Summary of Scalable Offline Reinforcement Learning For Mean Field Games, by Axel Brunnbauer et al.
Scalable Offline Reinforcement Learning for Mean Field Games
by Axel Brunnbauer, Julian Lemmel, Zahra Babaiee, Sophie Neubauer, Radu Grosu
First submitted to arxiv on: 23 Oct 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 A novel reinforcement learning algorithm for mean-field games, Offline Munchausen Mirror Descent (Off-MMD), is presented that can optimize policies in large populations of interacting agents using purely offline data. This scalable framework leverages iterative mirror descent and importance sampling techniques to estimate the mean-field distribution from static datasets without relying on simulation or environment dynamics. To address common issues like Q-value overestimation, Off-MMD incorporates techniques from offline reinforcement learning. The algorithm is shown to perform well on benchmark tasks like crowd exploration or navigation, making it applicable to real-world multi-agent systems where online experimentation is infeasible. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning helps computers learn from experience. In this paper, researchers developed a new way for computers to learn how to make good decisions when many people are involved. They used “offline” data, which means they didn’t need to simulate or experiment with the real world. The algorithm is called Offline Munchausen Mirror Descent (Off-MMD) and it’s really good at making decisions in complex situations. This could be helpful for things like planning how crowds move through a city or deciding where robots should go. |
Keywords
* Artificial intelligence * Reinforcement learning
