کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4946769 | 1439419 | 2016 | 39 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Model-based reinforcement learning with dimension reduction
ترجمه فارسی عنوان
یادگیری تقویت مبتنی بر مدل با کاهش ابعاد
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کلمات کلیدی
یادگیری تقویت مبتنی بر مدل، برآورد مدل گذار، کاهش اندازه مناسب،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
The goal of reinforcement learning is to learn an optimal policy which controls an agent to acquire the maximum cumulative reward. The model-based reinforcement learning approach learns a transition model of the environment from data, and then derives the optimal policy using the transition model. However, learning an accurate transition model in high-dimensional environments requires a large amount of data which is difficult to obtain. To overcome this difficulty, in this paper, we propose to combine model-based reinforcement learning with the recently developed least-squares conditional entropy (LSCE) method, which simultaneously performs transition model estimation and dimension reduction. We also further extend the proposed method to imitation learning scenarios. The experimental results show that policy search combined with LSCE performs well for high-dimensional control tasks including real humanoid robot control.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 84, December 2016, Pages 1-16
Journal: Neural Networks - Volume 84, December 2016, Pages 1-16
نویسندگان
Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama,