An active exploration method for data efficient reinforcement learning - Zhao, Dongfang; Liu, Jiafeng; Wu, Rui; Cheng, Dansong; Tang, Xianglong (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: reinforcement learning; information entropy; PILCO; data efficiency