Summary of Multi-agent Off-policy Actor-critic Reinforcement Learning For Partially Observable Environments, by Ainur Zhaikhan and Ali H. Sayed
Multi-agent Off-policy Actor-Critic Reinforcement Learning for Partially Observable Environments
by Ainur Zhaikhan, Ali H. Sayed
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
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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 social learning method to estimate a global state within a multi-agent off-policy actor-critic algorithm for reinforcement learning (RL) operating in a partially observable environment. The algorithm assumes a fully-decentralized network of agents exchanging variables with their immediate neighbors. The study shows that the difference between final outcomes when the global state is fully observed versus estimated through social learning is ε-bounded after sufficient iterations. Unlike existing dec-POMDP-based RL approaches, this algorithm can be used for model-free multi-agent reinforcement learning without requiring knowledge of a transition model. Experimental results demonstrate the efficacy and superiority of the proposed algorithm over current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses a new way to help groups of robots learn together in uncertain environments. The robots share information with each other to guess what’s happening globally, which helps them make better decisions. This is different from usual approaches that require knowing how the environment works or having all the information at once. The results show that this method is effective and better than current methods. |
Keywords
* Artificial intelligence * Reinforcement learning
