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name: multi-agent-and-runner description: "Route skrl 2.1.0 IPPO/MAPPO multi-agent workflows, framework Runner configuration, model and memory dictionaries, trainer scopes, and distributed/checkpoint boundaries." disable-model-invocation: true metadata: disco-role: operating license: MIT

Multi-agent and Runner

Use this route when the request names IPPO, MAPPO, PettingZoo or another multi-agent environment, per-agent model/memory dictionaries, a centralized state/value function, Runner/YAML configuration, trainer scopes, or multi-process/distributed execution. It owns the multi-agent contract and declarative Runner machinery; it does not replace environment wrapping or the single-agent framework guides.

Route quickly

1. Establish a framework-wrapped multi-agent environment first. Confirm possible_agents, current agents, observation_spaces, state_spaces, action_spaces, device, and num_envs. For PettingZoo or Isaac Lab, start with ../environment-integration/SKILL.md.
2. Choose the learning semantics in references/multi-agent-workflows.md: IPPO is decentralized training/execution with an independent value model; MAPPO is centralized training/decentralized execution through a state-based value model. Both expose the same per-agent constructor shape.
3. Build nested dictionaries keyed by every possible_agents entry: models[agent_id]["policy"], models[agent_id]["value"], and, for training, memories[agent_id]. A single-agent model dictionary is not a multi-agent dictionary. Validate role keys, spaces, and model initialization before constructing the algorithm.
4. For declarative setup, use references/runner-configuration.md. A Runner needs a wrapped environment and top-level models, memory, agent, and trainer configuration. Treat YAML as trusted input: Runner evaluates selected component strings and expression fields.
5. Select a trainer deliberately. A single IPPO/MAPPO object normally uses SequentialTrainer, StepTrainer, or direct loop ownership. ParallelTrainer is a Torch multiprocessing facility, not the same thing as a multi-agent environment. Scope validation and simultaneous-agent limitations are in the workflow reference.
6. Before resuming, check framework-specific checkpoint format, experiment directory, rank ownership of logging, and model-role compatibility. Use references/troubleshooting.md for shape, key, migration, YAML, scope, and distributed failures.

Framework boundaries

• Torch: IPPO and MAPPO are available; Torch additionally supports a policy/value shared model through shared_model and automatic mixed precision. ParallelTrainer is Torch-only.
• JAX: IPPO and MAPPO are available with separate policy/value models. Runner does not support shared policy/value models and removes the unsupported mixed-precision setting. Call init_state_dict for generated models before use.
• Warp: the Warp Runner and sequential trainer cover single-agent Warp agents. This release has no Warp IPPO/MAPPO mapping; do not route a Warp multi-agent request to this path as if it were supported. See ../warp-agent-training/SKILL.md for the single-agent boundary.
• Framework-specific model construction belongs to ../torch-agent-training/SKILL.md, ../jax-agent-training/SKILL.md, or ../warp-agent-training/SKILL.md. Link there for mixin or device details rather than duplicating those guides.

Completion checklist

A safe handoff has: a wrapper-derived, immutable possible_agents list; all model and memory dictionaries covering it; policy/action and value/state space compatibility; an explicit choice between IPPO and MAPPO; a finite trainer timestep plan; validated scopes if multiple agents share a vectorized environment; trusted Runner/YAML component names if used; explicit logging and checkpoint paths; and a framework/distributed boundary statement. Do not run full multi-agent training or an unreviewed user YAML merely to diagnose its shape.