The behavior is the same as the original repo when agent_obsk=None, it simply becomes a single agent environment, it was just missdocumented

https://github.com/schroederdewitt/multiagent_mujoco/blob/b212ddd74b258e7cea006ff1d642b5ffada4b99d/multiagent_mujoco/mujoco_multi.py#L43

https://github.com/schroederdewitt/multiagent_mujoco/blob/b212ddd74b258e7cea006ff1d642b5ffada4b99d/multiagent_mujoco/mujoco_multi.py#L67

I think the main difference is

https://github.com/schroederdewitt/multiagent_mujoco/blob/b212ddd74b258e7cea006ff1d642b5ffada4b99d/multiagent_mujoco/mujoco_multi.py#L108-L116

and

In the original repo, the final action to use is constructed by multiple agents, while the final action is decided by the first agent in this repo, when agent_obsk == None.

I make a PR #228 and pass my tests in https://github.com/xihuai18/MaMuJoCo-PettingZoo/blob/main/tests/mamujoco_pettingzoo_v1_test.py, I think these changes make the current implementation perform the same behavior as the original repo.

1. Can you provide an example of the behavior of agent_obsk=None when using the original implementation

>>> import gymnasium_robotics         
>>> from gymnasium_robotics import mamujoco_v1
>>> env = mamujoco_v1.parallel_env("Ant", "2x4", agent_obsk=None)
>>> env.action_spaces
{'agent_0': Box(-1.0, 1.0, (8,), float32)}
>>> env.observation_spaces
{'agent_0': Box(-inf, inf, (105,), float64)}

2. regardless of (1.), what is the reason you want fully observable multi-agent factorizations of the environments, this is equivalent to a single agent environment, with extra steps (https://discord.com/channels/765294874832273419/808462033866588180/1261698405630214185 )