Summary of A Finite Time Analysis Of Distributed Q-learning, by Han-dong Lim et al.
A finite time analysis of distributed Q-learning
by Han-Dong Lim, Donghwan Lee
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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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 Multi-agent reinforcement learning has seen significant progress, driven by the success of single-agent approaches. This study focuses on a distributed Q-learning scenario where multiple agents collaborate to solve a sequential decision-making problem without access to a central reward function. We analyze the finite-time performance of a distributed Q-learning algorithm and provide a new sample complexity result. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a new method for multi-agent reinforcement learning. They look at how a group of agents can work together to make decisions when they don’t have all the information. This is different from single-agent learning, where one agent makes decisions based on its own rewards. The authors study an algorithm that helps agents learn and make good choices, even without knowing the overall goal. |
Keywords
» Artificial intelligence » Reinforcement learning
