| 英文标题 | Robot path planning based on soft AC algorithm for multilayer attention mechanism |
| 作者英文名 | Han Jinliang, Ren Haijing, Wu Songwei, Jiang Xinxin, Liu Fengkai |
| 机构英文名 | a.School of Mathematics,b.School of Environment & Spatial Informatics,c.School of Safety Engineering,d.School of Information & Control Engineering,China University of Mining & Technology,Xuzhou Jiangsu 221116,China |
| 英文摘要 | Aiming at the high dimensionality of the empirical learning sample and the low robustness of the strategy gradient model in the actor-critic algorithm, this paper constructed the attention mechanism network and acted as a proxy based on the information cooperation advantages of the multi-agent systems, introducing a multi-layer parallel attention mechanism. By adding the network model and the soft function to the actor-critic algorithm, this paper proposed a soft actor-critic algorithm based on multi-layer parallel attention mechanism to solve the problem of robot path planning, enhance the actors' strategy gradient robustness and reduce regression error of the critics, and achieved the fast convergence of robot path planning. The experimental results show that this method can effectively overcome the local optimization problem of robot path planning, and has the advantages of fast computation speed and stable convergence. |
| 英文关键词 | actor-critic algorithm; attention mechanism; deep reinforcement learning; robot path planning |