Summary of Near-optimal Reinforcement Learning with Self-play Under Adaptivity Constraints, by Dan Qiao et al.
Near-Optimal Reinforcement Learning with Self-Play under Adaptivity Constraints
by Dan Qiao, Yu-Xiang Wang
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); 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 researchers investigate multi-agent reinforcement learning (MARL) under adaptivity constraints, a problem motivated by real-world applications where policy deployments are costly and updates must be minimized. For two-player zero-sum Markov Games, they propose an algorithm based on policy elimination, achieving regret of () with batch complexity O(H+K). The algorithm’s performance is matched by a lower bound proof for all algorithms with () regret, which naturally extends to learning bandit games and reward-free MARL. These results are the first steps towards understanding MARL under low adaptivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MARL is a type of machine learning that allows multiple agents to learn from each other. In this research, scientists focused on making sure these agents don’t waste time by constantly updating their rules. They created an algorithm that helps agents make decisions quickly and efficiently while still getting good results. The goal was to reduce the number of times agents have to change what they do. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
