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Authors: Francesco Belardinelli 1 ; 2 ; Borja G. León 2 and Vadim Malvone 3

Affiliations: 1 Département d’Informatique, Université d’Evry, Evry, France ; 2 Department of Computing, Imperial College London, London, U.K. ; 3 INFRES, Télécom Paris, Paris, France

Keyword(s): Markov Decision Processes, Partial Observability, Extended Partially Observable Decision Process, non-Markovian Rewards.

Abstract: Markovian systems are widely used in reinforcement learning (RL), when the successful completion of a task depends exclusively on the last interaction between an autonomous agent and its environment. Unfortunately, real-world instructions are typically complex and often better described as non-Markovian. In this paper we present an extension method that allows solving partially-observable non-Markovian reward decision processes (PONMRDPs) by solving equivalent Markovian models. This potentially facilitates Markovian-based state-of-the-art techniques, including RL, to find optimal behaviours for problems best described as PONMRDP. We provide formal optimality guarantees of our extension methods together with a counterexample illustrating that naive extensions from existing techniques in fully-observable environments cannot provide such guarantees.

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Paper citation in several formats:
Belardinelli, F.; G. León, B. and Malvone, V. (2022). Enabling Markovian Representations under Imperfect Information. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-547-0; ISSN 2184-433X, SciTePress, pages 450-457. DOI: 10.5220/0010882200003116

@conference{icaart22,
author={Francesco Belardinelli. and Borja {G. León}. and Vadim Malvone.},
title={Enabling Markovian Representations under Imperfect Information},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={450-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010882200003116},
isbn={978-989-758-547-0},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Enabling Markovian Representations under Imperfect Information
SN - 978-989-758-547-0
IS - 2184-433X
AU - Belardinelli, F.
AU - G. León, B.
AU - Malvone, V.
PY - 2022
SP - 450
EP - 457
DO - 10.5220/0010882200003116
PB - SciTePress