Add to ORKGThis paper considers the problem of stochastic robustness testing for plans. As many authors have ob...
We consider large-scale Markov decision processes (MDPs) with parameter un-certainty, under the robu...
Optimal solutions to Markov decision problems may be very sensitive with respect to the state transi...
Environment models are not always known a priori, and approximating stochastic transition dynamics m...
Markov decision processes (MDP) is a standard modeling tool for sequential decision making in a dyna...
Stochastic Shortest Path problems (SSPs), a sub-class of Markov Decision Problems (MDPs), can be eff...
Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be effi...
Chapter 22International audienceWe review a class of online planning algorithms for deterministic an...
Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be effi...
It is fair to say that in many real world decision problems the underlying models cannot be accurate...
We review a class of online planning algorithms for deterministic and stochastic optimal control pro...
A framework capable of computing optimal control policies for a continuous system in the presence of...
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usua...
Abstract — This paper presents a new robust decision-making algorithm that accounts for model uncert...
In this paper, we seek robust policies for uncertain Markov Decision Processes (MDPs). Most robust o...
This paper considers the problem of stochastic robustness testing for plans. As many authors have ob...
We consider large-scale Markov decision processes (MDPs) with parameter un-certainty, under the robu...
Optimal solutions to Markov decision problems may be very sensitive with respect to the state transi...
Environment models are not always known a priori, and approximating stochastic transition dynamics m...
Markov decision processes (MDP) is a standard modeling tool for sequential decision making in a dyna...
Stochastic Shortest Path problems (SSPs), a sub-class of Markov Decision Problems (MDPs), can be eff...
Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be effi...
Chapter 22International audienceWe review a class of online planning algorithms for deterministic an...
Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be effi...
It is fair to say that in many real world decision problems the underlying models cannot be accurate...
We review a class of online planning algorithms for deterministic and stochastic optimal control pro...
A framework capable of computing optimal control policies for a continuous system in the presence of...
Model-Based Reinforcement Learning (MBRL) algorithms solve sequential decision-making problems, usua...
Abstract — This paper presents a new robust decision-making algorithm that accounts for model uncert...
In this paper, we seek robust policies for uncertain Markov Decision Processes (MDPs). Most robust o...
This paper considers the problem of stochastic robustness testing for plans. As many authors have ob...
We consider large-scale Markov decision processes (MDPs) with parameter un-certainty, under the robu...
Optimal solutions to Markov decision problems may be very sensitive with respect to the state transi...