Author

J. Pajarinen

2 papers indexed here

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Preprint Aug 2026

Momba: Network Modernization Improves Multi-Objective Reinforcement Learning

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...

Adam Štafa, Santeri Heiskanen, Petr Novotný et al. · 0 citations

#artificial intelligence Preprint Sep 2026

SUN: Reaching for Novelty in Reinforcement Learning

Exploration in reinforcement learning (RL) remains a fundamental challenge. Recent goal-conditioned RL strategies (which select goals to encourage broader state coverage) have shown promising results, but none scores a goal by novelty and reachability jointly: the two signals are traded off by hand, applied in sequence...

Wen-Yan Yang, A. Mustafin, Dominik Baumann et al. · 0 citations