Cover Image

Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Vincent, Théo, Wahren, Fabian, Peters, Jan, Belousov, Boris, D'Eramo, Carlo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Iterated $Q$-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2024)

Eau De $Q$-Network: Adaptive Distillation of Neural Networks in Deep Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2025)

Parameterized Projected Bellman Operator
by: Vincent, Théo, et al.
Published: (2023)

Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
by: Vincent, Théo, et al.
Published: (2025)

$K$-Level Policy Gradients for Multi-Agent Reinforcement Learning
by: Reddi, Aryaman, et al.
Published: (2025)

Gradient Iterated Temporal-Difference Learning
by: Vincent, Théo, et al.
Published: (2026)

Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
by: Hendawy, Ahmed, et al.
Published: (2023)

Use the Online Network If You Can: Towards Fast and Stable Reinforcement Learning
by: Hendawy, Ahmed, et al.
Published: (2025)

Sharing Knowledge in Multi-Task Deep Reinforcement Learning
by: D'Eramo, Carlo, et al.
Published: (2024)

On the Benefit of Optimal Transport for Curriculum Reinforcement Learning
by: Klink, Pascal, et al.
Published: (2023)

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning
by: Farr, Noah, et al.
Published: (2026)

Deep Reinforcement Learning Agents are not even close to Human Intelligence
by: Delfosse, Quentin, et al.
Published: (2025)

Learning to Explore in Diverse Reward Settings via Temporal-Difference-Error Maximization
by: Griesbach, Sebastian, et al.
Published: (2025)

MuJoCo MPC for Humanoid Control: Evaluation on HumanoidBench
by: Meser, Moritz, et al.
Published: (2024)

Deterministic Exploration via Stationary Bellman Error Maximization
by: Griesbach, Sebastian, et al.
Published: (2024)

Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learning
by: Holgado-Alvarez, Jose-Luis, et al.
Published: (2025)

Do Not Imitate, Reinforce: Iterative Classification via Belief Refinement
by: Kallel, Mahdi, et al.
Published: (2026)

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
by: Palenicek, Daniel, et al.
Published: (2025)

CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
by: Bhatt, Aditya, et al.
Published: (2019)

Adaptive Neighborhood-Constrained Q Learning for Offline Reinforcement Learning
by: Mao, Yixiu, et al.
Published: (2025)

SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Ratio Reinforcement Learning
by: Romeo, Carlo, et al.
Published: (2025)

Scaling CrossQ with Weight Normalization
by: Palenicek, Daniel, et al.
Published: (2025)

Uncertainty Representations in State-Space Layers for Deep Reinforcement Learning under Partial Observability
by: Luis, Carlos E., et al.
Published: (2024)

Domain Randomization via Entropy Maximization
by: Tiboni, Gabriele, et al.
Published: (2023)

Double Successive Over-Relaxation Q-Learning with an Extension to Deep Reinforcement Learning
by: R, Shreyas S
Published: (2024)

Adaptive Data Exploitation in Deep Reinforcement Learning
by: Yuan, Mingqi, et al.
Published: (2025)

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization
by: Palenicek, Daniel, et al.
Published: (2025)

Offline Reinforcement Learning with Imputed Rewards
by: Romeo, Carlo, et al.
Published: (2024)

ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
by: Wu, Kun, et al.
Published: (2024)

When is Offline Policy Selection Sample Efficient for Reinforcement Learning?
by: Liu, Vincent, et al.
Published: (2023)

Learning Distinguishable Representations in Deep Q-Networks for Linear Transfer
by: Sathish, Sooraj, et al.
Published: (2025)

Adaptive Target Localization under Uncertainty using Multi-Agent Deep Reinforcement Learning with Knowledge Transfer
by: Alagha, Ahmed, et al.
Published: (2025)

Augmented Bayesian Policy Search
by: Kallel, Mahdi, et al.
Published: (2024)

Deep Reinforcement Learning with Spiking Q-learning
by: Chen, Ding, et al.
Published: (2022)

Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
by: Jin, Yonggang, et al.
Published: (2023)

Value-Distributional Model-Based Reinforcement Learning
by: Luis, Carlos E., et al.
Published: (2023)

schlably: A Python Framework for Deep Reinforcement Learning Based Scheduling Experiments
by: de Puiseau, Constantin Waubert, et al.
Published: (2023)

Pretraining a Shared Q-Network for Data-Efficient Offline Reinforcement Learning
by: Park, Jongchan, et al.
Published: (2025)

Robust Deep Reinforcement Learning with Adaptive Adversarial Perturbations in Action Space
by: Liu, Qianmei, et al.
Published: (2024)

Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
by: Shaik, Thanveer, et al.
Published: (2023)