Summary of Refined Sample Complexity For Markov Games with Independent Linear Function Approximation, by Yan Dai et al.
Refined Sample Complexity for Markov Games with Independent Linear Function Approximation
by Yan Dai, Qiwen Cui, Simon S. Du
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Science and Game Theory (cs.GT); 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 paper proposes a novel approach to tackle the “curse of multi-agents” in Multi-Agent Reinforcement Learning (MARL) by refining the AVLPR framework. The authors design a data-dependent pessimistic estimation of the sub-optimality gap, allowing for a broader choice of plug-in algorithms. They also introduce action-dependent bonuses to mitigate occasionally extreme estimation errors. The proposed algorithm achieves an optimal O(T^(-1/2)) convergence rate and avoids polynomial dependency on the number of actions simultaneously. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps solve a problem in Multi-Agent Reinforcement Learning (MARL) called the “curse of multi-agents”. This means that as the number of agents increases, it becomes harder for the algorithm to learn. The authors propose a new way to improve this by using something called data-dependent pessimistic estimation. They also introduce bonuses to help with errors in their estimates. The result is an algorithm that works better and faster than previous ones. |
Keywords
* Artificial intelligence * Reinforcement learning
