Summary of Comadice: Offline Cooperative Multi-agent Reinforcement Learning with Stationary Distribution Shift Regularization, by the Viet Bui and Thanh Hong Nguyen and Tien Mai
ComaDICE: Offline Cooperative Multi-Agent Reinforcement Learning with Stationary Distribution Shift Regularization
by Viet Bui, Thanh Hong Nguyen, Tien Mai
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a new algorithm for offline multi-agent reinforcement learning (MARL) called ComaDICE. The authors address the challenge of distributional shift, which occurs when the target policy deviates from the behavior policy that generated the data. They introduce a regularizer in the space of stationary distributions to handle this issue and combine it with a value decomposition strategy for multi-agent training. The algorithm is tested on various MARL benchmarks, including MuJoCo and StarCraft II, and outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Offline learning can help robots learn new skills without needing to interact with the environment again. But when many agents work together, it gets harder because they all have different goals and actions. The authors of this paper found a way to make offline multi-agent learning better by using something called a “stationary distribution regularizer.” This helps the agents learn to work together more effectively. They tested their method on some big datasets and showed that it works better than other methods. |
Keywords
* Artificial intelligence * Reinforcement learning
