Show HN: REST API for Gymnasium (fka OpenAI Gym) reinforcement learning library

REST interface for the Gymnasium library to allow for language-agnostic development of reinforcement learning agents. Spiritual successor to openai/gym-http-api. The fastest way to get started is to pull and run the latest version of the associated container image from GHCR: docker run --rm -it -p 5000:5000 ghcr.io/cloudkj/gymnasium-http-api Alternatively, you can clone the code and run the server directly: python3 -m uvicorn app.main:app --port 5000 --host 0.0.0.0 Once the server is running, create and interact with environments through the various endpoints. Here's a command-line example of creating an environment, resetting its state, then taking one action. % curl -X POST http://localhost:5000/v1/envs/ -H 'Content-Type: application/json' -d '{"env_id":"CartPole-v1"}' {"instance_id":"986a40f7-6472-4ac6-bc5f-86b7939898b1"} % curl -X POST http://localhost:5000/v1/envs/986a40f7-6472-4ac6-bc5f-86b7939898b1/reset/ {"observation":[-0.001753740361891687,0.0477466844022274,0.003655971959233284,-0.030443252995610237],"info":{}} % curl -X POST http://localhost:5000/v1/envs/986a40f7-6472-4ac6-bc5f-86b7939898b1/step/ -H 'Content-Type: application/json' -d '{"action": 0}' {"observation":[-0.0037473568227142096,-0.3425928056240082,0.008314925245940685,0.557033360004425],"reward":1.0,"terminated":false,"truncated":false,"info":{}} To see an agent in action, check out the standalone, client-side Javascript agent available at /agent.html which shows a typical agent loop over a single episode. You can also modify the agent code directly in your browser and try out different heuristics or policies. To observe the state of all active environments on the server, check out the monitoring page available at /monitor.html . To start developing an agent, simply call endpoints to create and interact with the environment of your choice. Here's a simple Python example that wraps the main endpoints as a drop-in replacement for gymnasium.Env : import requests class Env: BASE_URL = "http://localhost:5000/v1/envs" def __init__(self, env_id): self.instance_id = requests.post(f"{self.BASE_URL}/", json={"env_id": env_id}).json()["instance_id"] def reset(self): return requests.post(f"{self.BASE_URL}/{ self.instance_id}/reset/", json={}).json() def step(self, action): return requests.post(f"{self.BASE_URL}/{self.instance_id}/step/", json={"action": action}).json() def close(self): requests.delete(f"{self.BASE_URL}/{self.instance_id}/") # Create an environment and reset state to start a new episode env = Env("CartPole-v1") initial = env.reset() observation, info = initial["observation"], initial["info"] print(f"Starting observation: {observation}") episode_over = False total_reward = 0 while not episode_over: action = 0 # To the left, to the left # Take the action and see what happens state = env.step(action) total_reward += state["reward"] episode_over = state["terminated"] or state["truncated"] print(f"Episode finished! Total reward: {total_reward}") env.close() The endpoints largely follow the conventions established by the legacy gym-http-api project. For the latest documentation, start an instance of the server and navigate to /docs to view auto-generated documentation for all supported endpoints. GET /v1/envs/ - List all active environment instancesPOST /v1/envs/ - Create an instance of the specified environmentPOST /v1/envs/{instance_id}/reset/ - Reset the environmentPOST /v1/envs/{instance_id}/step/ - Step through the environment with a specified actionGET /v1/envs/{instance_id}/action_space/ - Get action space propertiesGET /v1/envs/{instance_id}/observation_space/ - Get observation space propertiesDELETE /v1/envs/{instance_id}/ - Close and remove the environment GET /v1/envs/{instance_id}/monitor/render/ - Render the current state of the environmentGET /v1/envs/{instance_id}/monitor/stream/ - Continuously stream the current state of the environment