Research map / Multi-agent systems and games / Cooperative multi-agent RL
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| centralized critic that conditions on all agents' actions (MADDPG) instead of decentralized RL | multi-agent actor-critic · Cooperative communication | listener reaches target | Helped |
| approximating other agents' policies online instead of using true policies (ablation) | MADDPG · Cooperative communication | average reward | No clear effect |
| training agents with policy ensembles (K sub-policies) (ablation) | MADDPG · Keep-away, cooperative navigation, predator-prey | win rate vs single-policy adversaries | Helped |
| centralized critic (MADDPG) vs decentralized DDPG | cooperative navigation | collisions per episode (N=2): roughly half the collisions | Helped |
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- Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning (NeurIPS 2020 · similarity 0.90)
- Multi-agent cooperation through learning-aware policy gradients (ICLR 2025 · similarity 0.90)
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