多智能体编队控制中的迁移强化学习算法研究

Considering the obstacle avoidance and collision avoidance for multi-agent cooperative formation in multi-obstacle environment, a formation control algorithm based on transfer learning and reinforcement learning is proposed. Firstly, in the source task learning stage, the large storage space required by Q-table solution is avoided by using the value function approximation method, which effectively reduces the storage space requirement and im- proves the solving speed of the algorithm. Secondly, in the learning phase of the target task, Gaussian clustering al- gorithm was used to classify the source tasks. According to the distance between the clustering center and the target task, the optimal source task class was selected for target task learning, which effectively avoided the negative transfer phenomenon, and improved the generalization ability and convergence speed of reinforcement learning algo- rithm. Finally, the simulation results show that this method can effectively form and maintain formation configuration of multi-agent system in complex environment with obstacles, and realize obstacle avoidance and collision avoidance at the same time.