Bandits are one of the most basic examples of decision-making with uncertainty. A Markovian restless bandit can be seen as the following sequential allocation problem: At each decision epoch, one or several arms are activated (pulled); all arms generate an instantaneous reward that depend on their state and their activation; the state of each arm then changes in a Markovian fashion, based on an underlying transition matrix. Both the rewards and the probability matrices are known, and the new state is revealed to the decision maker for its next decision. The word restless serves to emphasize the fact that arms that are not activated can also change states, hence generalizes the simpler rested bandits. In principle, the above problem can be s...
The manuscript is divided in two parts. The first consists in Chapters I to IV and offers a unified ...
Over the past two decades, electric utilities operate their power systems at full power and often cl...
A fundamental problem in automatic control is the control of uncertain plants in the presence of inp...
What will be tomorrow’s big cities objectives and challenges? Most of the operational problems from ...
Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-...
The impressive breakthroughs of the last two decades in the field of machine learning can be in larg...
With rapid development of mathematical models and simulation tools, the need of uncertainty quantifi...
Streaming applications are responsible for the majority of the computation load in many embedded sys...
Here we present, for the first time, a frequentist progressive Multiple Sequence Alignment (MSA) met...
In real-world logistic operations there are a lot of situations that can be exploited to get better ...
We study the link between Backward SDEs and some stochastic optimal control problems and their appli...
Since the eighties localization and mapping problems have attracted the efforts of robotics research...
L'immense potentiel des approches d'apprentissage par renforcement profond (ARP) pour la conception ...
By using Reinforcement Learning (RL), an autonomous agent interacting with the environment can learn...
Automated treatment surface facilities, which employ computer-controlled hoists for part transportat...
The manuscript is divided in two parts. The first consists in Chapters I to IV and offers a unified ...
Over the past two decades, electric utilities operate their power systems at full power and often cl...
A fundamental problem in automatic control is the control of uncertain plants in the presence of inp...
What will be tomorrow’s big cities objectives and challenges? Most of the operational problems from ...
Une approche classique pour traiter les problèmes d’optimisation avec incertitude à deux- et multi-...
The impressive breakthroughs of the last two decades in the field of machine learning can be in larg...
With rapid development of mathematical models and simulation tools, the need of uncertainty quantifi...
Streaming applications are responsible for the majority of the computation load in many embedded sys...
Here we present, for the first time, a frequentist progressive Multiple Sequence Alignment (MSA) met...
In real-world logistic operations there are a lot of situations that can be exploited to get better ...
We study the link between Backward SDEs and some stochastic optimal control problems and their appli...
Since the eighties localization and mapping problems have attracted the efforts of robotics research...
L'immense potentiel des approches d'apprentissage par renforcement profond (ARP) pour la conception ...
By using Reinforcement Learning (RL), an autonomous agent interacting with the environment can learn...
Automated treatment surface facilities, which employ computer-controlled hoists for part transportat...
The manuscript is divided in two parts. The first consists in Chapters I to IV and offers a unified ...
Over the past two decades, electric utilities operate their power systems at full power and often cl...
A fundamental problem in automatic control is the control of uncertain plants in the presence of inp...