计算机应用与软件2024，Vol.41Issue(5)：226-232,239,8.DOI:10.3969/j.issn.1000-386x.2024.05.035
基于改进分层DQN算法的智能体路径规划
PATH PLANNING FOR AGENT BASED ON IMPROVED LAYERED DQN ALGORITHM
摘要
Abstract
In order to solve the problems that the convergence speed is slow and it is difficult for Q value to describe the action accurately when an agent uses DQN algorithm in the process of path planning,a layered DQN algorithm optimized by the model structure of DQN is proposed.The excitation layer and the action layer built by the algorithm were superimposed to generate a more accurate Q value,which was used to select the optimal action and make the anti-interference ability of the whole network stronger.The simulation results show that the agent using layered DQN algorithm has a faster convergence speed,thus verifying the feasibility and effectiveness of the algorithm.关键词
分层DQN/神经网络/强化学习/路径规划Key words
Layered DQN/Neural network/Reinforcement learning/Path planning分类
信息技术与安全科学引用本文复制引用
杨尚志,张刚,陈跃华,何小龙..基于改进分层DQN算法的智能体路径规划[J].计算机应用与软件,2024,41(5):226-232,239,8.基金项目
国家自然科学基金项目(51675286) （51675286）
浙江省重点研发项目(2018C02G2070536). （2018C02G2070536）
