Summary of Finite-time Analysis Of Simultaneous Double Q-learning, by Hyunjun Na et al.
Finite-Time Analysis of Simultaneous Double Q-learning
by Hyunjun Na, Donghwan Lee
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
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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 paper proposes a modified version of double Q-learning, called simultaneous double Q-learning (SDQ), which addresses overestimation bias in reinforcement learning (RL) algorithms. The proposed method eliminates the need for random selection between two Q-estimators and allows for efficient finite-time analysis using a novel switching system framework. Empirical studies show that SDQ converges faster than traditional double Q-learning while mitigating maximization bias. A finite-time expected error bound is also derived for SDQ. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SDQ is an improved version of the well-known reinforcement learning algorithm, double Q-learning. This new method can learn much faster and make better decisions than before. It’s like a robot that gets smarter and more accurate over time. The researchers studied this method using special math tools and found that it works really well. |
Keywords
* Artificial intelligence * Reinforcement learning
