Policy Gradient Fuzzy Reinforcement Learning Control of Humanoid Walking

AbstractThis paper presents a novel dynamic control approach to acquire biped walking of humanoid robots focussed on policy gradient reinforcement learning with fuzzy evaluative feedback. The proposed structure of controller involves two feedback loops: conventional computed torque controller including impact-force controller and reinforcement learning computed torque controller. Reinforcement learning part includes fuzzy information about Zero-Moment Point errors. To demonstrate the effectiveness of our method, we apply it in simulation to the learning of a biped walking.