"distributional reinforcement learning" Papers
12 papers found
Conference
$Q\sharp$: Provably Optimal Distributional RL for LLM Post-Training
Jin Zhou, Kaiwen Wang, Jonathan Chang et al.
NEURIPS 2025arXiv:2502.20548
12
citations
ADDQ: Adaptive distributional double Q-learning
Leif Döring, Benedikt Wille, Maximilian Birr et al.
ICML 2025arXiv:2506.19478
1
citations
Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Bounds
Hao Liang, Zhiquan Luo
NEURIPS 2025arXiv:2210.14051
18
citations
Conformal Prediction Beyond the Horizon: Distribution-Free Inference for Policy Evaluation
Feichen Gan, Lu Youcun, Yingying Zhang et al.
NEURIPS 2025oralarXiv:2510.26026
Estimation and Inference in Distributional Reinforcement Learning
Liangyu Zhang, Yang Peng, Jiadong Liang et al.
NEURIPS 2025arXiv:2309.17262
4
citations
Intrinsic Benefits of Categorical Distributional Loss: Uncertainty-aware Regularized Exploration in Reinforcement Learning
Ke Sun, Yingnan Zhao, Enze Shi et al.
NEURIPS 2025arXiv:2110.03155
2
citations
A Distributional Analogue to the Successor Representation
Harley Wiltzer, Jesse Farebrother, Arthur Gretton et al.
ICML 2024spotlightarXiv:2402.08530
10
citations
Distributional Bellman Operators over Mean Embeddings
Li Kevin Wenliang, Gregoire Deletang, Matthew Aitchison et al.
ICML 2024oralarXiv:2312.07358
4
citations
Exploration and Anti-Exploration with Distributional Random Network Distillation
Kai Yang, jian tao, Jiafei Lyu et al.
ICML 2024arXiv:2401.09750
34
citations
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning
Kaiwen Wang, Owen Oertell, Alekh Agarwal et al.
ICML 2024arXiv:2402.07198
17
citations
Neural-Kernel Conditional Mean Embeddings
Eiki Shimizu, Kenji Fukumizu, Dino Sejdinovic
ICML 2024arXiv:2403.10859
4
citations
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Yu Chen, XiangCheng Zhang, Siwei Wang et al.
ICML 2024arXiv:2402.18159
3
citations