:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Ghosh, Debamita, Atia, George K., Wang, Yue
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2512.18957
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

ORVIT: Near-Optimal Online Distributionally Robust Reinforcement Learning
by: Ghosh, Debamita, et al.
Published: (2025)

Sample-Efficient Distributionally Robust Multi-Agent Reinforcement Learning via Online Interaction
by: Farhat, Zain Ulabedeen, et al.
Published: (2025)

Provably Sample-Efficient Robust Reinforcement Learning with Average Reward
by: Roch, Zachary, et al.
Published: (2025)

Robust Transfer Learning with Side Information
by: Awad, Akram S., et al.
Published: (2026)

CADENT: Gated Hybrid Distillation for Sample-Efficient Transfer in Reinforcement Learning
by: Alinejad, Mahyar, et al.
Published: (2026)

RLAF: Reinforcement Learning from Automaton Feedback
by: Alinejad, Mahyar, et al.
Published: (2025)

HoloBeam: Learning Optimal Beamforming in Far-Field Holographic Metasurface Transceivers
by: Ghosh, Debamita, et al.
Published: (2023)

Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
by: Zhang, Chi, et al.
Published: (2025)

Bayesian Inverse Reinforcement Learning for Non-Markovian Rewards
by: Topper, Noah, et al.
Published: (2024)

Corruption-Robust Offline Reinforcement Learning with General Function Approximation
by: Ye, Chenlu, et al.
Published: (2023)

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
by: Zhao, Runze, et al.
Published: (2025)

Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
by: Chen, Yu, et al.
Published: (2024)

Distributionally Robust Online Markov Game with Linear Function Approximation
by: Zheng, Zewu, et al.
Published: (2025)

Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
by: Singireddy, Suraj, et al.
Published: (2023)

Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function Approximation
by: Liu, Zhishuai, et al.
Published: (2024)

Thompson Sampling in Online RLHF with General Function Approximation
by: Feng, Songtao, et al.
Published: (2025)

Towards Differentially Private Reinforcement Learning with General Function Approximation
by: He, Yi, et al.
Published: (2026)

Replicable Reinforcement Learning with Linear Function Approximation
by: Eaton, Eric, et al.
Published: (2025)

Nonstationary Reinforcement Learning with Linear Function Approximation
by: Zhou, Huozhi, et al.
Published: (2020)

On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
by: Huang, Jiawei, et al.
Published: (2023)

Adaptive Resolving Methods for Reinforcement Learning with Function Approximations
by: Jiang, Jiashuo, et al.
Published: (2025)

Reinforcement Learning with Function Approximation: From Linear to Nonlinear
by: Long, Jihao, et al.
Published: (2023)

Strategically Robust Multi-Agent Reinforcement Learning with Linear Function Approximation
by: Gonzales, Jake, et al.
Published: (2026)

Provably Efficient Offline-to-Online Value Adaptation with General Function Approximation
by: Li, Shangzhe, et al.
Published: (2026)

Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness
by: Wen, Xiaoyu, et al.
Published: (2023)

Generative Modeling with Continuous Flows: Sample Complexity of Flow Matching
by: Gaur, Mudit, et al.
Published: (2025)

RL as Regressor: A Reinforcement Learning Approach for Function Approximation
by: Huang, Yongchao
Published: (2025)

Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation
by: Hwang, Taehyun, et al.
Published: (2022)

Randomized Exploration for Reinforcement Learning with Multinomial Logistic Function Approximation
by: Cho, Wooseong, et al.
Published: (2024)

Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation
by: Li, Long-Fei, et al.
Published: (2024)

Online Estimation and Inference for Robust Policy Evaluation in Reinforcement Learning
by: Liu, Weidong, et al.
Published: (2023)

Reinforcement Learning with Function Approximation for Non-Markov Processes
by: Kara, Ali Devran
Published: (2026)

RLOMM: An Efficient and Robust Online Map Matching Framework with Reinforcement Learning
by: Chen, Minxiao, et al.
Published: (2025)

Model-Free Robust Reinforcement Learning with Sample Complexity Analysis
by: Wang, Yudan, et al.
Published: (2024)

A Nearly Optimal and Low-Switching Algorithm for Reinforcement Learning with General Function Approximation
by: Zhao, Heyang, et al.
Published: (2023)

Online Learning for Approximately-Convex Functions with Long-term Adversarial Constraints
by: Sarkar, Dhruv, et al.
Published: (2025)

AGaLiTe: Approximate Gated Linear Transformers for Online Reinforcement Learning
by: Pramanik, Subhojeet, et al.
Published: (2023)

Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
by: Park, Jaehyun, et al.
Published: (2024)

Bellman Unbiasedness: Toward Provably Efficient Distributional Reinforcement Learning with General Value Function Approximation
by: Cho, Taehyun, et al.
Published: (2024)

Variance-Adaptive Optimal Algorithm for Reinforcement Learning with Multinomial Logit Function Approximation
by: Kim, Wonyoung, et al.
Published: (2026)