提升基于强化学习的局部运动规划器对未见人群行为的泛化能力
Improving the Generalization of Unseen Crowd Behaviors for Reinforcement Learning based Local Motion Planners
AI总结：
本文提出一种通过最大化信息论目标来增强单一策略内智能体多样性的方法，以解决基于强化学习的运动规划器在不可预测行人场景中的过拟合和同质冲突问题，提升了智能体对未见人群行为的适应性和避碰能力。
摘要：
在有人类行人存在的场景中部署安全的移动机器人策略具有挑战性，因为行人的运动不可预测。当前的基于强化学习的运动规划器依赖单一策略来模拟行人运动，可能会遭遇过拟合问题。或者，将避碰问题构建为多智能体框架，其中智能体在学会到达目标的同时生成动态运动，但由于其同质性，可能会与人类行人产生冲突。为了解决这个问题，我们引入了一种有效的方法，通过最大化信息论目标来增强单一策略内智能体的多样性。这种多样性丰富了每个智能体的经验，提高了其对未见人群行为的适应性。在评估智能体对未见人群的鲁棒性时，我们提出了受行人人群行为启发的多样化场景。我们的行为条件策略在这些具有挑战性的场景中优于现有工作，在不增加额外时间或行驶距离的情况下减少了潜在碰撞。
英文摘要：
Deploying a safe mobile robot policy in scenarios with human pedestrians is challenging due to their unpredictable movements. Current Reinforcement Learning-based motion planners rely on a single policy to simulate pedestrian movements and could suffer from the over-fitting issue. Alternatively, framing the collision avoidance problem as a multi-agent framework, where agents generate dynamic movements while learning to reach their goals, can lead to conflicts with human pedestrians due to their homogeneity. To tackle this problem, we introduce an efficient method that enhances agent diversity within a single policy by maximizing an information-theoretic objective. This diversity enriches each agent's experiences, improving its adaptability to unseen crowd behaviors. In assessing an agent's robustness against unseen crowds, we propose diverse scenarios inspired by pedestrian crowd behaviors. Our behavior-conditioned policies outperform existing works in these challenging scenes, reducing potential collisions without additional time or travel.
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。
