Biped walking on rough terfrain using reinforcement learning

摘要

In this paper, we propose a novel reinforcement learning method to stabilize biped walking on rough terrain. For the state space and the action space of the biped walking problem is continuous, the neural network is used in our method, which is based on actor-critic learning, to approximate the policy function of actor and the value function of critic. The neural network learns on-line through the process. The proposed method is examined in simulation. The simulation results show that the robot can learn to improve the stability of walking on rough terrain by using the proposed method.