Summary of Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms, by Thanh Nguyen-tang et al.
Learning in Markov Games with Adaptive Adversaries: Policy Regret, Fundamental Barriers, and Efficient Algorithms
by Thanh Nguyen-Tang, Raman Arora
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 studies learning in a dynamic environment modeled as a Markov game between a learner and a strategic opponent that can adapt to the learner’s strategies. The authors focus on policy regret, which aims to compete with the return that would have been attained if the learner had followed the best fixed sequence of policy in hindsight. They show that sample-efficient learning is not possible when the opponent has unbounded memory or is non-stationary. For memory-bounded and stationary opponents, they demonstrate that learning is statistically hard if the set of feasible strategies for the learner is exponentially large. To guarantee learnability, they introduce a new notion of consistent adaptive adversaries, where the adversary responds similarly to similar strategies of the learner. The authors provide algorithms that achieve square root T policy regret against memory-bounded, stationary, and consistent adversaries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how machines can learn in changing environments where other players adapt their moves. Instead of focusing on how well a machine learns compared to others (external regret), this work looks at how well the machine would have done if it had used the best strategy from the start (policy regret). The researchers show that if the opponents can remember everything or change their strategies, machines can’t learn efficiently. However, if opponents only remember recent moves and don’t change their strategies often, machines can still learn but might not do very well if there are many possible actions. |
