Shangtong Zhang

According to our database, Shangtong Zhang authored at least 79 papers between 2015 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Convergence of Two-Timescale Markovian Stochastic Approximations with Applications in Reinforcement Learning.

Vagul Mahadevan, Claire Chen, Shuze Daniel Liu, Shangtong Zhang

CoRR, May, 2026

Latent Q-Barrier Shielding for Safe In-Context Reinforcement Learning.

Minjae Kwon, Amir Moeini, Shangtong Zhang, Lu Feng

CoRR, May, 2026

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective.

Jiuqi Wang, Jayanth Srinivasa, Claire Chen, Shuze Daniel Liu, Ali Payani, Shangtong Zhang

CoRR, May, 2026

Beyond Linear Attention: Softmax Transformers Implement In-Context Reinforcement Learning.

Zixuan Xie, Xinyu Liu, Claire Chen, Shuze Daniel Liu, Rohan Chandra, Shangtong Zhang

CoRR, May, 2026

MathlibPR: Pull Request Merge-Readiness Benchmark for Formal Mathematical Libraries.

Zixuan Xie, Xinyu Liu, Shangtong Zhang

CoRR, May, 2026

Convergence and Emergence of In-Context Reinforcement Learning with Chain of Thought.

Zixuan Xie, Xinyu Liu, Rohan Chandra, Shangtong Zhang

CoRR, May, 2026

Almost Sure Convergence Rates of Stochastic Approximation and Reinforcement Learning via a Poisson-Moreau Drift.

Xinyu Liu, Zixuan Xie, Shangtong Zhang

CoRR, May, 2026

On the Divergence of Differential Temporal Difference Learning without Local Clocks.

David Antrobius, Shangtong Zhang

CoRR, May, 2026

Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning.

Alper Kamil Bozkurt, Xiaoan Xu, Shangtong Zhang, Miroslav Pajic, Yuichi Motai

CoRR, May, 2026

Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes.

Ethan Blaser, Jiuqi Wang, Shangtong Zhang

CoRR, February, 2026

MathlibLemma: Folklore Lemma Generation and Benchmark for Formal Mathematics.

Xinyu Liu, Zixuan Xie, Amir Moeini, Claire Chen, Shuze Daniel Liu, Yu Meng, Aidong Zhang, Shangtong Zhang

CoRR, February, 2026

Multi-agent DRL-based Lane Change Decision Model for Cooperative Planning in Mixed Traffic.

Zeyu Mu, Shangtong Zhang, B. Brian Park

CoRR, January, 2026

PRISM: A Locality-Aware Near-Memory Processing Framework for Scalable Triangle Counting.

Shangtong Zhang, Xueyan Wang, Yier Jin

Proceedings of the Design, Automation & Test in Europe Conference, 2026

Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise.

Ethan Blaser, Shangtong Zhang

Proceedings of the Fortieth AAAI Conference on Artificial Intelligence, 2026

2025

Prompt-Driven Domain Adaptation for End-to-End Autonomous Driving via In-Context RL.

Aleesha Khurram, Amir Moeini, Shangtong Zhang, Rohan Chandra

CoRR, November, 2025

Towards Formalizing Reinforcement Learning Theory.

Shangtong Zhang

CoRR, November, 2025

Extensions of Robbins-Siegmund Theorem with Applications in Reinforcement Learning.

Xinyu Liu, Zixuan Xie, Shangtong Zhang

CoRR, September, 2025

Safe In-Context Reinforcement Learning.

Amir Moeini, Minjae Kwon, Alper Kamil Bozkurt, Yuichi Motai, Rohan Chandra, Lu Feng, Shangtong Zhang

CoRR, September, 2025

Towards Provable Emergence of In-Context Reinforcement Learning.

Jiuqi Wang, Rohan Chandra, Shangtong Zhang

CoRR, September, 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, June, 2025

Experience Replay Addresses Loss of Plasticity in Continual Learning.

Jiuqi Wang, Rohan Chandra, Shangtong Zhang

CoRR, March, 2025

Group Fairness in Multi-Task Reinforcement Learning.

