A Deep Neural Network for Modeling Music. - Pengjing Zhang; Xiaoqing Zheng; Wenqiang Zhang; Siyan Li; Sheng Qian; Wenqi He; Shangtong Zhang; Ziyuan Wang (ICMR, 2015)

Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks. - Vivek Veeriah; Shangtong Zhang; Richard S. Sutton (ECML/PKDD (1), 2017)

Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control. - Shangtong Zhang; Osmar R. Zaïane (CoRR, 2017)

A Deeper Look at Experience Replay. - Shangtong Zhang; Richard S. Sutton (CoRR, 2017)

QUOTA: The Quantile Option Architecture for Reinforcement Learning. - Shangtong Zhang; Borislav Mavrin; Linglong Kong; Bo Liu 006; Hengshuai Yao (CoRR, 2018)

ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search. - Shangtong Zhang; Hao Chen; Hengshuai Yao (CoRR, 2018)

mlpack 3: a fast, flexible machine learning library. - Ryan R. Curtin; Marcus Edel; Mikhail Lozhnikov; Yannis Mentekidis; Sumedh Ghaisas; Shangtong Zhang (J. Open Source Softw., 2018)

ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search. - Shangtong Zhang; Hengshuai Yao (AAAI, 2019)

QUOTA: The Quantile Option Architecture for Reinforcement Learning. - Shangtong Zhang; Hengshuai Yao (AAAI, 2019)

Exploration in the Face of Parametric and Intrinsic Uncertainties. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong (AAMAS, 2019)

Generalized Off-Policy Actor-Critic. - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (NeurIPS, 2019)

DAC: The Double Actor-Critic Architecture for Learning Options. - Shangtong Zhang; Shimon Whiteson (NeurIPS, 2019)

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards. - Yuhang Song 001; Jianyi Wang; Thomas Lukasiewicz; Zhenghua Xu 001; Shangtong Zhang; Mai Xu (CoRR, 2019)

Distributional Reinforcement Learning for Efficient Exploration. - Borislav Mavrin; Shangtong Zhang; Hengshuai Yao; Linglong Kong; Kaiwen Wu; Yaoliang Yu (CoRR, 2019)

Provably Convergent Off-Policy Actor-Critic with Function Approximation. - Shangtong Zhang; Bo Liu 006; Hengshuai Yao; Shimon Whiteson (CoRR, 2019)

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards. - Yuhang Song 001; Jianyi Wang; Thomas Lukasiewicz; Zhenghua Xu 001; Shangtong Zhang; Andrzej Wojcicki; Mai Xu (AAAI, 2020)

Deep Residual Reinforcement Learning. - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (AAMAS, 2020)

GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values. - Shangtong Zhang; Bo Liu 006; Shimon Whiteson (ICML, 2020)

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation. - Shangtong Zhang; Bo Liu 006; Hengshuai Yao; Shimon Whiteson (ICML, 2020)

Learning Retrospective Knowledge with Reverse Reinforcement Learning. - Shangtong Zhang; Vivek Veeriah; Shimon Whiteson (NeurIPS, 2020)

Per-Step Reward: A New Perspective for Risk-Averse Reinforcement Learning. - Shangtong Zhang; Bo Liu 006; Shimon Whiteson (CoRR, 2020)

Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning. - Shangtong Zhang; Bo Liu 006; Shimon Whiteson (AAAI, 2021)

Average-Reward Off-Policy Policy Evaluation with Function Approximation. - Shangtong Zhang; Yi Wan 004; Richard S. Sutton; Shimon Whiteson (ICML, 2021)

Breaking the Deadly Triad with a Target Network. - Shangtong Zhang; Hengshuai Yao; Shimon Whiteson (ICML, 2021)

Deep Residual Reinforcement Learning (Extended Abstract). - Shangtong Zhang; Wendelin Boehmer; Shimon Whiteson (IJCAI, 2021)

Learning Expected Emphatic Traces for Deep RL. - Ray Jiang; Shangtong Zhang; Veronica Chelu; Adam White 001; Hado van Hasselt (AAAI, 2022)

A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms. - Shangtong Zhang; Romain Laroche; Harm van Seijen; Shimon Whiteson; Remi Tachet des Combes (AAMAS, 2022)

On the Chattering of SARSA with Linear Function Approximation. - Shangtong Zhang; Remi Tachet des Combes; Romain Laroche (CoRR, 2022)

Global Optimality and Finite Sample Analysis of Softmax Off-Policy Actor Critic under State Distribution Mismatch. - Shangtong Zhang; Remi Tachet des Combes; Romain Laroche (J. Mach. Learn. Res., 2022)

Truncated Emphatic Temporal Difference Methods for Prediction and Control. - Shangtong Zhang; Shimon Whiteson (J. Mach. Learn. Res., 2022)

