Summary of Last-iterate Convergence Of Payoff-based Independent Learning in Zero-sum Stochastic Games, by Zaiwei Chen et al.
Last-Iterate Convergence of Payoff-Based Independent Learning in Zero-Sum Stochastic Games
by Zaiwei Chen, Kaiqing Zhang, Eric Mazumdar, Asuman Ozdaglar, Adam Wierman
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT)
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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 novel learning dynamics for two-player zero-sum matrix and stochastic games. These dynamics are designed to be payoff-based, convergent, rational, and symmetric between the players. The authors develop smoothed best-response dynamics for matrix games and build upon these to create learning dynamics for stochastic games. To their knowledge, this is the first finite-sample analysis of such learning dynamics with last-iterate guarantees. The results imply sample complexities of O(ε^-1) and O(ε^-8) for finding Nash distributions and equilibria in matrix and stochastic games, respectively. To establish these results, the authors developed a coupled Lyapunov-based approach that may be of independent interest to those studying stochastic approximation algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper studies how two players make decisions when playing a game. The researchers create new ways for the players to learn from each other and find the best moves. They test these methods on different types of games, including ones where the outcome is certain (matrix game) and ones where there’s some chance involved (stochastic game). The results show that it takes a certain number of “experiments” or samples to find the best strategy. This research might be useful for people studying how machines learn from each other. |
