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2020 – today
- 2023
[j15]Han Zhong, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Can Reinforcement Learning Find Stackelberg-Nash Equilibria in General-Sum Markov Games with Myopically Rational Followers? J. Mach. Learn. Res. 24: 35:1-35:52 (2023)- [j14]Qiaomin Xie
, Yudong Chen, Zhaoran Wang, Zhuoran Yang:
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium. Math. Oper. Res. 48(1): 433-462 (2023) - [j13]Chi Jin, Zhuoran Yang, Zhaoran Wang, Michael I. Jordan:
Provably Efficient Reinforcement Learning with Linear Function Approximation. Math. Oper. Res. 48(3): 1496-1521 (2023) - [j12]Nikola Banovic, Zhuoran Yang, Aditya Ramesh, Alice Liu:
Being Trustworthy is Not Enough: How Untrustworthy Artificial Intelligence (AI) Can Deceive the End-Users and Gain Their Trust. Proc. ACM Hum. Comput. Interact. 7(CSCW1): 1-17 (2023) - [j11]Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang:
A Two-Timescale Stochastic Algorithm Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic. SIAM J. Optim. 33(1): 147-180 (2023) - [c102]Ruitu Xu, Yifei Min, Tianhao Wang, Michael I. Jordan, Zhaoran Wang, Zhuoran Yang:
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models via Reinforcement Learning. AISTATS 2023: 375-407 - [c101]Yixuan Wang, Simon Sinong Zhan, Zhilu Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu:
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning. ICCPS 2023: 132-141 - [c100]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Represent to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. ICLR 2023 - [c99]Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang:
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes. ICLR 2023 - [c98]Zhuoqing Song, Jason D. Lee, Zhuoran Yang:
Can We Find Nash Equilibria at a Linear Rate in Markov Games? ICLR 2023 - [c97]Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Wai Kin Victor Chan, Xianyuan Zhan:
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization. ICLR 2023 - [c96]Wenhao Zhan, Jason D. Lee, Zhuoran Yang:
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. ICLR 2023 - [c95]Sirui Zheng, Lingxiao Wang, Shuang Qiu, Zuyue Fu, Zhuoran Yang, Csaba Szepesvári, Zhaoran Wang:
Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics. ICLR 2023 - [c94]Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang:
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model. ICML 2023: 5194-5218 - [c93]Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang:
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. ICML 2023: 11967-11997 - [c92]Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu:
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. ICML 2023: 36593-36604 - [c91]Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee:
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. ICML 2023: 42200-42226 - [c90]Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic:
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning. L4DC 2023: 315-332 - [c89]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. EC 2023: 1188 - [i117]Zhuoqing Song, Jason D. Lee, Zhuoran Yang:
Can We Find Nash Equilibria at a Linear Rate in Markov Games? CoRR abs/2303.03095 (2023) - [i116]Ruitu Xu, Yifei Min, Tianhao Wang, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning. CoRR abs/2303.04833 (2023) - [i115]Siyu Chen, Jibang Wu, Yifan Wu, Zhuoran Yang:
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model. CoRR abs/2303.08613 (2023) - [i114]Siyu Chen, Yitan Wang, Zhaoran Wang, Zhuoran Yang:
A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations. CoRR abs/2303.11187 (2023) - [i113]Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Wai Kin Victor Chan, Xianyuan Zhan:
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization. CoRR abs/2303.15810 (2023) - [i112]Yulai Zhao, Zhuoran Yang, Zhaoran Wang, Jason D. Lee:
