Summary of Offline Multi-agent Reinforcement Learning Via In-sample Sequential Policy Optimization, by Zongkai Liu et al.
Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization
by Zongkai Liu, Qian Lin, Chao Yu, Xiawei Wu, Yile Liang, Donghui Li, Xuetao Ding
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new offline multi-agent reinforcement learning algorithm called In-Sample Sequential Policy Optimization (InSPO) to address issues in existing offline MARL methods. InSPO sequentially updates each agent’s policy while considering teammates’ updated policies, and also explores low-probability actions in the behavior policy to prevent premature convergence. Theoretical analysis shows that InSPO guarantees monotonic policy improvement and converges to quantal response equilibrium (QRE). Experimental results demonstrate its effectiveness compared to current state-of-the-art offline MARL methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline learning is tricky when many agents are involved. Right now, we don’t have the best way to teach multiple agents how to work together. A team of researchers came up with a new plan to make this happen. They created an algorithm that helps each agent learn from what others are doing. This makes sure everyone works together well and doesn’t get stuck in bad habits. The new method is better than what we have now, which gets stuck and doesn’t work as well. |
Keywords
» Artificial intelligence » Optimization » Probability » Reinforcement learning
