The Formation Control of Mobile Autonomous Multi-Agent Systems Using Deep Reinforcement Learning

摘要

The formation control of mobile autonomous multi-agent systems has been an important task in the fields of automatic control and robotics. Many applications, from swarm robots to autonomous cars, need the corresponding agents (i.e., robots or cars) to follow the designed control law for their group behaviors. Finding a feasible and collision free trajectory for each agent in the formation control is critical and challenging for these multi-agent systems. This work presents a formation control algorithm for mobile autonomous multi-agent systems based on the application of deep reinforcement learning. Specifically, the proposed method can lead the agent to develop a policy network to learn not only its own policy but also the policy of its neighbors. Thus, the value network can evaluate the policy that it has learned and find the correct actions after the training process. Also, the proposed method removes the assumption that other agents perform the same policy, which is widely used in some existing collision avoidance algorithms. Finally, the experimental results show the effectiveness of our proposed method.