Local Optimization Achieves Global Optimality in Multi-Agent Reinforcement Learning. CoRR abs/2305.04819 (2023) - [i111]Zhihan Liu, Miao Lu, Wei Xiong, Han Zhong, Hao Hu, Shenao Zhang, Sirui Zheng, Zhuoran Yang, Zhaoran Wang:
One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration. CoRR abs/2305.18258 (2023) - [i110]Zihao Li, Zhuoran Yang, Mengdi Wang:
Reinforcement Learning with Human Feedback: Learning Dynamic Choices via Pessimism. CoRR abs/2305.18438 (2023) - [i109]Haoran He, Chenjia Bai, Kang Xu, Zhuoran Yang, Weinan Zhang, Dong Wang, Bin Zhao, Xuelong Li:
Diffusion Model is an Effective Planner and Data Synthesizer for Multi-Task Reinforcement Learning. CoRR abs/2305.18459 (2023) - [i108]Yufeng Zhang, Fengzhuo Zhang, Zhuoran Yang, Zhaoran Wang:
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization. CoRR abs/2305.19420 (2023) - [i107]Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic:
Provably Efficient Generalized Lagrangian Policy Optimization for Safe Multi-Agent Reinforcement Learning. CoRR abs/2306.00212 (2023) - [i106]Jiacheng Guo, Zihao Li, Huazheng Wang, Mengdi Wang, Zhuoran Yang, Xuezhou Zhang:
Provably Efficient Representation Learning with Tractable Planning in Low-Rank POMDP. CoRR abs/2306.12356 (2023) - [i105]Nuoya Xiong, Zhaoran Wang, Zhuoran Yang:
A General Framework for Sequential Decision-Making under Adaptivity Constraints. CoRR abs/2306.14468 (2023) - [i104]Pangpang Liu, Zhuoran Yang, Zhaoran Wang, Will Wei Sun:
Contextual Dynamic Pricing with Strategic Buyers. CoRR abs/2307.04055 (2023) - [i103]Siyu Chen, Mengdi Wang, Zhuoran Yang:
Actions Speak What You Want: Provably Sample-Efficient Reinforcement Learning of the Quantal Stackelberg Equilibrium from Strategic Feedbacks. CoRR abs/2307.14085 (2023) - [i102]Nuoya Xiong, Zhihan Liu, Zhaoran Wang, Zhuoran Yang:
Sample-Efficient Multi-Agent RL: An Optimization Perspective. CoRR abs/2310.06243 (2023) - [i101]Fengzhuo Zhang, Vincent Y. F. Tan, Zhaoran Wang, Zhuoran Yang:
Learning Regularized Monotone Graphon Mean-Field Games. CoRR abs/2310.08089 (2023) - [i100]Fengzhuo Zhang, Vincent Y. F. Tan, Zhaoran Wang, Zhuoran Yang:
Learning Regularized Graphon Mean-Field Games with Unknown Graphons. CoRR abs/2310.17531 (2023) - [i99]Shuang Qiu, Ziyu Dai, Han Zhong, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
Posterior Sampling for Competitive RL: Function Approximation and Partial Observation. CoRR abs/2310.19861 (2023) - [i98]Jianqing Fan, Zhaoran Wang, Zhuoran Yang, Chenlu Ye:
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks. CoRR abs/2311.13180 (2023) - [i97]Yixuan Wang, Ruochen Jiao, Chengtian Lang, Simon Sinong Zhan, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu:
Empowering Autonomous Driving with Large Language Models: A Safety Perspective. CoRR abs/2312.00812 (2023) - 2022
- [c88]Zehao Dou, Zhuoran Yang, Zhaoran Wang, Simon S. Du:
Gap-Dependent Bounds for Two-Player Markov Games. AISTATS 2022: 432-455 - [c87]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhi-Hong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. ICLR 2022 - [c86]Baihe Huang, Jason D. Lee, Zhaoran Wang, Zhuoran Yang:
Towards General Function Approximation in Zero-Sum Markov Games. ICLR 2022 - [c85]Zhi Zhang, Zhuoran Yang, Han Liu, Pratap Tokekar, Furong Huang:
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory. ICLR 2022 - [c84]Qi Cai, Zhuoran Yang, Zhaoran Wang:
Reinforcement Learning from Partial Observation: Linear Function Approximation with Provable Sample Efficiency. ICML 2022: 2485-2522 - [c83]Siyu Chen, Donglin Yang, Jiayang Li, Senmiao Wang, Zhuoran Yang, Zhaoran Wang:
Adaptive Model Design for Markov Decision Process. ICML 2022: 3679-3700 - [c82]Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang:
