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

机译：使用深增强学习的移动自主多助手系统的形成控制

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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：移动自主多智能体系的形成控制是自动控制和机器人领域的重要任务。许多应用，从群体机器人到自动车辆，需要相应的代理（即机器人或汽车）来遵循设计的控制法为他们的团体行为。在形成控制中找到可行和碰撞的自由轨迹，对于这些多种代理系统来说，每个代理都是至关重要的并具有挑战性的。基于深度加固学习的应用，这项工作介绍了移动自主多功能多功能系统的形成控制算法。具体而言，所提出的方法可以引导代理商制定策略网络，不仅可以学习自己的政策，还可以了解其邻居的政策。因此，值网络可以评估它在培训过程后已经学习并找到了正确操作的策略。此外，所提出的方法消除了其他代理执行相同策略的假设，该策略广泛用于一些现有的碰撞避免算法。最后，实验结果表明了我们提出的方法的有效性。

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