Multi-agent cooperative strategy learning method based on transfer Learning
Beinuo Huang, Xin Chen, Yipu Sun, Wenpeng He · 2022 13th Asian Control Conference (ASCC) · 2022
Path planning is of great significance for industrial production and daily life, where various intelligent methods have been applied to it. Reinforcement learning (RL), as a popular area of machine learning, can effectively find the optimal path by interacting with the environment. Q-learning is one of the most popular algorithms among the reinforcement learning methods. Although Q-learning is simple and effective, its training time will rise sharply when the environment to be explored becomes complex. To solve this problem, this paper proposes a multi-agent cooperative strategy learning method by combining transfer learning (TL) and traditional Q-learning. The proposed method is applied to the maze game and the experiments show that the proposed method outperforms the existing Q-learning methods in both iterations and performance.