Efficient exploration through active learning for value function approximation in reinforcement learning.
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares policy iteration (LSPI) framework allows us to employ statistical active learning methods for linear regression. Then we propose a design method of good sampling policies for efficient exploration, which is particularly useful when the sampling cost of immediate rewards is high. The effectiveness of the proposed method, which we call active policy iteration (API), is demonstrated through simulations with a batting robot.
Akiyama
Takayuki
T
Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan. akiyama@sg.cs.titech.ac.jp
Hachiya
Hirotaka
H
Sugiyama
Masashi
M
D000465:Algorithms; D001185:Artificial Intelligence; D005106:Exploratory Behavior; D006801:Humans; D016018:Least-Squares Analysis; D016014:Linear Models; D009048:Motor Skills; D012054:Reinforcement, Psychology; D012201:Reward; D012371:Robotics