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).