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. ICML 2022: 3773-3793 - [c81]Hongyi Guo, Qi Cai, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Offline Reinforcement Learning for Partially Observable Markov Decision Processes. ICML 2022: 8016-8038 - [c80]Zhihan Liu, Miao Lu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Welfare Maximization in Competitive Equilibrium: Reinforcement Learning for Markov Exchange Economy. ICML 2022: 13870-13911 - [c79]Zhihan Liu, Yufeng Zhang, Zuyue Fu, Zhuoran Yang, Zhaoran Wang:
Learning from Demonstration: Provably Efficient Adversarial Policy Imitation with Linear Function Approximation. ICML 2022: 14094-14138 - [c78]Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang:
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. ICML 2022: 14601-14638 - [c77]Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang:
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. ICML 2022: 18168-18210 - [c76]Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. ICML 2022: 27117-27142 - [c75]Gene Li, Junbo Li, Anmol Kabra, Nati Srebro, Zhaoran Wang, Zhuoran Yang:
Exponential Family Model-Based Reinforcement Learning via Score Matching. NeurIPS 2022 - [c74]Boyi Liu, Jiayang Li, Zhuoran Yang, Hoi-To Wai, Mingyi Hong, Yu Nie, Zhaoran Wang:
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence. NeurIPS 2022 - [c73]Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. NeurIPS 2022 - [c72]Grigoris Velegkas, Zhuoran Yang, Amin Karbasi:
Reinforcement Learning with Logarithmic Regret and Policy Switches. NeurIPS 2022 - [c71]Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
A Unifying Framework of Off-Policy General Value Function Evaluation. NeurIPS 2022 - [c70]Fengzhuo Zhang, Boyi Liu, Kaixin Wang, Vincent Y. F. Tan, Zhuoran Yang, Zhaoran Wang:
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL. NeurIPS 2022 - [c69]Shichao Xu, Yangyang Fu, Yixuan Wang, Zhuoran Yang, Zheng O'Neill, Zhaoran Wang, Qi Zhu:
Accelerate online reinforcement learning for building HVAC control with heterogeneous expert guidances. BuildSys@SenSys 2022: 89-98 - [c68]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. EC 2022: 471-472 - [i96]Yixuan Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu:
Joint Differentiable Optimization and Verification for Certified Reinforcement Learning. CoRR abs/2201.12243 (2022) - [i95]Han Zhong, Wei Xiong, Jiyuan Tan, Liwei Wang, Tong Zhang, Zhaoran Wang, Zhuoran Yang:
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets. CoRR abs/2202.07511 (2022) - [i94]Jibang Wu, Zixuan Zhang, Zhe Feng, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan, Haifeng Xu:
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning. CoRR abs/2202.10678 (2022) - [i93]Chenjia Bai, Lingxiao Wang, Zhuoran Yang, Zhihong Deng, Animesh Garg, Peng Liu, Zhaoran Wang:
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning. CoRR abs/2202.11566 (2022) - [i92]Boxiang Lyu, Qinglin Meng, Shuang Qiu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan:
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach. CoRR abs/2202.12797 (2022) - [i91]Grigoris Velegkas, Zhuoran Yang, Amin Karbasi:
The Best of Both Worlds: Reinforcement Learning with Logarithmic Regret and Policy Switches. CoRR abs/2203.01491 (2022) - [i90]Yifei Min, Tianhao Wang, Ruitu Xu, Zhaoran Wang, Michael I. Jordan, Zhuoran Yang:
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets. CoRR abs/2203.03684 (2022) - [i89]Qi Cai, Zhuoran Yang, Zhaoran Wang:
Sample-Efficient Reinforcement Learning for POMDPs with Linear Function Approximations. CoRR abs/2204.09787 (2022) - [i88]Boxiang Lyu, Zhaoran Wang, Mladen Kolar, Zhuoran Yang:
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. CoRR abs/2205.02450 (2022) - [i87]Xiaoyu Chen, Han Zhong, Zhuoran Yang, Zhaoran Wang, Liwei Wang:
