9 citations · 17 across the 6 of their papers we have counts for
6 papers
On State Variables, Bandit Problems and POMDPs
Warren B Powell
State variables are easily the most subtle dimension of sequential decision problems. This is especially true in the context of active learning problems (bandit problems") where de…
Risk Directed Importance Sampling in Stochastic Dual Dynamic Programming with Hidden Markov Models for Grid Level Energy Storage
Joseph L. Durante, Juliana Nascimento, Warren B. Powell
Power systems that need to integrate renewables at a large scale must account for the high levels of uncertainty introduced by these power sources. This can be accomplished with a…
Reinforcement Learning via Parametric Cost Function Approximation for Multistage Stochastic Programming
Saeed Ghadimi, Raymond T. Perkins, Warren B. Powell
The most common approaches for solving stochastic resource allocation problems in the research literature is to either use value functions ("dynamic programming") or scenario trees…
From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions
Warren B Powell
There are over 15 distinct communities that work in the general area of sequential decisions and information, often referred to as decisions under uncertainty or stochastic optimiz…
Optimal Learning for Stochastic Optimization with Nonlinear Parametric Belief Models
Xinyu He, Warren B. Powell
We consider the problem of estimating the expected value of information (the knowledge gradient) for Bayesian learning problems where the belief model is nonlinear in the parameter…
Stochastic Search with an Observable State Variable
Lauren A. Hannah, Warren B. Powell, David M. Blei
In this paper we study convex stochastic search problems where a noisy objective function value is observed after a decision is made. There are many stochastic search problems whos…