151 citations · 780 across the 55 of their papers we have counts for
9 papers · 1 filter
Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning
Siyuan Zhang, Nan Jiang
How to select between policies and value functions produced by different training algorithms in offline reinforcement learning (RL) -- which is crucial for hyperpa-rameter tuning -…
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes
Chengchun Shi, Masatoshi Uehara, Jiawei Huang +1
We consider off-policy evaluation (OPE) in Partially Observable Markov Decision Processes (POMDPs), where the evaluation policy depends only on observable variables and the behavio…
Bellman-consistent Pessimism for Offline Reinforcement Learning
Tengyang Xie, Ching-An Cheng, Nan Jiang +2
The use of pessimism, when reasoning about datasets lacking exhaustive exploration has recently gained prominence in offline reinforcement learning. Despite the robustness it adds…
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning
Tengyang Xie, Nan Jiang, Huan Wang +2
Recent theoretical work studies sample-efficient reinforcement learning (RL) extensively in two settings: learning interactively in the environment (online RL), or learning from an…
On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction
Jiawei Huang, Nan Jiang
In this paper, we study the convergence properties of off-policy policy improvement algorithms with state-action density ratio correction under function approximation setting, wher…
Minimax Model Learning
Cameron Voloshin, Nan Jiang, Yisong Yue
We present a novel off-policy loss function for learning a transition model in model-based reinforcement learning. Notably, our loss is derived from the off-policy policy evaluatio…
Model-free Representation Learning and Exploration in Low-rank MDPs
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy +2
The low rank MDP has emerged as an important model for studying representation learning and exploration in reinforcement learning. With a known representation, several model-free e…
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang +3
We offer a theoretical characterization of off-policy evaluation (OPE) in reinforcement learning using function approximation for marginal importance weights and -functions when…
On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function
Gellért Weisz, Philip Amortila, Barnabás Janzer +3
We consider local planning in fixed-horizon MDPs with a generative model under the assumption that the optimal value function lies close to the span of a feature map. The generativ…