A Multiobjective Collaborative Deep Reinforcement Learning Algorithm for Jumping Optimization of Bipedal Robot

Abstract

Due to the nonlinearity and underactuation of bipedal robots, developing efficient jumping strategies remains challenging. To address this, a multiobjective collaborative deep reinforcement learning algorithm based on the actor‐critic framework is presented. Initially, two deep deterministic policy gradient (DDPG) networks are established for training the jumping motion, each focusing on different objectives and collaboratively learning the optimal jumping policy. Following this, a recovery experience replay mechanism, predicated on dynamic time warping, is integrated into the DDPG to enhance sample utilization efficiency. Concurrently, a timely adjustment unit is incorporated, which works in tandem with the training frequency to improve the convergence accuracy of the algorithm. Additionally, a Markov decision process is designed to manage the complexity and parameter uncertainty in the dynamic model of the bipedal robot. Finally, the proposed method is validated on a PyBullet platform. The results show that the method outperforms baseline methods by improving learning speed and enabling robust jumps with greater height and distance.