arXiv Machine Learning
Jun 16
On the Role of Computation in Reinforcement Learning
arXiv:2602. 05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning?
Discovering Interpretable Multi-Parameter Control Policies for Evolutionary Algorithms Using Deep Reinforcement Learning
arXiv:2606. 10129v1 Announce Type: new Abstract: While deep Reinforcement Learning (deep-RL) has been increasingly applied to parameter control in evolutionary algorithms, rigorous theoretical analysis of parameter control remains largely restricted to single-parameter settings, owing to the difficulty of deriving effective, interpretable multi-parameter policies amenable to formal study.
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Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
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Interpret Policies in Deep Reinforcement Learning using SILVER with RL-Guided Labeling: A Model-level Approach to High-dimensional and Multi-action Environments
arXiv:2510. 19244v3 Announce Type: replace Abstract: Deep reinforcement learning (RL) achieves remarkable performance but lacks interpretability, limiting trust in policy behavior.
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Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
arXiv:2607. 07769v1 Announce Type: cross Abstract: Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade.
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Parameter Exploration for RLVR via Variational Learning
arXiv:2608. 09805v1 Announce Type: cross Abstract: Exploration has been a focus of reinforcement learning research for a long time.
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Enhancing Q-Value Updates in Deep Q-Learning via Successor-State Prediction
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