1 citations · 3 across the 3 of their papers we have counts for
4 papers
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…
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…
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…