Tengyang Xie | DeepAI

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∙ 02/21/2023

Adversarial Model for Offline Reinforcement Learning

We propose a novel model-based offline Reinforcement Learning (RL) frame...

0 Mohak Bhardwaj, et al. ∙

∙ 11/08/2022

ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data

We propose a new model-based offline RL framework, called Adversarial Mo...

0 Tengyang Xie, et al. ∙

∙ 10/09/2022

The Role of Coverage in Online Reinforcement Learning

Coverage conditions – which assert that the data logging distribution ad...

0 Tengyang Xie, et al. ∙

∙ 06/16/2022

Interaction-Grounded Learning with Action-inclusive Feedback

Consider the problem setting of Interaction-Grounded Learning (IGL), in ...

2 Tengyang Xie, et al. ∙

∙ 02/05/2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning

We propose Adversarially Trained Actor Critic (ATAC), a new model-free a...

0 Ching-An Cheng, et al. ∙

∙ 06/13/2021

Bellman-consistent Pessimism for Offline Reinforcement Learning

The use of pessimism, when reasoning about datasets lacking exhaustive e...

0 Tengyang Xie, et al. ∙

∙ 06/09/2021

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning

Recent theoretical work studies sample-efficient reinforcement learning ...

10 Tengyang Xie, et al. ∙

∙ 06/09/2021

Interaction-Grounded Learning

Consider a prosthetic arm, learning to adapt to its user's control signa...

0 Tengyang Xie, et al. ∙

∙ 02/05/2021

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency

We offer a theoretical characterization of off-policy evaluation (OPE) i...

2 Masatoshi Uehara, et al. ∙

∙ 11/02/2020

A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting

Recently, Wang et al. (2020) showed a highly intriguing hardness result ...

0 Philip Amortila, et al. ∙

∙ 08/11/2020

Batch Value-function Approximation with Only Realizability

We solve a long-standing problem in batch reinforcement learning (RL): l...

0 Tengyang Xie, et al. ∙

∙ 03/09/2020

Q^ Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison

We prove performance guarantees of two algorithms for approximating Q^ i...

0 Tengyang Xie, et al. ∙

∙ 06/08/2019

Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling

Motivated by the many real-world applications of reinforcement learning ...

0 Tengyang Xie, et al. ∙

∙ 05/30/2019

Provably Efficient Q-Learning with Low Switching Cost

We take initial steps in studying PAC-MDP algorithms with limited adapti...

3 Yu Bai, et al. ∙

∙ 02/01/2019

Privacy Preserving Off-Policy Evaluation

Many reinforcement learning applications involve the use of data that is...

0 Tengyang Xie, et al. ∙

∙ 09/07/2018

A Block Coordinate Ascent Algorithm for Mean-Variance Optimization

Risk management in dynamic decision problems is a primary concern in man...

0 Bo Liu, et al. ∙

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