CrowdGAIL: A spatiotemporal aware method for agent navigation

Document Type

Article

Publication Date

1-1-2023

Abstract

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 shortterm 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

Identifier

85147002160 (Scopus)

Publication Title

Electronic Research Archive

External Full Text Location

https://doi.org/10.3934/era.2023057

e-ISSN

26881594

First Page

1134

Last Page

1146

Issue

2

Volume

31

Grant

IIS-2153311

Fund Ref

National Science Foundation

This document is currently not available here.

Share

COinS