A New Challenge in Policy Evaluation. - Shangtong Zhang (AAAI, 2023)

On the Convergence of SARSA with Linear Function Approximation. - Shangtong Zhang; Remi Tachet des Combes; Romain Laroche (ICML, 2023)

Improving Monte Carlo Evaluation with Offline Data. - Shuze Daniel Liu; Shangtong Zhang (CoRR, 2023)

Direct Gradient Temporal Difference Learning. - Xiaochi Qian; Shangtong Zhang (CoRR, 2023)

AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning. - Michaël Mathieu; Sherjil Ozair; Srivatsan Srinivasan; Çaglar Gülçehre; Shangtong Zhang; Ray Jiang; Tom Le Paine; Richard Powell; Konrad Zolna; Julian Schrittwieser; David H. Choi; Petko Georgiev; et al. (CoRR, 2023)

IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations. - Yuntao Wei; Xueyan Wang; Shangtong Zhang; Jianlei Yang 001; Xiaotao Jia; Zhaohao Wang; Gang Qu 001; Weisheng Zhao (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design. - Shuze Daniel Liu; Shangtong Zhang (ICML, 2024)

CRISP: Triangle Counting Acceleration via Content Addressable Memory-Integrated 3D-Stacked Memory. - Shangtong Zhang; Xueyan Wang; Weisheng Zhao; Yier Jin (ITC-Asia, 2024)

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise. - Shuze Daniel Liu; Shuhang Chen; Shangtong Zhang (CoRR, 2024)

Transformers Learn Temporal Difference Methods for In-Context Reinforcement Learning. - Jiuqi Wang; Ethan Blaser; Hadi Daneshmand; Shangtong Zhang (CoRR, 2024)

Efficient Multi-Policy Evaluation for Reinforcement Learning. - Shuze Daniel Liu; Yuxin Chen; Shangtong Zhang (CoRR, 2024)

Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features. - Jiuqi Wang; Shangtong Zhang (CoRR, 2024)

Almost Sure Convergence of Average Reward Temporal Difference Learning. - Ethan Blaser; Shangtong Zhang (CoRR, 2024)

Almost Sure Convergence Rates and Concentration of Stochastic Approximation and Reinforcement Learning with Markovian Noise. - Xiaochi Qian; Zixuan Xie; Xinyu Liu; Shangtong Zhang (CoRR, 2024)

CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening. - Amar Kulkarni; Shangtong Zhang; Madhur Behl (CoRR, 2024)

Efficient Multi-Policy Evaluation for Reinforcement Learning. - Shuze Daniel Liu; Claire Chen; Shangtong Zhang (AAAI, 2025)

Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning. - Claire Chen; Shuze Daniel Liu; Shangtong Zhang (ICLR, 2025)

Doubly Optimal Policy Evaluation for Reinforcement Learning. - Shuze Daniel Liu; Claire Chen; Shangtong Zhang (ICLR, 2025)

Revisiting a Design Choice in Gradient Temporal Difference Learning. - Xiaochi Qian; Shangtong Zhang (ICLR, 2025)

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning. - Jiuqi Wang; Ethan Blaser; Hadi Daneshmand; Shangtong Zhang (ICLR, 2025)

Linear Q-Learning Does Not Diverge: Convergence Rates to a Bounded Set. - Xinyu Liu; Zixuan Xie; Shangtong Zhang (CoRR, 2025)

A Survey of In-Context Reinforcement Learning. - Amir Moeini; Jiuqi Wang; Jacob Beck; Ethan Blaser; Shimon Whiteson; Rohan Chandra; Shangtong Zhang (CoRR, 2025)

Towards Large Language Models that Benefit for All: Benchmarking Group Fairness in Reward Models. - Kefan Song; Jin Yao; Runnan Jiang; Rohan Chandra; Shangtong Zhang (CoRR, 2025)

Group Fairness in Multi-Task Reinforcement Learning. - Kefan Song; Runnan Jiang; Rohan Chandra; Shangtong Zhang (CoRR, 2025)

GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments. - Vagul Mahadevan; Shangtong Zhang; Rohan Chandra (CoRR, 2025)

Experience Replay Addresses Loss of Plasticity in Continual Learning. - Jiuqi Wang; Rohan Chandra; Shangtong Zhang (CoRR, 2025)

Counterfactual Explanations for Continuous Action Reinforcement Learning. - Shuyang Dong; Shangtong Zhang; Lu Feng (CoRR, 2025)

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features. - Zixuan Xie; Xinyu Liu; Rohan Chandra; Shangtong Zhang (CoRR, 2025)

Reward Is Enough: LLMs Are In-Context Reinforcement Learners. - Kefan Song; Amir Moeini; Peng Wang; Lei Gong; Rohan Chandra; Yanjun Qi; Shangtong Zhang (CoRR, 2025)