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| Offline Reinforcement Learning Algorithm Based on Selection of High-Quality Samples |
| HOU Yonghong1, DING Wang1, REN Yi2, DONG Hongwei2, YANG Songling1 |
1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300072;
2. National Key Laboratory of Space Integrated Information System, Institute of Software, Chinese Academy of Sciences, Beijing 100190 |
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Abstract To address the issue of over-reliance on the quality of dataset samples of offline reinforcement learning algorithms, an offline reinforcement learning algorithm based on selection of high-quality samples(SHS) is proposed. In the policy evaluation stage, higher update weights are assigned to the samples with advantage values, and a policy entropy term is added to quickly identify high-quality action samples with high probability within the data distribution, thereby screening out more valuable action samples. In the policy optimization stage, SHS aims to maximize the normalized advantage function while maintaining the policy constraints on the actions within the dataset. Consequently, high-quality samples can be efficiently utilized when the sample quality of the dataset is low, thereby improving the learning efficiency and performance of the strategy. Experiments show that SHS performs well on D4RL offline dataset in the MuJoCo-Gym environment and successfully screens out more valuable samples, thus its effectiveness is verified.
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Received: 23 August 2024
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Corresponding Authors:
REN Yi, Ph.D., senior engineer. His research interests include reinforcement learning and intelligent game.
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| About author:: HOU Yonghong, Ph.D., professor. His research interests include computer vision, video and image processing, and digital communication.DING Wang, Master student. His research interests include offline reinforcement learning and artificial intelligence.DONG Hongwei, Ph.D., assistant professor. His research interests include machine learning and pattern recognition.YANG Songling, Master student. His research interests include reinforcement lear-ning and artificial intelligence. |
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2024, Vol. 37 
