Cambridge University Press23d
Discounted cost exponential semi-Markov decision processes with unbounded transition rates: a service rate control problem with impatient customers
end{equation} The value iteration procedure is very useful in the applications of Markov decision processes. It can be used to compute the optimal value functions. It can also be used to prove ...
blog.faculty.london.edu10d
Dynamic Stochastic Matching Under Limited Time
Our approach involves the development of a host of new techniques, including linear programming benchmarks, value function approximations, and proxies for continuous-time Markov chains, which may be ...
Frontiers25d
Decentralized multi-agent reinforcement learning based on best-response policies
Introduction: Multi-agent systems are an interdisciplinary research field that describes the concept of multiple decisive individuals interacting with a usually partially observable environment ...
Georgia Tech News Center5d
Online Master of Science in Analytics - Curriculum
The practicum is 10-15 weeks depending on the semester and the process starts several months in advance. Our OMS Analytics curriculum grid breaks down the different types of courses and concentrations ...
University of Oslo15d
IN-STK5100 – Reinforcement Learning and Decision Making Under Uncertainty
The aim of the course is two-fold. Firstly, to give a thorough understanding of statistical decision making, Markov decision processes, and the relation of statistical decision making to human ...
www.cs.utexas.edu25d
TEXPLORE: Real-Time Sample-Efficient Reinforcement Learning for Robots
TEXPLORE: Real-Time Sample-Efficient Reinforcement Learning for Robots. Todd Hester and Peter Stone. Machine Learning, 90(3):385–429, 2013.