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 shape of the action space depends on the partitioning. The partitioning has the following form:

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.

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