Summary of Finite-time Error Analysis Of Soft Q-learning: Switching System Approach, by Narim Jeong and Donghwan Lee
Finite-Time Error Analysis of Soft Q-Learning: Switching System Approach
by Narim Jeong, Donghwan Lee
First submitted to arxiv on: 11 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 Soft Q-learning, a variation of Q-learning, is designed to solve entropy regularized Markov decision problems. This paper provides a unified, finite-time control-theoretic analysis of two soft Q-learning algorithms: log-sum-exp and Boltzmann operator-based methods. The authors utilize dynamical switching system models to derive novel finite-time error bounds for both algorithms, shedding light on the connections between soft Q-learning and switching systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Soft Q-learning is a way to solve problems where an agent tries to make good choices by maximizing a special kind of value function. This paper helps us understand how this works better by looking at two types of soft Q-learning methods and how they behave over time. The authors use new models to show that these methods are accurate and efficient. |
