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Authors: Menore Tekeba Mengistu 1 ; 2 ; Getachew Alemu 2 ; Pierre Chevaillier 1 and Pierre De Loor 1

Affiliations: 1 Lab-STICC, UMR CNRS 6285, ENIB, France ; 2 School of Electrical and Computer Engineering, Addis Ababa University, King George VI Street, Addis Ababa, Ethiopia

Keyword(s): Unsupervised Learning, Autonomous Agents, State Representation Learning, Contrastive Learning, Atari Games.

Abstract: In this paper, we present an unsupervised state representation learning of spatio-temporally evolving sequences of autonomous agents’ observations. Our method uses contrastive learning through mutual information (MI) maximization between a sample and the views derived through selection of pixels from the sample and other randomly selected negative samples. Our method employs balancing MI by finding the optimal ratios of positive-to-negative pixels in these derived (constructed) views. We performed several experiments and determined the optimal ratios of positive-to-negative signals to balance the MI between a given sample and the constructed views. The newly introduced method is named as Balanced View Spatial Deep InfoMax (BVS-DIM). We evaluated our method on Atari games and performed comparisons with the state-of-the-art unsupervised state representation learning baseline method. We show that our solution enables to successfully learn state representations from sparsely sampled or r andomly shuffled observations. Our BVS-DIM method also marginally enhances the representation powers of encoders to capture high-level latent factors of the agents’ observations when compared with the baseline method. (More)

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Paper citation in several formats:
Mengistu, M.; Alemu, G.; Chevaillier, P. and De Loor, P. (2022). Unsupervised Learning of State Representation using Balanced View Spatial Deep InfoMax: Evaluation on Atari Games. 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 110-119. DOI: 10.5220/0010785000003116

@conference{icaart22,
author={Menore Tekeba Mengistu. and Getachew Alemu. and Pierre Chevaillier. and Pierre {De Loor}.},
title={Unsupervised Learning of State Representation using Balanced View Spatial Deep InfoMax: Evaluation on Atari Games},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2022},
pages={110-119},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010785000003116},
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 - Unsupervised Learning of State Representation using Balanced View Spatial Deep InfoMax: Evaluation on Atari Games
SN - 978-989-758-547-0
IS - 2184-433X
AU - Mengistu, M.
AU - Alemu, G.
AU - Chevaillier, P.
AU - De Loor, P.
PY - 2022
SP - 110
EP - 119
DO - 10.5220/0010785000003116
PB - SciTePress

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