Behavior Acquisition of Six-Legged Robot by Reinforcement Learning using Value Iteration

Abstract

In this research, model-based reinforcement learning was applied to the six-legged robot. The objective for this robot was to acquire efficient walk movement by model-based reinforcement learning. By setting several constraint conditions on motors of this robot, we considered the reinforcement learning problem with three-dimensional state and action space. Through experiments, the effectiveness of our method using value iteration was revealed in comparison with Q-learning.