Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning

Authors: Arpit Garg, Yazied A. Hasan, Adam Yañez, Lydia Tapia
Published: 2019-10-09

Source: https://arxiv.org/abs/1910.04537

PDF: https://arxiv.org/pdf/1910.04537

AIにより推定されたラベル

強化学習環境 リスク評価 実験的検証

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Abstract

Coordinated defensive escorts can aid a navigating payload by positioning themselves in order to maintain the safety of the payload from obstacles. In this paper, we present a novel, end-to-end solution for coordinating an escort team for protecting high-value payloads. Our solution employs deep reinforcement learning (RL) in order to train a team of escorts to maintain payload safety while navigating alongside the payload. This is done in a distributed fashion, relying only on limited range positional information of other escorts, the payload, and the obstacles. When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31 by up to 75 learned solution is general to several adaptations in the scenario including: a changing number of escorts in the team, changing obstacle density, and changes in payload conformation. Video: https://youtu.be/SoYesKti4VA.