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arXiv · 2211.04813

Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning

Jernej Hribar·Luke Hackett·Ivana Dusparic
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Abstract

In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective 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.

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Jernej Hribar, Luke Hackett, Ivana Dusparic. 2022-11-09. Deep W-Networks: Solving Multi-Objective Optimisation Problems With Deep Reinforcement Learning. https://doi.org/10.5220/0011610300003393

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