEnv-spec environment_config dict for | |
| submission to the HuggingFace Hub. | |
| Usage: | |
| python register.py # verify registration works | |
| import register # in any script to auto-register | |
| """ | |
| import gymnasium as gym | |
| from gymnasium.envs.registration import register | |
| # ── Gymnasium registration ─────────────────────────────────────────────────── | |
| register( | |
| id = "healthcare-routing-v1", | |
| entry_point = "openenv_env.healthcare_env:HealthcareRoutingEnv", | |
| max_episode_steps = 200, | |
| reward_threshold = 100.0, | |
| kwargs = {}, | |
| ) | |
| # ── OpenEnv spec dict (for Hub submission) ─────────────────────────────────── | |
| ENVIRONMENT_CONFIG = { | |
| "env_id": "healthcare-routing-v1", | |
| "version": "1.0.0", | |
| "description": ( | |
| "AI-Powered Smart Healthcare Routing & Emergency Management. " | |
| "An RL agent dispatches the optimal ambulance and hospital for each " | |
| "incoming emergency patient, maximising survival outcomes while " | |
| "minimising response times and wasted resources." | |
| ), | |
| "tags": ["healthcare", "routing", "multi-objective", "real-world", "emergency"], | |
| "observation_type": "Box", | |
| "action_type": "Discrete", | |
| "reward_range": (-200, 220), | |
| "authors": ["Healthcare-RL Team"], | |
| "license": "MIT", | |
| "gym_make_kwargs": {}, | |
| "entry_point": "openenv_env.healthcare_env:HealthcareRoutingEnv", | |
| } | |
| def make_env(**kwargs): | |
| """Convenience factory used by OpenEnv infrastructure.""" | |
| from openenv_env.healthcare_env import HealthcareRoutingEnv | |
| return HealthcareRoutingEnv(**kwargs) | |
| if __name__ == "__main__": | |
| print("Registering healthcare-routing-v1 with Gymnasium …") | |
| env = gym.make("healthcare-routing-v1") | |
| obs, info = env.reset() | |
| print(f" ✅ Environment created successfully") | |
| print(f" Observation shape : {obs.shape}") | |
| print(f" Action space : {env.action_space}") | |
| print(f" Config : {ENVIRONMENT_CONFIG['env_id']} v{ENVIRONMENT_CONFIG['version']}") | |
| env.close() | |
| print("Registration OK 🎉") | |