دورية أكاديمية
An Active Exploration Method for Data Efficient Reinforcement Learning
| العنوان: | An Active Exploration Method for Data Efficient Reinforcement Learning |
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| المؤلفون: | Zhao Dongfang, Liu Jiafeng, Wu Rui, Cheng Dansong, Tang Xianglong |
| المصدر: | International Journal of Applied Mathematics and Computer Science, Vol 29, Iss 2, Pp 351-362 (2019) |
| بيانات النشر: | Sciendo, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Mathematics LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | reinforcement learning, information entropy, pilco, data efficiency, Mathematics, QA1-939, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Reinforcement learning (RL) constitutes an effective method of controlling dynamic systems without prior knowledge. One of the most important and difficult problems in RL is the improvement of data efficiency. Probabilistic inference for learning control (PILCO) is a state-of-the-art data-efficient framework that uses a Gaussian process to model dynamic systems. However, it only focuses on optimizing cumulative rewards and does not consider the accuracy of a dynamic model, which is an important factor for controller learning. To further improve the data efficiency of PILCO, we propose its active exploration version (AEPILCO) that utilizes information entropy to describe samples. In the policy evaluation stage, we incorporate an information entropy criterion into long-term sample prediction. Through the informative policy evaluation function, our algorithm obtains informative policy parameters in the policy improvement stage. Using the policy parameters in the actual execution produces an informative sample set; this is helpful in learning an accurate dynamic model. Thus, the AEPILCO algorithm improves data efficiency by learning an accurate dynamic model by actively selecting informative samples based on the information entropy criterion. We demonstrate the validity and efficiency of the proposed algorithm for several challenging controller problems involving a cart pole, a pendubot, a double pendulum, and a cart double pendulum. The AEPILCO algorithm can learn a controller using fewer trials compared to PILCO. This is verified through theoretical analysis and experimental results. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2083-8492 2019-0026 |
| Relation: | https://doaj.org/toc/2083-8492 |
| DOI: | 10.2478/amcs-2019-0026 |
| URL الوصول: | https://doaj.org/article/f6dc2b7911544c01b01453a6d6a5e207 |
| رقم الأكسشن: | edsdoj.f6dc2b7911544c01b01453a6d6a5e207 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20838492 20190026 |
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| DOI: | 10.2478/amcs-2019-0026 |