Abstract：Many excellent classical algorithms have emerged in the field of path planning,but these traditional methods are often based on static environment and lack processing power for dynamic variable environment.This paper proposes a path planning algorithm for dynamic environment based on LSTM reinforcement learning.First of all,this paper takes the environment image as the input to ensure the original information source to the maximum extent.Then an Autoencoder is built to reduce the dimension of environment image,which reduces the complexity of the whole model.At last,the deep reinforcement learning algorithm DDPG is used for path planning,and the Actor part uses LSTM network,so that the Actor can refer to the prior information and make decisions with the prediction of environment change.Finally,the feasibility and efficiency of the proposed algorithm are proved by experiments.
2021, Vol. 42 
