A Dueling Twin Delayed DDPG Architecture for mobile robot navigation
Haoge Jiang, Kong-Wah Wan, Han Wang, Xudong Jiang · 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV) · 2022
Collision-free path planning is challenging for mobile robot navigation tasks. Recently, the deep reinforcement learning method provided a more effective way to derive safe and efficient velocity commands directly from raw sensor information. Twin Delayed Deep Deterministic policy gradient (TD3) is an efficient approach for DRL navigation. However, original TD3 exists issues such as inefficiency learning and slow convergence speed which may influence the model to derive an ideal action for mobile robot navigation. To address these issues, we proposed a novel dueling architecture model, dueling deep deterministic policy gradient (Dueling-TD3), we compose the dueling network architecture into the critic network to increase the Q-value estimate precision. The results demonstrate that our proposed model outperforms the original model in terms of route planning capabilities.