212 citations · 549 across the 39 of their papers we have counts for
40 papers
The Sample Complexity of Online Strategic Decision Making with Information Asymmetry and Knowledge Transportability
Jiachen Hu, Rui Ai, Han Zhong +4
Information asymmetry is a pervasive feature of multi-agent systems, especially evident in economics and social sciences. In these settings, agents tailor their actions based on pr…
An Instrumental Value for Data Production and its Application to Data Pricing
Rui Ai, Boxiang Lyu, Zhaoran Wang +2
How much value does a dataset or a data production process have to an agent who wishes to use the data to assist decision-making? This is a fundamental question towards understandi…
Provably Efficient High-Dimensional Bandit Learning with Batched Feedbacks
Jianqing Fan, Zhaoran Wang, Zhuoran Yang +1
We study high-dimensional multi-armed contextual bandits with batched feedback where the steps of online interactions are divided into batches. In specific, each batch coll…
FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning
Xiao-Yang Liu, Ziyi Xia, Jingyang Rui +6
Finance is a particularly difficult playground for deep reinforcement learning. However, establishing high-quality market environments and benchmarks for financial reinforcement le…
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL
Fengzhuo Zhang, Boyi Liu, Kaixin Wang +3
The cooperative Multi-A gent R einforcement Learning (MARL) with permutation invariant agents framework has achieved tremendous empirical successes in real-world applications. Unfo…
Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes
Zuyue Fu, Zhengling Qi, Zhaoran Wang +3
We study the offline reinforcement learning (RL) in the face of unmeasured confounders. Due to the lack of online interaction with the environment, offline RL is facing the followi…
Human-in-the-loop: Provably Efficient Preference-based Reinforcement Learning with General Function Approximation
Xiaoyu Chen, Han Zhong, Zhuoran Yang +2
We study human-in-the-loop reinforcement learning (RL) with trajectory preferences, where instead of receiving a numeric reward at each step, the agent only receives preferences ov…
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets
Yifei Min, Tianhao Wang, Ruitu Xu +3
We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context,…
Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learning
Chenjia Bai, Lingxiao Wang, Zhuoran Yang +4
Offline Reinforcement Learning (RL) aims to learn policies from previously collected datasets without exploring the environment. Directly applying off-policy algorithms to offline…
Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning
Jibang Wu, Zixuan Zhang, Zhe Feng +4
In today's economy, it becomes important for Internet platforms to consider the sequential information design problem to align its long term interest with incentives of the gig ser…
FinRL-Podracer: High Performance and Scalable Deep Reinforcement Learning for Quantitative Finance
Zechu Li, Xiao-Yang Liu, Jiahao Zheng +3
Machine learning techniques are playing more and more important roles in finance market investment. However, finance quantitative modeling with conventional supervised learning app…
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning
Yingjie Fei, Zhuoran Yang, Yudong Chen +1
We study risk-sensitive reinforcement learning (RL) based on the entropic risk measure. Although existing works have established non-asymptotic regret guarantees for this problem,…
Dynamic Bottleneck for Robust Self-Supervised Exploration
Chenjia Bai, Lingxiao Wang, Lei Han +4
Exploration methods based on pseudo-count of transitions or curiosity of dynamics have achieved promising results in solving reinforcement learning with sparse rewards. However, su…
Provably Efficient Generative Adversarial Imitation Learning for Online and Offline Setting with Linear Function Approximation
Zhihan Liu, Yufeng Zhang, Zuyue Fu +2
In generative adversarial imitation learning (GAIL), the agent aims to learn a policy from an expert demonstration so that its performance cannot be discriminated from the expert p…
A Unified Off-Policy Evaluation Approach for General Value Function
Tengyu Xu, Zhuoran Yang, Zhaoran Wang +1
General Value Function (GVF) is a powerful tool to represent both the {\em predictive} and {\em retrospective} knowledge in reinforcement learning (RL). In practice, often multiple…
Towards General Function Approximation in Zero-Sum Markov Games
Baihe Huang, Jason D. Lee, Zhaoran Wang +1
This paper considers two-player zero-sum finite-horizon Markov games with simultaneous moves. The study focuses on the challenging settings where the value function or the model is…
Gap-Dependent Bounds for Two-Player Markov Games
Zehao Dou, Zhuoran Yang, Zhaoran Wang +1
As one of the most popular methods in the field of reinforcement learning, Q-learning has received increasing attention. Recently, there have been more theoretical works on the reg…
Randomized Exploration for Reinforcement Learning with General Value Function Approximation
Haque Ishfaq, Qiwen Cui, Viet Nguyen +5
We propose a model-free reinforcement learning algorithm inspired by the popular randomized least squares value iteration (RLSVI) algorithm as well as the optimism principle. Unlik…
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
Yan Li, Lingxiao Wang, Jiachen Yang +4
Multi-agent reinforcement learning (MARL) becomes more challenging in the presence of more agents, as the capacity of the joint state and action spaces grows exponentially in the n…
Principled Exploration via Optimistic Bootstrapping and Backward Induction
Chenjia Bai, Lingxiao Wang, Lei Han +4
One principled approach for provably efficient exploration is incorporating the upper confidence bound (UCB) into the value function as a bonus. However, UCB is specified to deal w…
Cocktail: Learn a Better Neural Network Controller from Multiple Experts via Adaptive Mixing and Robust Distillation
Yixuan Wang, Chao Huang, Zhilu Wang +3
Neural networks are being increasingly applied to control and decision-making for learning-enabled cyber-physical systems (LE-CPSs). They have shown promising performance without r…
A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum
Prashant Khanduri, Siliang Zeng, Mingyi Hong +3
This paper proposes a new algorithm -- the \underline{S}ingle-timescale Do\underline{u}ble-momentum \underline{St}ochastic \underline{A}pprox\underline{i}matio\underline{n} (SUSTAI…
A Primal-Dual Approach to Constrained Markov Decision Processes
Yi Chen, Jing Dong, Zhaoran Wang
In many operations management problems, we need to make decisions sequentially to minimize the cost while satisfying certain constraints. One modeling approach to study such proble…
On Function Approximation in Reinforcement Learning: Optimism in the Face of Large State Spaces
Zhuoran Yang, Chi Jin, Zhaoran Wang +2
The classical theory of reinforcement learning (RL) has focused on tabular and linear representations of value functions. Further progress hinges on combining RL with modern functi…