Add to ORKGInternational audienceMarkov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision problems under uncertainty as well as Reinforcement Learning problems. Written by experts in the field, this book provides a global view of current research using MDPs in Artificial Intelligence. It starts with an introductory presentation of the fundamental aspects of MDPs (planning in MDPs, Reinforcement Learning, Partially Observable MDPs, Markov games and the use of non-classical criteria). Then it presents more advanced research trends in the domain and gives some concrete examples using illustrative applications
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a k...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
International audienceMarkov Decision Processes (MDPs) are a mathematical framework for modeling seq...
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision probl...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
The Markov Decision Process (MDP) is the principal theoretical formalism in the area of Reinforcemen...
Markov decision processes (MDPs) are models of dynamic decision making under uncertainty. These mode...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...
In control systems theory, the Markov decision process (MDP) is a widely used optimization model inv...
It is over 30 years ago since D.J. White started his series of surveys on practical applications of ...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
We provide a tutorial on the construction and evalua-tion of Markov decision processes (MDPs), which...
We provide a tutorial on the construction and evalua-tion of Markov decision processes (MDPs), which...
This thesis is about chance and choice, or decisions under uncertainty. The desire for creating an ...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a k...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...
International audienceMarkov Decision Processes (MDPs) are a mathematical framework for modeling seq...
Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential decision probl...
A short tutorial introduction is given to Markov decision processes (MDP), including the latest acti...
The Markov Decision Process (MDP) is the principal theoretical formalism in the area of Reinforcemen...
Markov decision processes (MDPs) are models of dynamic decision making under uncertainty. These mode...
This chapter presents an overview of simulation-based techniques useful for solving Markov decision ...
In control systems theory, the Markov decision process (MDP) is a widely used optimization model inv...
It is over 30 years ago since D.J. White started his series of surveys on practical applications of ...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
We provide a tutorial on the construction and evalua-tion of Markov decision processes (MDPs), which...
We provide a tutorial on the construction and evalua-tion of Markov decision processes (MDPs), which...
This thesis is about chance and choice, or decisions under uncertainty. The desire for creating an ...
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observation...
Sequential decision making, commonly formalized as optimization of a Markov Decision Process, is a k...
Real-world planning problems frequently involve mixtures of continuous and discrete state variables ...