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Life sciences · Preprint
arXiv · August 7, 2026
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This preprint proposes integrating three neural network design improvements—observation and feature normalization, weight normalization, and distributional return modeling—into an entropy-regularized multi-objective reinforcement learning algorithm. The authors report that these architectural changes improve solution set quality on standard continuous control benchmarks, but the work is unpublished, lacks independent validation, and does not quantify effect sizes or statistical significance.
Computational methods development and empirical benchmarking study. Intervention: Integration of observation and feature normalization, weight normalization, and distributional return modeling into entropy-regularized multi-objective reinforcement learning algorithm.
Integration of observation and feature normalization, weight normalization, and distributional returns modeling into MORL improves quality of produced solution sets Improvements demonstrated across standard continuous control benchmarks Changes require no major modification to underlying algorithm
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A preprint reporting architectural improvements to a multi-objective reinforcement learning algorithm in simulation benchmarks, without peer review, clinical validation, or independent replication.
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Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample efficiency and asymptotic performance without altering the underlying algorithms. In contrast, work on multi-objective reinforcement learning (MORL), which aims to discover a set of policies that balance trade-offs among conflicting objectives, has predominantly focused on algorithmic innovations, leaving the area of architectures underexplored. While the optimal policies and value functions can differ significantly depending on the trade-offs, MORL algorithms commonly represent them with simple feedforward networks conditioned on the trade-off. This raises the question of whether the performance of the algorithms could be improved with more expressive function approximators. In this paper, we integrate recent advances in neural network design: (i) observation and feature normalization, (ii) weight normalization, and (iii) modeling of distributional returns with an entropy-regularized MORL algorithm. The empirical results across standard continuous control benchmarks demonstrate that these changes substantially improve the quality of the produced solution sets without requiring major changes to the underlying algorithm.
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