A class of Markov decision processes is considered with a finite state and action space and with an incompletely known transition mechanism. The controller is looking for a strategy maximizing the Bayesian expected total discounted return. In section 2 approximations are given for this value and in section 3 we indicate how to compute the value for a fixed prior distribution
Given a Markov chain with uncertain transi-tion probabilities modelled in a Bayesian way, we investi...
This paper deals with a continuous-time Markov decision process M, with Borel state and action space...
International audienceThis paper deals with a continuous-time Markov decision process M, with Borel ...
summary:We consider a class of discrete-time Markov control processes with Borel state and action sp...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
In this work, we deal with a discrete-time infinite horizon Markov decision process with locally com...
In this work, we deal with a discrete-time infinite horizon Markov decision process with locally com...
We consider a discrete-time Markov decision process with Borel state and action spaces, and possibly...
This work addresses the problem of estimating the optimal value function in a Markov Decision Proces...
We consider the problem of "optimal learning" for Markov decision processes with uncertain...
In this paper we consider some problems and results in the field of Markov decision processes with a...
In this paper, we propose an approach for approximating the value function and an ϵ-optimal policy o...
International audienceWe consider a discrete-time Markov decision process with Borel state and actio...
International audienceThis paper deals with a continuous-time Markov decision process M, with Borel ...
Given a Markov chain with uncertain transi-tion probabilities modelled in a Bayesian way, we investi...
This paper deals with a continuous-time Markov decision process M, with Borel state and action space...
International audienceThis paper deals with a continuous-time Markov decision process M, with Borel ...
summary:We consider a class of discrete-time Markov control processes with Borel state and action sp...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
Due to copyright restrictions, the access to the full text of this article is only available via sub...
In this work, we deal with a discrete-time infinite horizon Markov decision process with locally com...
In this work, we deal with a discrete-time infinite horizon Markov decision process with locally com...
We consider a discrete-time Markov decision process with Borel state and action spaces, and possibly...
This work addresses the problem of estimating the optimal value function in a Markov Decision Proces...
We consider the problem of "optimal learning" for Markov decision processes with uncertain...
In this paper we consider some problems and results in the field of Markov decision processes with a...
In this paper, we propose an approach for approximating the value function and an ϵ-optimal policy o...
International audienceWe consider a discrete-time Markov decision process with Borel state and actio...
International audienceThis paper deals with a continuous-time Markov decision process M, with Borel ...
Given a Markov chain with uncertain transi-tion probabilities modelled in a Bayesian way, we investi...
This paper deals with a continuous-time Markov decision process M, with Borel state and action space...
International audienceThis paper deals with a continuous-time Markov decision process M, with Borel ...
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