عنوان انگلیسی مقاله:
Correlation minimizing replay memory in temporal-difference reinforcement learning
ترجمه فارسی عنوان مقاله:
حداقل سازی همبستگی پاسخ در یادگیری تقویتی متفاوت موقت
Sciencedirect - Elsevier - Neurocomputing, 393 (2020) 91-100. doi:10.1016/j.neucom.2020.02.004
Mirza Ramicic a , ∗, Andrea Bonarini b
Online reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approx- imation, thus the learning process itself, it has been proposed to select randomly a mini-batch of the past experiences that are stored in the replay memory buffer to be replayed at each learning step. In this work, we present an algorithm that classifies and samples the experiences into separate contextual memory buffers using an unsupervised learning technique. This allows each new experience to be as- sociated to a mini-batch of the past experiences that are not from the same contextual buffer as the current one, thus further reducing the correlation between experiences. Experimental results show that the correlation minimizing sampling improves over Q-learning algorithms with uniform sampling, and that a significant improvement can be observed when coupled with the sampling methods that prioritize on the experience temporal difference error.
Keywords: Reinforcement learning | Temporal-difference learning | Replay memory | Artificial neural networks