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Authors: Alexander Hill ; Marc Groefsema ; Matthia Sabatelli ; Raffaella Carloni and Marco Grzegorczyk

Affiliation: Faculty of Science and Engineering, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, The Netherlands

Keyword(s): Machine Learning.

Abstract: This paper proposes a novel method of utilising guide policies in Reinforcement Learning problems; Contextual Online Imitation Learning (COIL). This paper demonstrates that COIL can offer improved performance over both offline Imitation Learning methods such as Behavioral Cloning, and also Reinforcement Learning algorithms such as Proximal Policy Optimisation which do not take advantage of existing guide policies. An important characteristic of COIL is that it can effectively utilise guide policies that exhibit expert behavior in only a strict subset of the state space, making it more flexible than classical methods of Imitation Learning. This paper demonstrates that through using COIL, guide policies that achieve good performance in sub-tasks can also be used to help Reinforcement Learning agents looking to solve more complex tasks. This is a significant improvement in flexibility over traditional Imitation Learning methods. After introducing the theory and motivation behind COIL, t his paper tests the effectiveness of COIL on the task of mobile-robot navigation in both a simulation and real-life lab experiments. In both settings, COIL gives stronger results than offline Imitation Learning, Reinforcement Learning, and also the guide policy itself. (More)

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Paper citation in several formats:
Hill, A.; Groefsema, M.; Sabatelli, M.; Carloni, R. and Grzegorczyk, M. (2024). Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 178-185. DOI: 10.5220/0012312700003636

@conference{icaart24,
author={Alexander Hill. and Marc Groefsema. and Matthia Sabatelli. and Raffaella Carloni. and Marco Grzegorczyk.},
title={Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={178-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012312700003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Contextual Online Imitation Learning (COIL): Using Guide Policies in Reinforcement Learning
SN - 978-989-758-680-4
IS - 2184-433X
AU - Hill, A.
AU - Groefsema, M.
AU - Sabatelli, M.
AU - Carloni, R.
AU - Grzegorczyk, M.
PY - 2024
SP - 178
EP - 185
DO - 10.5220/0012312700003636
PB - SciTePress

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