Gymnasium Environments

OpenSMC provides a set of Gymnasium-compatible environments for training Reinforcement Learning agents to discover or optimize sliding mode control components.

Usage Example

import gymnasium as gym
import opensmc.envs

# Create a crane environment
env = gym.make("OpenSMC/Crane-v0")

# Reset and take a random step
obs, info = env.reset()
obs, reward, terminated, truncated, info = env.step(env.action_space.sample())

Available Environments

envs

OpenSMC Gymnasium Environments — RL-ready wrappers for plants.

Usage

import gymnasium as gym import opensmc # registers environments

env = gym.make("OpenSMC/DoubleIntegrator-v0") obs, info = env.reset() obs, reward, terminated, truncated, info = env.step(action)

Or directly

from opensmc.envs import DoubleIntegratorEnv env = DoubleIntegratorEnv()

Requires gymnasium: pip install opensmc[rl]