论文标题
通过潜在的实地增强增强学习的多机器人合作追求
Multi-robot Cooperative Pursuit via Potential Field-Enhanced Reinforcement Learning
论文作者
论文摘要
虽然有希望，但要协调集体机器人以分散的方式纯粹鉴于当地的观察，以分散的方式狩猎逃避者。在本文中，这一挑战是通过一种新型的混合合作追求算法来解决的，该算法将强化学习与人造潜在的现场方法相结合。在拟议的算法中，采用了分散的深度强化学习来学习适应动态环境的合作追求政策。人工电位方法被整合到学习过程中，作为预定义的规则，以提高数据效率和泛化能力。数值模拟显示的是，所提出的混合设计优于从香草增强学习中学到的追求策略，或者是通过潜在的现场方法设计的。此外，通过将学习的追求政策转移到现实世界移动机器人中来进行实验。实验结果证明了拟议算法在学习多种合作追求策略方面的可行性和潜力。
It is of great challenge, though promising, to coordinate collective robots for hunting an evader in a decentralized manner purely in light of local observations. In this paper, this challenge is addressed by a novel hybrid cooperative pursuit algorithm that combines reinforcement learning with the artificial potential field method. In the proposed algorithm, decentralized deep reinforcement learning is employed to learn cooperative pursuit policies that are adaptive to dynamic environments. The artificial potential field method is integrated into the learning process as predefined rules to improve the data efficiency and generalization ability. It is shown by numerical simulations that the proposed hybrid design outperforms the pursuit policies either learned from vanilla reinforcement learning or designed by the potential field method. Furthermore, experiments are conducted by transferring the learned pursuit policies into real-world mobile robots. Experimental results demonstrate the feasibility and potential of the proposed algorithm in learning multiple cooperative pursuit strategies.
