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Authors: Jernej Hribar ; Luke Hackett and Ivana Dusparic

Affiliation: School of Computer Science and Statistics, Trinity College Dublin, Ireland

Keyword(s): Deep Reinforcement Learning, Deep Q-Networks, W-Learning, Deep W-Networks, Multi-Objective.

Abstract: In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multiobjective tabular Reinforcement Learning (RL) algorithm W-learning to large state spaces. W-learning algorithm can naturally solve the competition between multiple single policies in multi-objective environments. However, the tabular version does not scale well to environments with large state spaces. To address this issue, we replace underlying Q-tables with DQN, and propose an addition of W-Networks, as a replacement for tabular weights (W) representations. We evaluate the resulting Deep W-Networks (DWN) approach in two widely-accepted multi-objective RL benchmarks: deep sea treasure and multi-objective mountain car. We show that DWN solves the competition between multiple policies while outperforming the baseline in the form of a DQN solution. Additionally, we demonstrate that the proposed algorithm can find the Pareto front in both tested environments.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Hribar, J.; Hackett, L. and Dusparic, I. (2023). Deep W-Networks: Solving Multi-Objective Optimisation Problems with Deep Reinforcement Learning. 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 17-26. DOI: 10.5220/0011610300003393

@conference{icaart23,
author={Jernej Hribar. and Luke Hackett. and Ivana Dusparic.},
title={Deep W-Networks: Solving Multi-Objective Optimisation Problems with Deep Reinforcement Learning},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2023},
pages={17-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011610300003393},
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 - Deep W-Networks: Solving Multi-Objective Optimisation Problems with Deep Reinforcement Learning
SN - 978-989-758-623-1
IS - 2184-433X
AU - Hribar, J.
AU - Hackett, L.
AU - Dusparic, I.
PY - 2023
SP - 17
EP - 26
DO - 10.5220/0011610300003393
PB - SciTePress

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