Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch ...Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.展开更多
基金the National Natural Science Foundation of China(No.62103009)。
文摘Bionic gait learning of quadruped robots based on reinforcement learning has become a hot research topic.The proximal policy optimization(PPO)algorithm has a low probability of learning a successful gait from scratch due to problems such as reward sparsity.To solve the problem,we propose a experience evolution proximal policy optimization(EEPPO)algorithm which integrates PPO with priori knowledge highlighting by evolutionary strategy.We use the successful trained samples as priori knowledge to guide the learning direction in order to increase the success probability of the learning algorithm.To verify the effectiveness of the proposed EEPPO algorithm,we have conducted simulation experiments of the quadruped robot gait learning task on Pybullet.Experimental results show that the central pattern generator based radial basis function(CPG-RBF)network and the policy network are simultaneously updated to achieve the quadruped robot’s bionic diagonal trot gait learning task using key information such as the robot’s speed,posture and joints information.Experimental comparison results with the traditional soft actor-critic(SAC)algorithm validate the superiority of the proposed EEPPO algorithm,which can learn a more stable diagonal trot gait in flat terrain.
