Research review of robot path planning based on DQN
With the continuous development of deep reinforcement learning,deep Q-learning network(DQN)has received extensive attention and research in robot path planning.Firstly,the basic principles and improvement ideas of DQN and algorithms such as Nature DQN,Double DQN,Dueling DQN and D3QN is briefly introduced.In view of the problems of high sample acquisition cost and low interaction efficiency in the algorithm,the research results and ideas of optimization from reward function,exploration ability,sample utilization rate,etc are systematically sorted and summarized.Finally,the advantages of DQN in robot path planning in modern logistics is discussed,and optimization directions for each scenario is proposed covering key aspects such as state space,action space,and reward function.
万方数据
维普
