Single Action Environments#
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
The Purpose of these is to allow researchers to debug multi-agent learning algorithms.
The action spaces is depended on the partitioning.
The agent receives the same observations as the single agent Gymnasium environment.
The agent receive the same reward as the single agent Gymnasium environment.
The starting state of the environment is the same as single agent Gymnasium environment.
The agent terminates and truncates at the same time, given the same conditions as the single agent Gymnasium environment.