Kefan Song, Runnan Jiang, Rohan Chandra, Shangtong Zhang

CoRR, March, 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, March, 2025

A Survey of In-Context Reinforcement Learning.

Amir Moeini, Jiuqi Wang, Jacob Beck, Ethan Blaser, Shimon Whiteson, Rohan Chandra, Shangtong Zhang

CoRR, February, 2025

Linear <i>Q</i>-Learning Does Not Diverge: Convergence Rates to a Bounded Set.

Xinyu Liu, Zixuan Xie, Shangtong Zhang

CoRR, January, 2025

The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise.

Shuze Daniel Liu, Shuhang Chen, Shangtong Zhang

J. Mach. Learn. Res., 2025

Finite Sample Analysis of Linear Temporal Difference Learning with Arbitrary Features.

Zixuan Xie, Xinyu Liu, Rohan Chandra, Shangtong Zhang

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

GameChat: Multi-LLM Dialogue for Safe, Agile, and Socially Optimal Multi-Agent Navigation in Constrained Environments.

Vagul Mahadevan, Shangtong Zhang, Rohan Chandra

Proceedings of the IEEE International Symposium on Multi-Robot and Multi-Agent Systems, 2025

Counterfactual Explanations for Continuous Action Reinforcement Learning.

Shuyang Dong, Shangtong Zhang, Lu Feng

Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, 2025

Linear Q-Learning Does Not Diverge in L2: Convergence Rates to a Bounded Set.

Xinyu Liu, Zixuan Xie, Shangtong Zhang

Proceedings of the Forty-second International Conference on Machine Learning, 2025

Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning.

Jiuqi Wang, Ethan Blaser, Hadi Daneshmand, Shangtong Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Revisiting a Design Choice in Gradient Temporal Difference Learning.

Xiaochi Qian, Shangtong Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Doubly Optimal Policy Evaluation for Reinforcement Learning.

Shuze Daniel Liu, Claire Chen, Shangtong Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Efficient Policy Evaluation with Safety Constraint for Reinforcement Learning.

Claire Chen, Shuze Daniel Liu, Shangtong Zhang

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

Efficient Multi-Policy Evaluation for Reinforcement Learning.

Shuze Daniel Liu, Claire Chen, Shangtong Zhang

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening.

Amar Kulkarni, Shangtong Zhang, Madhur Behl

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

Almost Sure Convergence of Average Reward Temporal Difference Learning.

Ethan Blaser, Shangtong Zhang

CoRR, 2024

Almost Sure Convergence of Linear Temporal Difference Learning with Arbitrary Features.

Jiuqi Wang, Shangtong Zhang

CoRR, 2024

Efficient Multi-Policy Evaluation for Reinforcement Learning.

Shuze Daniel Liu, Yuxin 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

CRISP: Triangle Counting Acceleration via Content Addressable Memory-Integrated 3D-Stacked Memory.

Shangtong Zhang, Xueyan Wang, Weisheng Zhao, Yier Jin

Proceedings of the IEEE International Test Conference in Asia, 2024

Efficient Policy Evaluation with Offline Data Informed Behavior Policy Design.

Shuze Daniel Liu, Shangtong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

IMGA: Efficient In-Memory Graph Convolution Network Aggregation With Data Flow Optimizations.

Yuntao Wei, Xueyan Wang, Shangtong Zhang, Jianlei Yang, Xiaotao Jia, Zhaohao Wang, Gang Qu, Weisheng Zhao

IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., December, 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, Daniel Toyama, Aja Huang, Roman Ring, Igor Babuschkin, Timo Ewalds, Mahyar Bordbar, Sarah Henderson, Sergio Gómez Colmenarejo, Aäron van den Oord, Wojciech Marian Czarnecki, Nando de Freitas, Oriol Vinyals

CoRR, 2023

Direct Gradient Temporal Difference Learning.

Xiaochi Qian, Shangtong Zhang

CoRR, 2023

Improving Monte Carlo Evaluation with Offline Data.

Shuze Daniel Liu, Shangtong Zhang

CoRR, 2023

On the Convergence of SARSA with Linear Function Approximation.

