arXiv Machine Learning
2d ago
Online Inference in Distributional Temporal-Difference Learning
arXiv:2608. 14408v1 Announce Type: cross Abstract: We study online statistical inference for functionals of the return distribution under a fixed policy.
Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
arXiv:2608. 12973v1 Announce Type: cross Abstract: In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning.
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Statistical Efficiency and Inference of Quantile Distributional Reinforcement Learning
arXiv:2607. 08444v1 Announce Type: cross Abstract: In this paper, we study quantile-based distributional reinforcement learning from the perspective of statistical efficiency.
arXiv Machine Learning
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Temporal Difference Learning for Diffusion Models
arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.
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Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions
arXiv:2509. 10303v2 Announce Type: replace-cross Abstract: Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies through direct interaction with simulated environments.
arXiv Machine Learning
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Model-based Bootstrap of Controlled Markov Chains
arXiv:2605. 12410v2 Announce Type: replace-cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history-dependent control policies, a setting that arises naturally in offline reinforcement learning (RL) when the behavior policy generating the data is unknown.
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QPILOTS: Efficient Test-Time Q-Steering for Flow Policies
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arXiv Machine Learning
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Reverse Flow Matching: A Unified Framework for Online Reinforcement Learning with Diffusion and Flow Policies
arXiv:2601. 08136v2 Announce Type: replace Abstract: Diffusion and flow policies are gaining prominence in online reinforcement learning (RL) due to their expressive power, yet training them efficiently remains a critical challenge.
arXiv AI
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Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
arXiv:2606. 10613v1 Announce Type: cross Abstract: Diffusion-based Q-learning has emerged as a powerful paradigm for offline reinforcement learning, but its reliance on multi-step denoising makes both training and inference computationally expensive and brittle.
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Aug 3
Analytic Planning under Uncertainty with Moment Closure
Effective model-based reinforcement learning in stochastic environments requires planning that accounts for predictive uncertainty. Propagating full state distributions analytically offers a principled way to do this, but has traditionally required restrictive policy or reward structures to remain tractable.
arXiv Machine Learning
Jun 5
Path-Coupled Bellman Flows for Distributional Reinforcement Learning
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arXiv AI
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StaQ: a Finite Memory Approach to Discrete Action Policy Mirror Descent
arXiv:2506. 13862v2 Announce Type: replace-cross Abstract: In Reinforcement Learning (RL), regularization with a Kullback-Leibler divergence that penalizes large deviations between successive policies has emerged as a popular tool both in theory and practice.
arXiv Machine Learning
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A Finite-Iteration Theory for Asynchronous Categorical Distributional Temporal-Difference Learning
arXiv:2605. 06866v2 Announce Type: replace Abstract: We study finite-iteration behavior of the exact asynchronous recursions used by categorical distributional temporal-difference methods.