Research map / Multi-agent systems and games / Cooperative multi-agent RL
Trust Region Policy Optimisation in Multi-Agent Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use sequential trust-region updates without requiring parameter sharing | HATRPO and HAPPO · Multi-Agent MuJoCo tasks | reward | Helped |
| Use hard KL constraints rather than PPO-style clipping | HATRPO versus HAPPO · Multi-Agent MuJoCo tasks | reward | Helped |
| Use HATRPO or HAPPO instead of MAPPO | SMAC · 2c-vs-64zg, 3s5z, and corridor | win rate: 100% win rate | No clear effect |
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- Scalable Constrained Policy Optimization for Safe Multi-agent Reinforcement Learning (NeurIPS 2024 · similarity 0.91)
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- Embedding Safety into RL: A New Take on Trust Region Methods (ICML 2025 · similarity 0.88)
- Towards Principled Unsupervised Multi-Agent Reinforcement Learning (NeurIPS 2025 · similarity 0.88)
- Maximum Entropy Heterogeneous-Agent Reinforcement Learning (ICLR 2024 · similarity 0.88)
- Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean Field Games (ICML 2025 · similarity 0.88)
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