Generative Reinforcement Learning

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GenerativeRL, short for Generative Reinforcement Learning, is a Python library for solving reinforcement learning (RL) problems using generative models, such as diffusion models and flow models. This library aims to provide a framework for combining the power of generative models with the decision-making capabilities of reinforcement learning algorithms.

Features

• Integration of diffusion models and flow models for state representation and policy learning in RL
• Implementation of popular RL algorithms tailored for generative models
• Support for various RL environments and benchmarks
• Easy-to-use API for training and evaluation

Installation

pip install grl

Or, if you want to install from source:

git clone https://github.com/zjowowen/GenerativeRL_Preview.git
cd generative-rl
pip install -e .

Quick Start

Here is an example of how to train a diffusion model for Q-guided policy optimization (QGPO) in the MuJoCo environment:

from grl_pipelines.configurations.halfcheetah_qgpo import config
from grl.algorithms import QGPOAlgorithm
from grl.utils.log import log
import gym

def qgpo_pipeline(config):

    qgpo = QGPOAlgorithm(config)
    qgpo.train()

    agent = qgpo.deploy()
    env = gym.make(config.deploy.env.env_id)
    env.reset()
    for _ in range(config.deploy.num_deploy_steps):
        env.render()
        env.step(agent.act(env.observation))

if __name__ == '__main__':
    log.info("config: \n{}".format(config))
    qgpo_pipeline(config)

For more detailed examples and documentation, please refer to the GenerativeRL documentation.

Contributing

We welcome contributions to GenerativeRL! If you are interested in contributing, please refer to the Contributing Guide.

License

GenerativeRL is licensed under the Apache License 2.0. See LICENSE for more details.