Single Action Environments

MaMuJoCo also supports single action Gymansium/MuJoCo/ environments such as Gymnasium/Mujoco/InvertedPendulum and Gymnasium/Mujoco/InvertedDoublePendulum.

And can be instantiated (without a partition):

env = mamujoco_v0.parallel_env("InvertedDoublePendulum", None)

env = mamujoco_v0.parallel_env("InvertedPendulum", None)

In which case, they simply are the same environments with a single agent using the PettingZoo APIs.

The Purpose of these is to allow researchers to debug multi-agent learning algorithms.

Action Space

The action spaces is depended on the partitioning.

Observation Space

The agent receives the same observations as the single agent Gymnasium environment.

Rewards

The agent receive the same reward as the single agent Gymnasium environment.

Starting state

The starting state of the environment is the same as single agent Gymnasium environment.

Episode End

The agent terminates and truncates at the same time, given the same conditions as the single agent Gymnasium environment.

Version History

v0: Initial version release, uses Gymnasium.MuJoCo-v4, and is a fork of the original multiagent_mujuco. No Changes from the original MaMuJoCo (schroederdewitt/multiagent_mujoco).