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PhD Defense

Scenario Generalization and its Estimation in Data-driven Decentralized Crowd Modeling


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Wednesday, December 08, 2021, 10:00am - 12:00pm


Speaker: Gang Qiao

Location : Via Zoom


Prof. Petros Faloutsos (York University)

Prof. Yongfeng Zhang

Prof. Mubbasir Kapadia

Prof. Vladimir Pavlovic (chair)

Event Type: PhD Defense

Abstract: In the context of crowd modeling, we propose the notion of scenario generalization, which is amacroscopic view of the performance of a decentralized crowd model. Based on this notion, firstly, weaim to answer the question that how a training paradigm and a training domain (source) affect thescenario generalization of an imitation learning model when applied to a different test domain (target).We evaluate the exact scenario generalizations of models built on combinations of imitation learningparadigms and source domains. Our empirical results suggest that (i) Behavior Cloning (BC) is better thanGenerative Adversarial Imitation Learning (GAIL), (ii) training samples in source domain with diverseagent-agent and agent-obstacle interactions are beneficial for reducing collisions when generalized tonew scenarios.Secondly, we note that although the exact evaluation of scenario generalization is accurate, it requirestraining and evaluation on large datasets, coupled with complex model selection and parameter tuning.To circumvent this challenge by estimating the scenario generalization without training, we proposed aninformation-theoretic inspired approach to characterize both the source and the target domains. Itestimates the Interaction Score (IS) that captures the task-level inter-agent interaction difficulty of targetscenario domain. When augmented with Diversity Quantification (DQ) on the source, the combined ISDQscore offers a means to estimating the source to target generalization of potential models. Variousexperiments verify the efficacy of ISDQ in estimating the scenario generalization, compared with the exactscenario generalizations of models trained with imitation leaning paradigms (BC, GAIL) and reinforcementlearning paradigm (proximal policy optimization, PPO). Thus, it would enable rapid selection of the bestsource-target domain pair among multiple possible choices prior to training and testing of the actualcrowd model.


Rutgers University School of Arts and Sciences

Contact   Prof. Vladimir Pavlovic (chair)

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