This library contains reinforcement learning environments for motion planning and object manipulation in the field of planar robotics. The environments follow either the Gymnasium API for single-agent RL or the PettingZoo parallel API for multi-agent RL. All environments are based on the MuJoCo physics engine. Note that this library depends on the latest MuJoCo Python bindings. mujoco-py is not supported.

The Gymnasium-Planar-Robotics package can be installed via PIP:

pip install gymnasium-planar-robotics

To install optional dependencies, to build the documentation, or to run the tests, use:

pip install gymnasium-planar-robotics[docs, tests]

Note: Depending on your shell (e.g. when using Zsh), you may need to use additional quotation marks:

pip install "gymnasium-planar-robotics[docs, tests]"

The documentation is available at: https://ubi-coro.github.io/gymnasium-planar-robotics/

GymPR is published under the GNU General Public License v3.0.

The following example shows how to use a trained policy with an example environment that follows the Gymnasium API:

import gymnasium as gym

env = gym.make("BenchmarkPushingEnv-v0", render_mode="human")
observation, info = env.reset(seed=42)

for _ in range(0,100):
    while not terminated and not truncated:
        action = policy(observation)  # custom policy
        observation, reward, terminated, truncated, info = env.step(action)

    observation, info = env.reset()
env.close()

Gymnasium-Planar-Robotics is currently maintained by Lara Bergmann (@lbergmann1).