Summary of Finite-time Error Analysis Of Online Model-based Q-learning with a Relaxed Sampling Model, by Han-dong Lim et al.
Finite-Time Error Analysis of Online Model-Based Q-Learning with a Relaxed Sampling Model
by Han-Dong Lim, HyeAnn Lee, Donghwan Lee
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research paper explores the integration of Q-learning, a powerful reinforcement learning algorithm, with a model-based approach. Specifically, the authors investigate the sample complexity of Q-learning when combined with a model-based framework. Through theoretical and empirical analyses, the study aims to identify the conditions under which this hybrid approach outperforms its model-free counterpart in terms of sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make Q-learning, a type of reinforcement learning, better by combining it with a model-based approach. The researchers want to know when using both methods together can be more efficient than just using one method alone. They’re trying to figure out the rules for when this hybrid approach is better. |
Keywords
* Artificial intelligence * Reinforcement learning
