对抗环境中的协同机动学习
Learning Coordinated Maneuver in Adversarial Environments
AI总结：
针对对抗环境中机器人团队的路径协调问题，本文提出结合理论分析与强化学习的方法，通过守卫行为与速度权衡最小化团队总成本，仿真验证了其有效性。
摘要：
本文旨在解决在存在随机位置对手的情况下，一支机器人团队沿路线行进时的协调问题。我们的目标是使团队的总成本最小化，该成本由（i）机器人停留在受对手影响区域时累积的风险以及（ii）任务完成时间决定。在行进过程中，机器人可以降低速度并充当“守卫”（速度越慢越好），这将降低某些对手造成的风险。这导致了机器人的守卫行为与其行进速度之间的权衡。所提出的问题高度非凸，现有算法无法高效求解。我们的方法包括对单对手情况下机器人行为的理论分析。随着问题规模的扩大，使用优化方法求解最优解变得具有挑战性，因此，我们采用强化学习技术，开发了新的编码和策略生成方法。仿真表明，我们的学习方法能够高效地产生团队协调行为。我们讨论了这些行为背后的推理，并解释了它们为何能降低团队总成本。
英文摘要：
This paper aims to solve the coordination of a team of robots traversing a route in the presence of adversaries with random positions. Our goal is to minimize the overall cost of the team, which is determined by (i) the accumulated risk when robots stay in adversary-impacted zones and (ii) the mission completion time. During traversal, robots can reduce their speed and act as a `guard' (the slower, the better), which will decrease the risks certain adversary incurs. This leads to a trade-off between the robots' guarding behaviors and their travel speeds. The formulated problem is highly non-convex and cannot be efficiently solved by existing algorithms. Our approach includes a theoretical analysis of the robots' behaviors for the single-adversary case. As the scale of the problem expands, solving the optimal solution using optimization approaches is challenging, therefore, we employ reinforcement learning techniques by developing new encoding and policy-generating methods. Simulations demonstrate that our learning methods can efficiently produce team coordination behaviors. We discuss the reasoning behind these behaviors and explain why they reduce the overall team cost.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
