27 citations · 78 across the 13 of their papers we have counts for
13 papers
Top-Two Thompson Sampling for Contextual Top-mc Selection Problems
Xinbo Shi, Yijie Peng, Gongbo Zhang
We aim to efficiently allocate a fixed simulation budget to identify the top-mc designs for each context among a finite number of contexts. The performance of each design under a c…
Efficient Learning for Selecting Top-m Context-Dependent Designs
Gongbo Zhang, Sihua Chen, Kuihua Huang +1
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we fo…
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient Algorithms
Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative reward. In this work, we consider the RL setting where the goal is to optimize the quantile of the cu…
An Efficient Dynamic Sampling Policy For Monte Carlo Tree Search
Gongbo Zhang, Yijie Peng, Yilong Xu
We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decis…
Quantile-Based Policy Optimization for Reinforcement Learning
Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards. In this work, we consider the RL setting where the goal is to optimize the quantile of the c…
Efficient Distributed Learning in Stochastic Non-cooperative Games without Information Exchange
Haidong Li, Anzhi Sheng, Yijie Peng +1
In this work, we study stochastic non-cooperative games, where only noisy black-box function evaluations are available to estimate the cost function for each player. Since each pla…
Asymptotically Optimal Sampling Policy for Selecting Top-m Alternatives
Gongbo Zhang, Yijie Peng, Jianghua Zhang +1
We consider selecting the top- alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a…
Efficient Sampling Policy for Selecting a Good Enough Subset
Gongbo Zhang, Bin Chen, Qing-shan Jia +1
The note studies the problem of selecting a good enough subset out of a finite number of alternatives under a fixed simulation budget. Our work aims to maximize the posterior proba…
Context-dependent Ranking and Selection under a Bayesian Framework
Haidong Li, Henry Lam, Zhe Liang +1
We consider a context-dependent ranking and selection problem. The best design is not universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic sampli…
Efficient Learning for Clustering and Optimizing Context-Dependent Designs
Haidong Li, Henry Lam, Yijie Peng
We consider a simulation optimization problem for a context-dependent decision-making. A Gaussian mixture model is proposed to capture the performance clustering phenomena of conte…
Efficient Sampling for Selecting Important Nodes in Random Network
Haidong Li, Xiaoyun Xu, Yijie Peng +1
We consider the problem of selecting important nodes in a random network, where the nodes connect to each other randomly with certain transition probabilities. The node importance…
Optimal Unbiased Estimation for Expected Cumulative Cost
Zhenyu Cui, Michael C. Fu, Yijie Peng +1
We consider estimating an expected infinite-horizon cumulative discounted cost/reward contingent on an underlying stochastic process by Monte Carlo simulation. An unbiased estimato…
Ranking and Selection as Stochastic Control
Yijie Peng, Edwin K. P. Chong, Chun-Hung Chen +1
Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the a…