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.


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).