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?
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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arXiv Machine Learning
Jun 19
Direct Advantage Estimation for Scalable and Sample-efficient Deep Reinforcement Learning
arXiv:2606. 20411v1 Announce Type: new Abstract: Direct Advantage Estimation (DAE) has been shown to improve the sample efficiency of deep reinforcement learning algorithms.
arXiv Machine Learning
Jun 10
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.
Hugging Face Trending Papers
Jun 4
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, making it unclear which components are essential for scalability.
arXiv AI
Jun 10
Bellman-Taylor Score Decoding for Markov Decision Processes with State-Dependent Feasible Action Sets
arXiv:2606. 10979v1 Announce Type: new Abstract: Many Markov decision processes (MDPs) in operations research have feasible actions that are state dependent and defined implicitly by various operational constraints.
arXiv Machine Learning
Jun 5
Representation Learning Enables Scalable Multitask Deep Reinforcement Learning
arXiv:2606. 05555v1 Announce Type: new Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge.
arXiv AI
Jun 10
Test-Time Gradient Guidance of Flow Policies in Reinforcement Learning
arXiv:2606. 11087v1 Announce Type: cross Abstract: Expressive continuous control policies, such as diffusion and flow models, form the backbone of recent advances in scaling imitation learning for simulated and real robot control.
arXiv AI
Jun 30
Accelerating Q-learning through Efficient Value-Sharing across Actions
arXiv:2606. 29806v1 Announce Type: cross Abstract: Action-values are foundational to many control algorithms such as Q-learning.
arXiv AI
Jun 30
RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
arXiv:2606. 29867v1 Announce Type: cross Abstract: Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade performance.
arXiv AI
Jun 2
Certificate-Guided Evaluation of Reinforcement Learning Generalization
arXiv:2606. 00840v1 Announce Type: new Abstract: This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks.
arXiv AI
Aug 10
Momba: Network Modernization Improves Multi-Objective Reinforcement Learning
arXiv:2608. 07180v1 Announce Type: cross Abstract: 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.
arXiv Machine Learning
Jul 21
Information-Based Exploration via Random Features for Reinforcement Learning
arXiv:2607. 17981v1 Announce Type: new Abstract: Representation learning has enabled classical exploration strategies to be extended to deep Reinforcement Learning (RL), but often makes algorithms more complex and theoretical guarantees harder to establish.