敵対環境下における協調機動の学習
Learning Coordinated Maneuver in Adversarial Environments
敵の位置がランダムな環境でロボットチームが経路を進む際、速度を落として「ガード」役になる行動と移動速度のトレードオフを強化学習で解き、チーム全体のコストを抑える協調行動を獲得した研究。
著者: Zechen Hu, Manshi Limbu, Daigo Shishika, Xuesu Xiao, Xuan Wang
分類: cs.RO
原文アブストラクト
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.
