23 citations · 87 across the 27 of their papers we have counts for
7 papers · 1 filter
Offline Reinforcement Learning with Differentiable Function Approximation is Provably Efficient
Ming Yin, Mengdi Wang, Yu-Xiang Wang
Offline reinforcement learning, which aims at optimizing sequential decision-making strategies with historical data, has been extensively applied in real-life applications. State-O…
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators
Jiachen Li, Edwin Zhang, Ming Yin +3
Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a poli…
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation
Thanh Nguyen-Tang, Ming Yin, Sunil Gupta +2
Sample-efficient offline reinforcement learning (RL) with linear function approximation has recently been studied extensively. Much of prior work has yielded the minimax-optimal bo…
Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks
Kaiqi Zhang, Ming Yin, Yu-Xiang Wang
Quantized neural networks have drawn a lot of attention as they reduce the space and computational complexity during the inference. Moreover, there has been folklore that quantizat…
Offline Stochastic Shortest Path: Learning, Evaluation and Towards Optimality
Ming Yin, Wenjing Chen, Mengdi Wang +1
Goal-oriented Reinforcement Learning, where the agent needs to reach the goal state while simultaneously minimizing the cost, has received significant attention in real-world appli…
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Ming Yin, Yaqi Duan, Mengdi Wang +1
Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. D…
Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost
Dan Qiao, Ming Yin, Ming Min +1
We study the problem of reinforcement learning (RL) with low (policy) switching cost - a problem well-motivated by real-life RL applications in which deployments of new policies ar…