[1N3] 機械学習-強化学習

[1N3-05] Inverse Reinforcement Learning with BDI Agents for Pedestrian Behavior Simulation

〇Nahum Alvarez¹, Itsuki Noda¹ (1. The National Institute of Advanced Industrial Science and Technology (AIST))

キーワード：inverse reinforcement learning, multi-agent system, pedestrian simulator

Crowd behavior has been subject of study in fields like disaster evacuation, smart town planning and business strategic placing. It is possible to create a model for those scenarios using machine learning techniques and a relatively small training data set to identify behavioral. We implemented a BDI-based agent model that uses such techniques into a large-scale crowd simulator, and apply inverse reinforcement learning to adjust agents' behaviors by examples. The goal of the system is to provide to the agents a realistic behavior model and a method to orient themselves without knowing the scenario's layout, based in learnt patterns around environment features.