Shangtong Zhang, Remi Tachet des Combes, Romain Laroche

Proceedings of the International Conference on Machine Learning, 2023

A New Challenge in Policy Evaluation.

Shangtong Zhang

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Truncated Emphatic Temporal Difference Methods for Prediction and Control.

Shangtong Zhang, Shimon Whiteson

J. Mach. Learn. Res., 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

On the Chattering of SARSA with Linear Function Approximation.

Shangtong Zhang, Remi Tachet des Combes, Romain Laroche

CoRR, 2022

A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms.

Shangtong Zhang, Romain Laroche, Harm van Seijen, Shimon Whiteson, Remi Tachet des Combes

Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 2022

Learning Expected Emphatic Traces for Deep RL.

Ray Jiang, Shangtong Zhang, Veronica Chelu, Adam White, Hado van Hasselt

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

Deep Residual Reinforcement Learning (Extended Abstract).

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Breaking the Deadly Triad with a Target Network.

Shangtong Zhang, Hengshuai Yao, Shimon Whiteson

Proceedings of the 38th International Conference on Machine Learning, 2021

Average-Reward Off-Policy Policy Evaluation with Function Approximation.

Shangtong Zhang, Yi Wan, Richard S. Sutton, Shimon Whiteson

Proceedings of the 38th International Conference on Machine Learning, 2021

Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning.

Shangtong Zhang, Bo Liu, Shimon Whiteson

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Per-Step Reward: A New Perspective for Risk-Averse Reinforcement Learning.

Shangtong Zhang, Bo Liu, Shimon Whiteson

CoRR, 2020

Learning Retrospective Knowledge with Reverse Reinforcement Learning.

Shangtong Zhang, Vivek Veeriah, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Provably Convergent Two-Timescale Off-Policy Actor-Critic with Function Approximation.

Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson

Proceedings of the 37th International Conference on Machine Learning, 2020

GradientDICE: Rethinking Generalized Offline Estimation of Stationary Values.

Shangtong Zhang, Bo Liu, Shimon Whiteson

Proceedings of the 37th International Conference on Machine Learning, 2020

Deep Residual Reinforcement Learning.

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, 2020

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards.

Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Shangtong Zhang, Andrzej Wojcicki, Mai Xu

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Provably Convergent Off-Policy Actor-Critic with Function Approximation.

Shangtong Zhang, Bo Liu, Hengshuai Yao, Shimon Whiteson

CoRR, 2019

Distributional Reinforcement Learning for Efficient Exploration.

Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong, Kaiwen Wu, Yaoliang Yu

CoRR, 2019

Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards.

Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Shangtong Zhang, Mai Xu

CoRR, 2019

DAC: The Double Actor-Critic Architecture for Learning Options.

Shangtong Zhang, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Generalized Off-Policy Actor-Critic.

Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Exploration in the Face of Parametric and Intrinsic Uncertainties.

Borislav Mavrin, Shangtong Zhang, Hengshuai Yao, Linglong Kong

Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems, 2019

QUOTA: The Quantile Option Architecture for Reinforcement Learning.

Shangtong Zhang, Hengshuai Yao

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search.

Shangtong Zhang, Hengshuai Yao

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

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, Hao Chen, Hengshuai Yao

CoRR, 2018

QUOTA: The Quantile Option Architecture for Reinforcement Learning.

Shangtong Zhang, Borislav Mavrin, Linglong Kong, Bo Liu, Hengshuai Yao

CoRR, 2018

2017

A Deeper Look at Experience Replay.

Shangtong Zhang, Richard S. Sutton

CoRR, 2017

Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control.

Shangtong Zhang, Osmar R. Zaïane

CoRR, 2017

Crossprop: Learning Representations by Stochastic Meta-Gradient Descent in Neural Networks.

Vivek Veeriah, Shangtong Zhang, Richard S. Sutton

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

2015

A Deep Neural Network for Modeling Music.

Pengjing Zhang, Xiaoqing Zheng, Wenqiang Zhang, Siyan Li, Sheng Qian, Wenqi He, Shangtong Zhang, Ziyuan Wang

Proceedings of the 5th ACM on International Conference on Multimedia Retrieval, 2015