Summary of Randomized Exploration in Cooperative Multi-agent Reinforcement Learning, by Hao-lun Hsu et al.
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
by Hao-Lun Hsu, Weixin Wang, Miroslav Pajic, Pan Xu
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 study proposes a unified algorithm framework for randomized exploration in cooperative multi-agent reinforcement learning (MARL) and two Thompson Sampling-type algorithms that achieve efficient exploration. The algorithms are designed to be flexible and easy to implement, with regret bounds proven theoretically for a special class of parallel Markov Decision Processes (MDPs). The results are evaluated on multiple RL environments, including video games and real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores ways for machines to learn together in teams. It develops new algorithms that allow agents to share information and work together more effectively. The researchers test their ideas on different scenarios, like a deep exploration problem and a video game, and show that their approach can perform better than other methods. |
Keywords
» Artificial intelligence » Reinforcement learning
