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NIPS
1998
13 years 10 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
NIPS
1996
13 years 9 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
JDCTA
2010
146views more  JDCTA 2010»
13 years 3 months ago
Modelling for Cruise Two-Dimensional Online Revenue Management System
To solve the cruise two-dimensional revenue management problem and develop such an automated system under uncertain environment, a static model which is a stochastic integer progr...
Bingzhou Li
IFM
2010
Springer
190views Formal Methods» more  IFM 2010»
13 years 7 months ago
On Model Checking Techniques for Randomized Distributed Systems
Abstract. The automata-based model checking approach for randomized distributed systems relies on an operational interleaving semantics of the system by means of a Markov decision ...
Christel Baier
CPAIOR
2009
Springer
14 years 3 months ago
Optimal Interdiction of Unreactive Markovian Evaders
The interdiction problem arises in a variety of areas including military logistics, infectious disease control, and counter-terrorism. In the typical formulation of network interdi...
Alexander Gutfraind, Aric A. Hagberg, Feng Pan