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

机译：基于深度强化学习的移动自主多智能体系统的编队控制

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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.
机译：移动自主多主体系统的编队控制已成为自动控制和机器人技术领域的重要任务。从群体机器人到自动驾驶汽车的许多应用都需要相应的代理程序（即，机器人或汽车）来遵循其群体行为的设计控制定律。对于这些多智能体系统，在地层控制中为每种智能体找到可行且无碰撞的轨迹至关重要且具有挑战性。这项工作基于深度强化学习的应用，提出了一种用于移动自主多主体系统的编队控制算法。具体地，所提出的方法可以引导代理开发策略网络以不仅学习其自身的策略而且学习其邻居的策略。因此，价值网络可以评估其已学习的策略，并在培训过程后找到正确的措施。同样，该方法消除了其他代理执行相同策略的假设，该假设已在某些现有的冲突避免算法中广泛使用。最后，实验结果表明了该方法的有效性。

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