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01/01/2022
The Parametric Cost Function Approximation: A new approach for multistage stochastic programming
The most common approaches for solving multistage stochastic programming...
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04/11/2020
Optimal Learning for Sequential Decisions in Laboratory Experimentation
The process of discovery in the physical, biological and medical science...
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02/14/2020
On State Variables, Bandit Problems and POMDPs
State variables are easily the most subtle dimension of sequential decis...
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12/19/2019
Zeroth-order Stochastic Compositional Algorithms for Risk-Aware Learning
We present Free-MESSAGEp, the first zeroth-order algorithm for convex me...
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12/07/2019
From Reinforcement Learning to Optimal Control: A unified framework for sequential decisions
There are over 15 distinct communities that work in the general area of ...
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10/18/2018
Approximate Dynamic Programming for Planning a Ride-Sharing System using Autonomous Fleets of Electric Vehicles
Within a decade, almost every major auto company, along with fleet opera...
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04/02/2018
Recursive Optimization of Convex Risk Measures: Mean-Semideviation Models
We develop and analyze stochastic subgradient methods for optimizing a n...
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04/20/2017
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds
Monte Carlo Tree Search (MCTS), most famously used in game-play artifici...
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09/07/2015
Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures
In this paper, we consider a finite-horizon Markov decision process (MDP...
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08/06/2015
A Knowledge Gradient Policy for Sequencing Experiments to Identify the Structure of RNA Molecules Using a Sparse Additive Belief Model
We present a sparse knowledge gradient (SpKG) algorithm for adaptively s...
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07/10/2014
A New Optimal Stepsize For Approximate Dynamic Programming
Approximate dynamic programming (ADP) has proven itself in a wide range ...
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09/28/2009