Robot Dynamic Path Planning Based on Prioritized Experience Replay and LSTM Network

To address the issues of slow convergence speed, poor dynamic adaptability, and path redundancy in the Double Deep Q Network (DDQN) within complex obstacle environments, this paper proposes an enhanced algorithm within the deep reinforcement learning framework. This algorithm, termed LPDDQN, integrates Prioritized Experience Replay (PER) and the Long Short Term Memory (LSTM) network to improve upon the DDQN algorithm. First, Prioritized Experience Replay (PER) is utilized to prioritize experience data and optimize storage and sampling operations through the SumTree structure, rather than the conventional experience queue. Second, the LSTM network is introduced to enhance the dynamic adaptability of the DDQN algorithm. Owing to the introduction of the LSTM model, the experience samples must be sliced and populated. The performance of the proposed LPDDQN algorithm is compared with five other path planning algorithms in both static and dynamic environments. Simulation analysis shows that in a static environment, LPDDQN demonstrates significant improvements over traditional DDQN in terms of convergence, number of moving steps, success rate, and number of turns, with respective improvements of 24.07%, 17.49%, 37.73%, and 61.54%. In dynamic and complex environments, the success rates of all algorithms, except TLD3 and the LPDDQN, decreased significantly. Further analysis reveals that the LPDDQN outperforms the TLD3 by 18.87%, 2.41%, and 39.02% in terms of moving steps, success rate, and number of turns, respectively.