In the study, the authors introduce Symmetric Q-learning to address skewed error distributions in online reinforcement learning. By adding noise to target values, the method aims to reduce skewness and improve sample efficiency. The proposed approach is evaluated on challenging tasks in MuJoCo, showcasing comparable or better performance than state-of-the-art methods. The study highlights the importance of addressing error distribution assumptions for effective RL algorithms.
The content discusses the challenges of skewed error distributions in reinforcement learning and introduces a method called Symmetric Q-learning to mitigate this issue. By adding noise to target values, the method aims to make the error distribution more symmetric and closer to a normal distribution. Experiments conducted on various tasks demonstrate improved sample efficiency and performance compared to existing methods.
The study delves into the implications of skewed error distributions in reinforcement learning and presents Symmetric Q-learning as a solution. By adjusting the error distribution through noise addition, the method enhances sample efficiency and overall performance. Results from experiments on benchmark tasks validate the effectiveness of correcting skewed error distributions for improved RL outcomes.
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by Motoki Omura... klokken arxiv.org 03-13-2024
https://arxiv.org/pdf/2403.07704.pdfDypere Spørsmål