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AMAI
2004
Springer
13 years 10 months ago
Approximate Probabilistic Constraints and Risk-Sensitive Optimization Criteria in Markov Decision Processes
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Dmitri A. Dolgov, Edmund H. Durfee
NIPS
1998
13 years 6 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
INFOCOM
2012
IEEE
11 years 7 months ago
Delay optimal multichannel opportunistic access
Abstract—The problem of minimizing queueing delay of opportunistic access of multiple continuous time Markov channels is considered. A new access policy based on myopic sensing a...
Shiyao Chen, Lang Tong, Qing Zhao
CORR
2011
Springer
183views Education» more  CORR 2011»
13 years 17 hour ago
Mean-Variance Optimization in Markov Decision Processes
We consider finite horizon Markov decision processes under performance measures that involve both the mean and the variance of the cumulative reward. We show that either randomiz...
Shie Mannor, John N. Tsitsiklis
AAAI
1998
13 years 6 months ago
Solving Very Large Weakly Coupled Markov Decision Processes
We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...