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name: multi-agent-and-wrappers description: "Use AgileRL multi-agent PettingZoo workflows, MADDPG/MATD3/IPPO, vector envs, wrappers, agent grouping, and multi-agent replay setup." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

AgileRL Multi-Agent And Wrappers

Use this sub-skill for AgileRL multi-agent reinforcement learning with PettingZoo parallel environments, MADDPG, MATD3, IPPO, AsyncPettingZooVecEnv, make_multi_agent_vect_envs, multi-agent replay buffers, agent grouping, asynchronous-agent wrappers, and curriculum/Skill wrappers.

Read First

• references/workflows.md for PettingZoo vectorization and multi-agent training flow.
• references/api-reference.md for major algorithms, vector envs, wrappers, and buffers.
• references/configuration.md for agent IDs, homogeneous groups, and nested net_config.
• references/troubleshooting.md for ordering, wrapper, and vector-env issues.
• scripts/inspect_multi_agent_setup.py --help for a no-training config probe.

Boundaries

• Use ../hpo-and-mutation/SKILL.md for tournament/mutation probabilities.
• Use ../evolvable-modules/SKILL.md for network architecture details.
• Use ../training-workflows/SKILL.md for single-agent Gymnasium workflows.
• This sub-skill owns PettingZoo and multi-agent-specific spaces, IDs, vectorization, wrappers, and replay layout.

Multi-Agent Flow

1. Create a PettingZoo parallel environment factory.
2. Vectorize with make_multi_agent_vect_envs(...) or AsyncPettingZooVecEnv.
3. Capture agent_ids, observation_spaces, and action_spaces in the same order.
4. Define net_config globally, per agent, or per homogeneous group.
5. Create a population with create_population(algo="MADDPG" | "MATD3" | "IPPO", ...).
6. Use MultiAgentReplayBuffer for off-policy multi-agent algorithms.
7. Configure HPO and call the matching multi-agent training helper.

Supported Patterns

• MADDPG and MATD3: multi-agent off-policy actor-critic workflows.
• IPPO: independent PPO-style on-policy workflow, including homogeneous group parameter-sharing patterns.
• AsyncAgentsWrapper: asynchronous-turn handling for supported algorithms.
• Skill/curriculum wrappers: hierarchical curriculum-style workflows when the environment exposes lower-level skills.

Safe Validation

python scripts/inspect_multi_agent_setup.py --agents speaker_0 listener_0

The helper validates ID prefixes, synthetic spaces, and grouped config shape. It does not import external PettingZoo environments or train.