Acceleration of reinforcement learning by a mobile robot using generalized rules
Kazuya Inoué, Jun Ota, Takeshi Katayama, Tamio Arai · 2002
We propose an architecture to accelerate reinforcement learning by a mobile robot. In order to solve the problem of explosion of learning time in former reinforcement learning methods, we introduce a mechanism to acquire and utilize a set of widely applicable generalized rules into a reinforcement learning system. The mechanism extracts these rules by a statistical analysis of experienced data from the learning process. By applying these rules, the learning process can be accelerated by reducing search space. Simulation results indicate the effectiveness of the proposed method.