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Authors: Nahum Alvarez and Itsuki Noda

Affiliation: National Institute of Advanced Industrial Science and Technology and Japan

Keyword(s): Inverse Reinforcement Learning, Behavioral Agents, Pedestrian Simulation.

Related Ontology Subjects/Areas/Topics: Agent Models and Architectures ; Agents ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Computational Intelligence ; Enterprise Information Systems ; Evolutionary Computing ; Information Systems Analysis and Specification ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Technologies ; Operational Research ; Simulation ; Soft Computing ; Symbolic Systems

Abstract: Machine learning is a discipline with many simulator-driven applications oriented to learn behavior. However, behavior simulation it comes with a number of associated difficulties, like the lack of a clear reward function, actions that depend of the state of the actor and the alternation of different policies. We present a method for behavior learning called Contextual Action Multiple Policy Inverse Reinforcement Learning (CAMP-IRL) that tackles those factors. Our method allows to extract multiple reward functions and generates different behavior profiles from them. We applied our method to a large scale crowd simulator using intelligent agents to imitate pedestrian behavior, making the virtual pedestrians able to switch between behaviors depending of the goal they have and navigating efficiently across unknown environments.

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Paper citation in several formats:
Alvarez, N. and Noda, I. (2019). Contextual Action with Multiple Policies Inverse Reinforcement Learning for Behavior Simulation. In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-350-6; ISSN 2184-433X, SciTePress, pages 887-894. DOI: 10.5220/0007684908870894

@conference{icaart19,
author={Nahum Alvarez. and Itsuki Noda.},
title={Contextual Action with Multiple Policies Inverse Reinforcement Learning for Behavior Simulation},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2019},
pages={887-894},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007684908870894},
isbn={978-989-758-350-6},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Contextual Action with Multiple Policies Inverse Reinforcement Learning for Behavior Simulation
SN - 978-989-758-350-6
IS - 2184-433X
AU - Alvarez, N.
AU - Noda, I.
PY - 2019
SP - 887
EP - 894
DO - 10.5220/0007684908870894
PB - SciTePress

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