Path Planning Method for Mobile Robot Based on Curiosity Distillation Double Q-Network.

Aiming at the problem of overestimation, low sample utilization and sparse reward of DQN algorithm in mobile robot path planning, an end- to- end path planning method based on improved deep reinforcement learning is proposed, namely the curiosity distillation module dueling deep double Q-network prioritized experience replay (CDM-D3QN-PER). This method is based on D3QN to reduce the adverse effects of overestimation. Long short term memory (LSTM) is added to the input to process the information of radar and camera to obtain more favorable environmental information. It uses prioritized experience replay (PER) as sampling method to make full use of samples and improve sample utilization, and the curiosity distillation module (CDM) is introduced to alleviate the problem of reward sparsity to some extent. The experimental results show that compared with DQN, DDQN and D3QN, the number of robots reaching the target point trained by CDM-D3QN-PER algorithm is significantly increased, and it is three times that of DQN algorithm. The algorithm makes reward worthy of promotion, network convergence speed is improved, in unknown complex environment can better obtain the optimal path. [ABSTRACT FROM AUTHOR]