Natural policy gradient reinforcement learning for a CPG control of a biped robot

Motivated by the perspective that animals' rhythmic movements such as locomotion are controlled by neural circuits called central pattern generators (CPGs), motor control mechanisms by CPG have been studied. As an autonomous learning framework for a CPG controller, we previously proposed a reinforcement learning (RL) method called the CPG-actor-critic method. In this article, we propose a natural policy gradient learning algorithm for the CPG-actor-critic method, and applied our RL to an automatic control problem by a biped robot simulator. Computer simulations show that our RL makes the biped robot walk stably on various terrain.