151 citations · 780 across the 55 of their papers we have counts for
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The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation
Philip Amortila, Nan Jiang, Csaba Szepesvári
Theoretical guarantees in reinforcement learning (RL) are known to suffer multiplicative blow-up factors with respect to the misspecification error of function approximation. Yet,…
Explaining RL Decisions with Trajectories
Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy +4
Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by…
Offline Learning in Markov Games with General Function Approximation
Yuheng Zhang, Yu Bai, Nan Jiang
We study offline multi-agent reinforcement learning (RL) in Markov games, where the goal is to learn an approximate equilibrium -- such as Nash equilibrium and (Coarse) Correlated…
Reinforcement Learning in Low-Rank MDPs with Density Features
Audrey Huang, Jinglin Chen, Nan Jiang
MDPs with low-rank transitions -- that is, the transition matrix can be factored into the product of two matrices, left and right -- is a highly representative structure that enabl…
ALSO-X#: Better Convex Approximations for Distributionally Robust Chance Constrained Programs
Nan Jiang, Weijun Xie
This paper studies distributionally robust chance constrained programs (DRCCPs), where the uncertain constraints must be satisfied with at least a probability of a prespecified thr…
Adversarial Model for Offline Reinforcement Learning
Mohak Bhardwaj, Tengyang Xie, Byron Boots +2
We propose a novel model-based offline Reinforcement Learning (RL) framework, called Adversarial Model for Offline Reinforcement Learning (ARMOR), which can robustly learn policies…