CrowdGAIL: A spatiotemporal aware method for agent navigation
CrowdGAIL: A spatiotemporal aware method for agent navigation
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DOI：
10.3934/era.2023057
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发表时间：
2022
影响因子：
0.8
通讯作者：
Longchao Da;Hua Wei
中科院分区：
文献类型：
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作者：
Longchao Da;Hua Wei
代理导航一直是当今服务和自动化工厂的一项关键任务。很多努力都是在某种场景下为智能体设定特定的规则来规范智能体的行为。然而，并非所有情况都可以提前考虑，这可能会导致实际应用程序中的糟糕性能。在本文中，我们提出了 CrowdGAIL，一种从专家行为中学习作为指导策略的方法，可以在导航问题上训练大多数“类人”代理，而无需手动设置任何奖励函数或预先规定。首先，所提出的模型结构基于生成对抗性模仿学习（GAIL），它最大限度地模仿人类如何采取行动并朝着目标移动，通过比较，我们证明了近端策略优化（PPO）相对于信任域策略优化的优势，因此，GAIL-PPO是我们的基础。其次，我们设计了一个与内部长短期记忆结构兼容的特殊 Sequential DemoBuffer，以将时空指令应用于智能体的下一步。第三，本文通过考虑人类避免碰撞和社交舒适距离，展示了该模型在多智能体场景中具有集成社交方式的潜力。最后，对 CrowdNav 生成的数据集进行的实验验证了我们的模型在轨迹方面与人类的行为有多接近，以及它如何通过避免任何碰撞来引导多智能体。在相同的评估指标下，CrowdGAIL 与经典的 Social-GAN 相比表现出了更好的结果。
Agent navigation has been a crucial task in today's service and automated factories. Many efforts are to set specific rules for agents in a certain scenario to regulate the agent's behaviors. However, not all situations could be in advance considered, which might lead to terrible performance in a real-world application. In this paper, we propose CrowdGAIL, a method to learn from expert behaviors as an instructing policy, can train most 'human-like' agents in navigation problems without manually setting any reward function or beforehand regulations. First, the proposed model structure is based on generative adversarial imitation learning (GAIL), which imitates how humans take actions and move toward the target to a maximum extent, and by comparison, we prove the advantage of proximal policy optimization (PPO) to trust region policy optimization, thus, GAIL-PPO is what we base. Second, we design a special Sequential DemoBuffer compatible with the inner long short-term memory structure to apply spatiotemporal instruction on the agent's next step. Third, the paper demonstrates the potential of the model with an integrated social manner in a multi-agent scenario by considering human collision avoidance as well as social comfort distance. At last, experiments on the generated dataset from CrowdNav verify how close our model would act like a human being in the trajectory aspect and also how it could guide the multi-agents by avoiding any collision. Under the same evaluation metrics, CrowdGAIL shows better results compared with classic Social-GAN.