151 citations · 780 across the 55 of their papers we have counts for
8 papers · 1 filter
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
Cameron Voloshin, Hoang M. Le, Nan Jiang +1
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications…
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Aditya Modi, Nan Jiang, Ambuj Tewari +1
Reinforcement learning (RL) methods have been shown to be capable of learning intelligent behavior in rich domains. However, this has largely been done in simulated domains without…
Minimax Weight and Q-Function Learning for Off-Policy Evaluation
Masatoshi Uehara, Jiawei Huang, Nan Jiang
We provide theoretical investigations into off-policy evaluation in reinforcement learning using function approximators for (marginalized) importance weights and value functions. O…
From Importance Sampling to Doubly Robust Policy Gradient
Jiawei Huang, Nan Jiang
We show that on-policy policy gradient (PG) and its variance reduction variants can be derived by taking finite difference of function evaluations supplied by estimators from the i…
On Value Functions and the Agent-Environment Boundary
Nan Jiang
When function approximation is deployed in reinforcement learning (RL), the same problem may be formulated in different ways, often by treating a pre-processing step as a part of t…
Provably Efficient Q-Learning with Low Switching Cost
Yu Bai, Tengyang Xie, Nan Jiang +1
We take initial steps in studying PAC-MDP algorithms with limited adaptivity, that is, algorithms that change its exploration policy as infrequently as possible during regret minim…
Information-Theoretic Considerations in Batch Reinforcement Learning
Jinglin Chen, Nan Jiang
Value-function approximation methods that operate in batch mode have foundational importance to reinforcement learning (RL). Finite sample guarantees for these methods often crucia…
Provably efficient RL with Rich Observations via Latent State Decoding
Simon S. Du, Akshay Krishnamurthy, Nan Jiang +3
We study the exploration problem in episodic MDPs with rich observations generated from a small number of latent states. Under certain identifiability assumptions, we demonstrate h…