Formation and Obstacle Avoidance Control Based on Multi-Agent Reinforcement Learning

For the multi-agent formation control problem, the current mainstream control methods are based on graph theory and consistency theory, which often require precise modeling of the system and rigorous mathematical reasoning. Based on the MADDPG deep reinforcement learning algorithm, this paper models the formation control problem as a reinforcement learning problem by considering various constraints on the agent, and obtains the behavior strategy of each agent through learning and training. Furthermore, this paper combines the leader-follow method to divide all agents into two categories, and solves the problem that it is difficult for agents to balance the long-term rewards of formation and obstacle avoidance tasks in reinforcement learning methods. The simulation experiments are divided into two types: circular formation control and formation obstacle avoidance. The dynamic movement process of the agent in the corresponding experimental scene is shown, and the effectiveness of the algorithm is verified by combining the formation process error.