reference/generated/torchrl.envs.MujocoPlaygroundAgentMapping

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MujocoPlaygroundAgentMapping

torchrl.envs.MujocoPlaygroundAgentMapping(agents: list[~torchrl.envs.libs.mujoco_playground.MujocoPlaygroundAgentSpec] = <factory>, homogenization_mode: ~typing.Literal['none', 'max', 'concat'] = 'none') → None

Agent mapping for MujocoPlaygroundWrapper.

Defines how to split a single-agent MuJoCo Playground environment into a cooperative multi-agent task by partitioning the observation and action vectors among named agents.

Parameters:

• agents (list of MujocoPlaygroundAgentSpec) – one entry per agent, defining each agent’s observation slice and the action indices it controls.

• homogenization_mode (str, optional) –

  strategy for unifying heterogeneous observation/action shapes across agents so that a single shared policy can be used.

  • "none" (default): each agent receives exactly its own observation/action slice; shapes may differ across agents.

  • "max": observations are padded to max_obs_size + n_agents (a one-hot agent-ID prefix is prepended), and actions are padded to max_action_size. All agents share the same input/output shape.

    Policy contract (max): each agent’s observation layout is [one_hot_id (n_agents) | raw_obs (len(observation_indices)) | zero_pad], for a total length of max_obs_size + n_agents. The action vector emitted by the policy has length max_action_size; only the first len(action_indices) entries are used and the rest are silently dropped.

  • "concat": each agent receives the full global observation/action vector with zeros at positions it does not own. All agents share the same input/output shape equal to the full environment dimensions.

    Policy contract (concat): the observation has length env.observation_size with zeros at positions not owned by the agent. The policy must emit a full-length action vector; only entries at the agent’s own action_indices are applied to the environment and entries at indices owned by other agents are ignored.

Examples

>>> mapping = MujocoPlaygroundAgentMapping(
...     agents=[
...         MujocoPlaygroundAgentSpec(
...             name="agent_0",
...             action_indices=[0, 1, 2],
...             observation_indices=[0, 1, 2, 3],
...         ),
...         MujocoPlaygroundAgentSpec(
...             name="agent_1",
...             action_indices=[3, 4, 5],
...             observation_indices=[4, 5, 6, 7],
...         ),
...     ],
...     homogenization_mode="none",
... )