Human-Inspired Multi-Agent Navigation using Knowledge Distillation
Human-Inspired Multi-Agent Navigation using Knowledge Distillation
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DOI：
10.1109/iros51168.2021.9636463
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
Pei Xu;Ioannis Karamouzas
中科院分区：
文献类型：
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作者：
Pei Xu;Ioannis Karamouzas
尽管在多智能体导航领域取得了重大进展，但智能体仍然缺乏人类在多智能体环境中表现出的复杂性和智能。在本文中，我们提出了一个框架，学习一个像人一般的碰撞避免政策，在完全分散的，多智能体环境中的代理-代理交互。我们的方法使用知识蒸馏与强化学习，通过行为克隆从人类轨迹演示中提取的专家策略来塑造奖励函数。我们表明，用我们的方法训练的代理可以在碰撞避免和目标导向的转向任务中采取类似人类的轨迹，而不是由演示提供，表现优于专家以及在没有知识蒸馏的情况下训练的基于学习的代理。
Despite significant advancements in the field of multi-agent navigation, agents still lack the sophistication and intelligence that humans exhibit in multi-agent settings. In this paper, we propose a framework for learning a human-like general collision avoidance policy for agent-agent interactions in fully decentralized, multi-agent environments. Our approach uses knowledge distillation with reinforcement learning to shape the reward function based on expert policies extracted from human trajectory demonstrations through behavior cloning. We show that agents trained with our approach can take human-like trajectories in collision avoidance and goal-directed steering tasks not provided by the demonstrations, outperforming the experts as well as learning-based agents trained without knowledge distillation.