README.md

metadata

title: CERNenv
emoji: ⚛️
colorFrom: blue
colorTo: indigo
sdk: docker
suggested_hardware: cpu-basic
pinned: false
license: bsd-3-clause
short_description: LHC particle-discovery RL environment

CERNenv — LHC Discovery RL Environment

OpenEnv-compatible reinforcement-learning environment that simulates an LHC (Large Hadron Collider) analysis. An LLM (Large Language Model) agent configures the beam, allocates luminosity, picks a decay channel and trigger, runs reconstruction, fits an invariant-mass spectrum, estimates significance, and finally submits a structured discovery claim that is graded against a hidden ground-truth particle.

The Space exposes the standard OpenEnv HTTP + WebSocket API:

• GET /health — liveness
• GET /schema — action / observation / state JSON schemas
• POST /reset — start a new episode ({ "seed": 7, "scenario": "easy_diphoton_160" })
• POST /step — execute one action
• GET /state — current CernState
• WS /ws — persistent session (recommended for multi-step rollouts)

Quickstart (Python client)

import asyncio
from openenv.core import EnvClient
from huggingface_hub import constants

# replace with your space id
SPACE = "YOUR_HF_USERNAME/cernenv"

# (option A) connect to the running Space directly
import websockets
async def main():
    async with EnvClient.from_env(SPACE) as env:  # uses websockets under the hood
        result = await env.reset(seed=7, scenario="easy_diphoton_160")
        ...

asyncio.run(main())

For training, see the companion CERNenv Trainer Space.