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation. CoRR abs/2205.11140 (2022) - [i86]Lingxiao Wang, Qi Cai, Zhuoran Yang, Zhaoran Wang:
Embed to Control Partially Observed Systems: Representation Learning with Provable Sample Efficiency. CoRR abs/2205.13476 (2022) - [i85]Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang:
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes. CoRR abs/2205.13589 (2022) - [i84]Wenhao Zhan, Jason D. Lee, Zhuoran Yang:
Decentralized Optimistic Hyperpolicy Mirror Descent: Provably No-Regret Learning in Markov Games. CoRR abs/2206.01588 (2022) - [i83]Shuang Qiu, Xiaohan Wei, Jieping Ye, Zhaoran Wang, Zhuoran Yang:
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions. CoRR abs/2207.12463 (2022) - [i82]Shuang Qiu, Lingxiao Wang, Chenjia Bai, Zhuoran Yang, Zhaoran Wang:
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learning. CoRR abs/2207.14800 (2022) - [i81]Mengxin Yu, Zhuoran Yang, Jianqing Fan:
Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments. CoRR abs/2208.11040 (2022) - [i80]Zuyue Fu, Zhengling Qi, Zhaoran Wang, Zhuoran Yang, Yanxun Xu, Michael R. Kosorok:
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes. CoRR abs/2209.08666 (2022) - [i79]Fengzhuo Zhang, Boyi Liu, Kaixin Wang, Vincent Y. F. Tan, Zhuoran Yang, Zhaoran Wang:
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL. CoRR abs/2209.09845 (2022) - [i78]Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu:
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments. CoRR abs/2209.15090 (2022) - [i77]Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan:
A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design. CoRR abs/2210.10278 (2022) - [i76]Han Zhong, Wei Xiong, Sirui Zheng, Liwei Wang, Zhaoran Wang, Zhuoran Yang, Tong Zhang:
GEC: A Unified Framework for Interactive Decision Making in MDP, POMDP, and Beyond. CoRR abs/2211.01962 (2022) - [i75]Banghua Zhu, Stephen Bates, Zhuoran Yang, Yixin Wang, Jiantao Jiao, Michael I. Jordan:
The Sample Complexity of Online Contract Design. CoRR abs/2211.05732 (2022) - [i74]Ying Jin, Zhimei Ren, Zhuoran Yang, Zhaoran Wang:
Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality. CoRR abs/2212.09900 (2022) - [i73]Zuyue Fu, Zhengling Qi, Zhuoran Yang, Zhaoran Wang, Lan Wang:
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information. CoRR abs/2212.12167 (2022) - [i72]Riashat Islam, Samarth Sinha, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Animesh Garg, Zhaoran Wang, Lihong Li, Doina Precup:
Offline Policy Optimization in RL with Variance Regularizaton. CoRR abs/2212.14405 (2022) - 2021
- [j10]Liya Fu, Zhuoran Yang, Jun Zhang, Anle Long, Yan Zhou:
Generalized estimating equations for analyzing multivariate survival data. Commun. Stat. Simul. Comput. 50(10): 3060-3068 (2021) - [j9]Liya Fu, Zhuoran Yang, Fengjing Cai, You-Gan Wang:
Efficient and doubly-robust methods for variable selection and parameter estimation in longitudinal data analysis. Comput. Stat. 36(2): 781-804 (2021) - [j8]Shuang Qiu, Zhuoran Yang, Jieping Ye, Zhaoran Wang:
On Finite-Time Convergence of Actor-Critic Algorithm. IEEE J. Sel. Areas Inf. Theory 2(2): 652-664 (2021) - [j7]Kaiqing Zhang, Zhuoran Yang, Tamer Basar:
Decentralized multi-agent reinforcement learning with networked agents: recent advances. Frontiers Inf. Technol. Electron. Eng. 22(6): 802-814 (2021) - [j6]Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Basar:
Finite-Sample Analysis for Decentralized Batch Multiagent Reinforcement Learning With Networked Agents. IEEE Trans. Autom. Control. 66(12): 5925-5940 (2021) - [c67]Jiaheng Wei, Zuyue Fu, Yang Liu, Xingyu Li, Zhuoran Yang, Zhaoran Wang:
Sample Elicitation. AISTATS 2021: 2692-2700 - [c66]Yufeng Zhang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Actor-Critic for Risk-Sensitive and Robust Adversarial RL: A Linear-Quadratic Case. AISTATS 2021: 2764-2772 - [c65]Dongsheng Ding, Xiaohan Wei, Zhuoran Yang, Zhaoran Wang, Mihailo R. Jovanovic:
Provably Efficient Safe Exploration via Primal-Dual Policy Optimization. AISTATS 2021: 3304-3312 - [c64]Zuyue Fu, Zhuoran Yang, Zhaoran Wang:
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy. ICLR 2021 - [c63]Yingjie Fei, Zhuoran Yang, Zhaoran Wang:
Risk-Sensitive Reinforcement Learning with Function Approximation: A Debiasing Approach. ICML 2021: 3198-3207 - [c62]Hongyi Guo, Zuyue Fu, Zhuoran Yang, Zhaoran Wang:
Decentralized Single-Timescale Actor-Critic on Zero-Sum Two-Player Stochastic Games. ICML 2021: 3899-3909 - [c61]Haque Ishfaq, Qiwen Cui, Viet Nguyen, Alex Ayoub, Zhuoran Yang, Zhaoran Wang, Doina Precup, Lin Yang:
Randomized Exploration in Reinforcement Learning with General Value Function Approximation. ICML 2021: 4607-4616 - [c60]Ying Jin, Zhuoran Yang, Zhaoran Wang:
Is Pessimism Provably Efficient for Offline RL? ICML 2021: 5084-5096 - [c59]Lewis Liu, Yufeng Zhang, Zhuoran Yang, Reza Babanezhad, Zhaoran Wang:
Infinite-Dimensional Optimization for Zero-Sum Games via Variational Transport. ICML 2021: 7033-7044 - [c58]Shuang Qiu, Xiaohan Wei, Jieping Ye, Zhaoran Wang, Zhuoran Yang:
Provably Efficient Fictitious Play Policy Optimization for Zero-Sum Markov Games with Structured Transitions. ICML 2021: 8715-8725 - [c57]Shuang Qiu, Jieping Ye, Zhaoran Wang, Zhuoran Yang:
On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game. ICML 2021: 8737-8747 - [c56]Wesley Suttle, Kaiqing Zhang, Zhuoran Yang, Ji Liu, David N. Kraemer:
Reinforcement Learning for Cost-Aware Markov Decision Processes. ICML 2021: 9989-9999 - [c55]Weichen Wang, Jiequn Han, Zhuoran Yang, Zhaoran Wang:
Global Convergence of Policy Gradient for Linear-Quadratic Mean-Field Control/Game in Continuous Time. ICML 2021: 10772-10782 - [c54]Qiaomin Xie, Zhuoran Yang, Zhaoran Wang, Andreea Minca:
Learning While Playing in Mean-Field Games: Convergence and Optimality. ICML 2021: 11436-11447 - [c53]Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. ICML 2021: 11581-11591 - [c52]Jingwei Zhang, Zhuoran Yang, Zhengyuan Zhou, Zhaoran Wang:
Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems. L4DC 2021: 597-598 - [c51]Boyi Liu, Qi Cai, Zhuoran Yang, Zhaoran Wang:
BooVI: Provably Efficient Bootstrapped Value Iteration. NeurIPS 2021: 7041-7053 - [c50]Yufeng Zhang, Siyu Chen, Zhuoran Yang, Michael I. Jordan, Zhaoran Wang:
Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic. NeurIPS 2021: 15993-16006 - [c49]Minshuo Chen, Yan Li, Ethan Wang, Zhuoran Yang, Zhaoran Wang, Tuo Zhao:
Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL. NeurIPS 2021: 17913-17926 - [c48]Yingjie Fei, Zhuoran Yang, Yudong Chen, Zhaoran Wang:
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning. NeurIPS 2021: 20436-20446 - [c47]Lingxiao Wang, Zhuoran Yang, Zhaoran Wang:
Provably Efficient Causal Reinforcement Learning with Confounded Observational Data. NeurIPS 2021: 21164-21175 - [c46]Runzhe Wu, Yufeng Zhang, Zhuoran Yang, Zhaoran Wang:
Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration. NeurIPS 2021: 25439-25451 - [c45]Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang:
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum. NeurIPS 2021: 30271-30283 - [i71]Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang:
A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization. CoRR abs/2102.07367 (2021) - [i70]Luofeng Liao, Zuyue Fu, Zhuoran Yang, Mladen Kolar, Zhaoran Wang:
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning. CoRR abs/2102.09907 (2021) - [i69]Tengyu Xu, Zhuoran Yang, Zhaoran Wang, Yingbin Liang:
Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality. CoRR abs/2102.11866 (2021) - [i68]