Manufacturing Reinforcement Learning Environment

Acknowledgements

This work was developed at the Institute for Advanced Manufacturing at the University of Nottingham, in collaboration with the Software and systems engineering and the High-performance machining groups at Mondragon University, as part of the Digital Manufacturing and Design Training Network.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 814078.

Please refer to this file for problem formulation and the environment description.

Installation

Clone

git clone git@github.com:torayeff/mfgrl.git

Install

pip install -e mfgrl

Usage

import gymnasium as gym

env_config = {
    "data_file": "data.json",
    "stochastic": True,
    "render_mode": "human",
}
env = gym.make("mfgrl:mfgrl/MfgEnv-v0", env_config=env_config)
obs, info = env.reset()

while True:
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated:
        break