19 citations · 49 across the 8 of their papers we have counts for
8 papers
Offline Reinforcement Learning for Human-Guided Human-Machine Interaction with Private Information
Zuyue Fu, Zhengling Qi, Zhuoran Yang +2
Motivated by the human-machine interaction such as training chatbots for improving customer satisfaction, we study human-guided human-machine interaction involving private informat…
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…
False Correlation Reduction for Offline Reinforcement Learning
Zhihong Deng, Zuyue Fu, Lingxiao Wang +5
Offline reinforcement learning (RL) harnesses the power of massive datasets for resolving sequential decision problems. Most existing papers only discuss defending against out-of-d…
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…
Instrumental Variable Value Iteration for Causal Offline Reinforcement Learning
Luofeng Liao, Zuyue Fu, Zhuoran Yang +3
In offline reinforcement learning (RL) an optimal policy is learned solely from a priori collected observational data. However, in observational data, actions are often confounded…
Single-Timescale Actor-Critic Provably Finds Globally Optimal Policy
Zuyue Fu, Zhuoran Yang, Zhaoran Wang
We study the global convergence and global optimality of actor-critic, one of the most popular families of reinforcement learning algorithms. While most existing works on actor-cri…
Actor-Critic Provably Finds Nash Equilibria of Linear-Quadratic Mean-Field Games
Zuyue Fu, Zhuoran Yang, Yongxin Chen +1
We study discrete-time mean-field Markov games with infinite numbers of agents where each agent aims to minimize its ergodic cost. We consider the setting where the agents have ide…
Sample Elicitation
Jiaheng Wei, Zuyue Fu, Yang Liu +3
It is important to collect credible training samples for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distributi…