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Authors: Petr Kuderov 1 and Aleksandr I. Panov 2 ; 1

Affiliations: 1 Moscow Institute of Physics and Technology, Moscow, Russia ; 2 Artificial Intelligence Research Institute, Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia

Keyword(s): Model-based Reinforcement Learning, Markov Decision Process, Planning, Hierarchical Temporal Memory, Sparse Distributed Representation.

Abstract: Sequential decision making is among the key problems in Artificial Intelligence. It can be formalized as Markov Decision Process (MDP). One approach to solve it, called model-based Reinforcement Learning (RL), combines learning the model of the environment and the global policy. Having a good model of the environment opens up such properties as data efficiency and targeted exploration. While most of the memory-based approaches are based on using Artificial Neural Networks (ANNs), in our work we instead draw the ideas from Hierarchical Temporal Memory (HTM) framework, which is based on human-like memory model. We utilize it to build an agent’s memory that learns the environment dynamics. We also accompany it with an example of planning algorithm, that enables the agent to solve RL tasks.

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Paper citation in several formats:
Kuderov, P. and Panov, A. (2021). Planning with Hierarchical Temporal Memory for Deterministic Markov Decision Problem. In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-484-8; ISSN 2184-433X, SciTePress, pages 1073-1081. DOI: 10.5220/0010317710731081

@conference{icaart21,
author={Petr Kuderov. and Aleksandr I. Panov.},
title={Planning with Hierarchical Temporal Memory for Deterministic Markov Decision Problem},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2021},
pages={1073-1081},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010317710731081},
isbn={978-989-758-484-8},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Planning with Hierarchical Temporal Memory for Deterministic Markov Decision Problem
SN - 978-989-758-484-8
IS - 2184-433X
AU - Kuderov, P.
AU - Panov, A.
PY - 2021
SP - 1073
EP - 1081
DO - 10.5220/0010317710731081
PB - SciTePress

- Science and Technology Publications, Lda.
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