arXiv Machine Learning

Enhancing Q-Value Updates in Deep Q-Learning via Successor-State Prediction

arXiv:2511. 03836v2 Announce Type: replace Abstract: Deep Q-Networks (DQNs) estimate future returns by learning from transitions sampled from a replay buffer.

arXiv Machine Learning

Aug 5

Revisiting TD Target Aggregation under Uncertainty in Q-Learning

arXiv:2608. 03069v1 Announce Type: new Abstract: Deep Q-Networks (DQNs) learn value functions through bootstrapped temporal-difference updates, where future returns are approximated using a greedy maximization over next-state action values.

By Lipeng Zu, Xiaonan Zhang

reinforcement-learning benchmarks

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.

By Prabhat Nagarajan, Brett Daley, Martha White, Marlos C. Machado

reinforcement-learning

arXiv AI

1d ago

Understanding and Stabilizing Deep Q-Learning via Controlled Bootstrapping and Regulated Value Dynamics

arXiv:2608. 16182v1 Announce Type: cross Abstract: Deep Q-learning (DQL) has achieved remarkable empirical success in reinforcement learning, yet its training process remains notoriously unstable.

By Bozhou Chen, Yongyi Wang, Hanyu Liu, Xionghui Yang, Wenxin Li

reinforcement-learning safety

arXiv AI

Jun 2

Drift Q-Learning

arXiv:2606. 00350v1 Announce Type: cross Abstract: Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates.

By Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh, Scott Fujimoto, Hsiu-Chin Lin, David Meger

diffusion reinforcement-learning efficiency data-engineering

arXiv Machine Learning

Jun 11

Space-sampled Value Decay: Forgetting Mechanisms for Non-stationary Deep Reinforcement Learning

arXiv:2606. 11797v1 Announce Type: new Abstract: Studies on rodents such as mice have shown the capabilities to adapt their behavior when dealing with changing parameters (``drift'') of the environment even if no information about change is provided (uncertainty) -- a behavior that can be modeled by forgetting mechanisms.

By Felix St\"orck, Fabian Hinder, Barbara Hammer

reinforcement-learning benchmarks

arXiv AI

Aug 10

Aftab: A Comprehensive Benchmark of CNN Encoders and Advanced Value Functions in Parallelized Q-Networks

arXiv:2608. 07335v1 Announce Type: cross Abstract: Recent advancements in deep reinforcement learning have increasingly favored simplified, highly parallelized paradigms.

By Taha Shieenavaz, Shabnam Zareshahraki, Loris Nanni

reinforcement-learning benchmarks

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.

By Tai Nguyen, Phong Le, Carola Doerr, Nguyen Dang

reinforcement-learning safety

arXiv AI

Jul 7

Ensemble Elastic DQN: A Step Dependent Ensemble Approach for Reducing Overestimation in Deep Value-Based Reinforcement Learning

arXiv:2506. 05716v2 Announce Type: replace-cross Abstract: Deep Q-Networks (DQN) can suffer from overestimation bias because bootstrapped targets use a maximisation operation over noisy value estimates.

By Adrian Ly, Richard Dazeley, Peter Vamplew, Francisco Cruz, Sunil Aryal

reinforcement-learning benchmarks safety

arXiv AI

Jun 16

QPILOTS: Efficient Test-Time Q-Steering for Flow Policies

arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.

By Yifan Ruan, Chenyang Cao, Andreas Burger, Ali Pesaranghader, Kaveh Kamali, Jaehong Kim, Nandita Vijaykumar, Alan Aspuru-Guzik, Igor Gilitschenski, Nicholas Rhinehart

diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 10

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.

By Thanh Nguyen, Tri Ton, Hongbin Choe, Tung M. Luu, Chang D. Yoo

diffusion reinforcement-learning efficiency

arXiv AI

Jun 16

Deep Q-Learning on H\"older Spaces

arXiv:2606. 16846v1 Announce Type: cross Abstract: We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions.

By Qian Qi

diffusion reinforcement-learning

arXiv Machine Learning

Aug 4

Upper-Expectile Multi-Step Q-Learning for Off-Policy Reinforcement Learning

arXiv:2608. 02034v1 Announce Type: new Abstract: Multi-step returns accelerate reward propagation in off-policy reinforcement learning, but couple the evaluation of each decision to the suboptimal logged actions that follow it, inducing a pessimistic bias that grows with the horizon.

By Abdelghani Ghanem, Mounir Ghogho

reinforcement-learning robotics efficiency safety

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