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Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning

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Artificial Neural Networks and Machine Learning – ICANN 2023 (ICANN 2023)

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Abstract

Sequential decision making in the real world often requires finding a good balance of conflicting objectives. In general, there exist a plethora of Pareto-optimal policies that embody different patterns of compromises between objectives, and it is technically challenging to obtain them exhaustively using deep neural networks. In this work, we propose a novel multi-objective reinforcement learning (MORL) algorithm that trains a single neural network via policy gradient to approximately obtain the entire Pareto set in a single run of training, without relying on linear scalarization of objectives. The proposed method works in both continuous and discrete action spaces with no design change of the policy network. Numerical experiments demonstrate the practicality and efficacy of our approach in comparison to standard MORL baselines.

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Author information

Authors and Affiliations

  1. Research and Development Group, Hitachi, Ltd., Kokubunji, Tokyo, 185-8601, Japan

    Takuya Kanazawa

  2. Industrial AI Lab, Hitachi America, Ltd. R &D, Santa Clara, CA, 95054, USA

    Chetan Gupta

Authors
  1. Takuya Kanazawa
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  2. Chetan Gupta
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Corresponding author

Correspondence to Takuya Kanazawa .

Editor information

Editors and Affiliations

  1. Democritus University of Thrace, Xanthi, Greece

    Lazaros Iliadis

  2. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  3. Lancaster University, Lancaster, UK

    Plamen Angelov

  4. Teesside University, Middlesbrough, UK

    Chrisina Jayne

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Kanazawa, T., Gupta, C. (2023). Latent-Conditioned Policy Gradient for Multi-Objective Deep Reinforcement Learning. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14259. Springer, Cham. https://doi.org/10.1007/978-3-031-44223-0_6

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  • DOI: https://doi.org/10.1007/978-3-031-44223-0_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-44222-3

  • Online ISBN: 978-3-031-44223-0

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