---

name: multi-agent-rl description: "Use AI-Optimizer easy-MARL tutorial code for multi-agent RL commands, environments, hyperparameters, and safe MARL extension." disable-model-invocation: true metadata: disco-role: operating license: MIT

Multi-Agent RL

Use this sub-skill when the task mentions multi-agent reinforcement learning (MARL), easy-MARL, IDQN, VDN, QMIX, CommNet, IDDPG, MADDPG, IPPO, MAPPO, discrete or continuous meeting environments, MAGYM, MPE, scenario names, or easy-MARL hyperparameter dispatch.

Do not use this sub-skill for model-based RL, offline RL, unrelated single-agent training, or external SMAC/API-network code. The AI-Optimizer MARL overview discusses those research directions, but the checked tutorial code covered here is the easy-MARL tutorial workflow. Treat uninitialized or external MARL code as a limitation, not an available runtime surface.

Fast route

1. Read references/marl-overview.md when the user asks what MARL capability exists in AI-Optimizer, how the algorithms differ, or how easy-MARL fits the broader taxonomy.
2. Read references/easy-marl-workflows.md to choose the correct entry script, algorithm family, environment family, scenario, and expected logs/models.
3. Read references/configuration-and-extension.md before changing hyperparameters, adding an algorithm, adding an environment/config pair, or repairing a missing dispatch branch.
4. Read references/troubleshooting.md when commands fail, scenario names are rejected, optional dependencies are missing, logs/models are unexpected, or a workflow would require expensive/full training.
5. Use scripts/build_easy_marl_command.py to build a shell-quoted easy-MARL command without importing ML libraries or starting training.

Command selection summary

• DQN-style discrete workflows use main_dqn.py with IDQN, VDN, QMIX, or the source-level CommNet branch on discrete_meeting or discrete_magym.
• DDPG-style continuous workflows use main_ddpg.py with IDDPG or MADDPG on continuous_meeting or continuous_mpe.
• PPO-style workflows use main_ppo.py with IPPO or MAPPO on any easy-MARL environment family.
• discrete_magym requires an explicit scenario name such as Switch4-v0 or Combat-v0; continuous_mpe requires an explicit scenario name such as simple_tag or simple_spread.
• The meeting environments do not use scenario names.

Example safe command construction:

python scripts/build_easy_marl_command.py --agent-name MAPPO --env-name continuous_mpe --scenario-name simple_tag

The helper prints the command to run from an easy-MARL working tree; it does not run training. Before executing a printed training command, verify dependencies and resource expectations. This sub-skill does not claim full RL training, CUDA, MAGYM, MPE, SMAC, or benchmark reproduction verification.