activity
20162022
most citedFrom Reinforcement Learning to Optimal Control: A unified framework for sequential decisions

9 citations · 37 across the 11 of their papers we have counts for

collaborators

15 papers

math.OC20221 cited

Stochastic Search for a Parametric Cost Function Approximation: Energy storage with rolling forecasts

Saeed Ghadimi, Warren B. Powell

Rolling forecasts have been almost overlooked in the renewable energy storage literature. In this paper, we provide a new approach for handling uncertainty not just in the accuracy…

cs.AI20211 cited

Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic

Lawrence Thul, Warren Powell

The pandemic caused by the SARS-CoV-2 virus has exposed many flaws in the decision-making strategies used to distribute resources to combat global health crises. In this paper, we…

cs.LG20204 cited

Optimal Learning for Sequential Decisions in Laboratory Experimentation

Kristopher Reyes, Warren B Powell

The process of discovery in the physical, biological and medical sciences can be painstakingly slow. Most experiments fail, and the time from initiation of research until a new adv…

cs.LG20202 cited

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…

math.OC20201 cited

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…

math.OC20205 cited

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…