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Authors: Léo Saulières ; Martin Cooper and Florence Bannay

Affiliation: IRIT, University of Toulouse III, France

Keyword(s): Explainable Artificial Intelligence, Reinforcement Learning

Abstract: In the context of reinforcement learning (RL), in order to increase trust in or understand the failings of an agent’s policy, we propose predictive explanations in the form of three scenarios: best-case, worst-case and most-probable. After showing W[1]-hardness of finding such scenarios, we propose linear-time approximations. In particular, to find an approximate worst/best-case scenario, we use RL to obtain policies of the environment viewed as a hostile/favorable agent. Experiments validate the accuracy of this approach.

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Paper citation in several formats:
Saulières, L., Cooper, M. and Bannay, F. (2023). Reinforcement Learning Explained via Reinforcement Learning: Towards Explainable Policies through Predictive Explanation. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 35-44. DOI: 10.5220/0011619600003393

@conference{icaart23,
author={Léo Saulières and Martin Cooper and Florence Bannay},
title={Reinforcement Learning Explained via Reinforcement Learning: Towards Explainable Policies through Predictive Explanation},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2023},
pages={35-44},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011619600003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Reinforcement Learning Explained via Reinforcement Learning: Towards Explainable Policies through Predictive Explanation
SN - 978-989-758-623-1
IS - 2184-433X
AU - Saulières, L.
AU - Cooper, M.
AU - Bannay, F.
PY - 2023
SP - 35
EP - 44
DO - 10.5220/0011619600003393
PB - SciTePress

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