"linear function approximation" Papers
10 papers found
Conference
Computational Hardness of Reinforcement Learning with Partial $q^{\pi}$-Realizability
Shayan Karimi, Xiaoqi Tan
NEURIPS 2025
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy et al.
ICLR 2025arXiv:2406.11810
3
citations
Deployment Efficient Reward-Free Exploration with Linear Function Approximation
Zihan Zhang, Yuxin Chen, Jason Lee et al.
NEURIPS 2025
Offline Actor-Critic for Average Reward MDPs
William Powell, Jeongyeol Kwon, Qiaomin Xie et al.
NEURIPS 2025
73
citations
Plastic Learning with Deep Fourier Features
Alex Lewandowski, Dale Schuurmans, Marlos C. Machado
ICLR 2025arXiv:2410.20634
12
citations
Provably Efficient RL under Episode-Wise Safety in Constrained MDPs with Linear Function Approximation
Toshinori Kitamura, Arnob Ghosh, Tadashi Kozuno et al.
NEURIPS 2025spotlightarXiv:2502.10138
The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang
NEURIPS 2025oralarXiv:2401.07844
13
citations
Averaging $n$-step Returns Reduces Variance in Reinforcement Learning
Brett Daley, Martha White, Marlos C. Machado
ICML 2024oralarXiv:2402.03903
5
citations
Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization
Yihan Du, Anna Winnicki, Gal Dalal et al.
ICML 2024arXiv:2402.10342
17
citations
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret
Han Zhong, Jiachen Hu, Yecheng Xue et al.
ICML 2024arXiv:2302.10796
11
citations