Off-Policy Natural Policy Gradient Method for a Biped Walking Using a CPG Controller
Yutaka Nakamura, Takeshi Mori, Yoichi Tokita,
Tomohiro Shibata, and Shin Ishii
Theoretical Life Science Lab., Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
Referring to the mechanism of animals’ rhythmic movements, motor control schemes using a central pattern generator (CPG) controller have been studied. We previously proposed reinforcement learning (RL) called the CPG-actor-critic model, as an autonomous learning framework for a CPG controller. Here, we propose an off-policy natural policy gradient RL algorithm for the CPG-actor-critic model, to solve the “exploration-exploitation” problem by meta-controlling “behavior policy.” We apply this RL algorithm to an automatic control problem using a biped robot simulator. Computer simulation demonstrated that the CPG controller enables the biped robot to walk stably and efficiently based on our new algorithm.
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