Summary of Rl in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound Under the Local Access Model, by Junyi Fan et al.
RL in Markov Games with Independent Function Approximation: Improved Sample Complexity Bound under the Local Access Model
by Junyi Fan, Yuxuan Han, Jialin Zeng, Jian-Feng Cai, Yang Wang, Yang Xiang, Jiheng Zhang
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents an innovative approach to learning equilibria in Markov games with large state and action spaces. The authors address the challenge of overcoming the curse of multi-agency, where existing methods have suboptimal dependencies on desired accuracy or action space size. They introduce Lin-Confident-FTRL, a new algorithm that learns coarse correlated equilibria (CCE) with local access to the simulator. This approach yields a provable optimal accuracy bound of O(ε^(-2)) and scales polynomially with problem parameters like agent number and time horizon. The authors’ analysis generalizes virtual policy iteration techniques from single-agent local planning, resulting in a computationally efficient algorithm with a tighter sample complexity bound when assuming random access to the simulator. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers solve a big problem in game theory by creating a new way to learn about equilibria in games. They’re trying to figure out what happens when there are many agents and many possible actions. The authors make an algorithm that can do this efficiently and accurately, using something called the “simulator”. This means they can test their ideas on a virtual copy of the game before playing it for real. |
