Add to ORKGWe consider stochastic approximation algorithms with Markovian dynamics as introduced in Benveniste, Métivier and Priouret in their 1990 book. Using a resetting mechanism with a fairly arbitrary truncation domain, the algorithm is shown to converge to the unique stationary point of the associated ODE with probability one. A self-contained outline to the basic technical aspects of the BMP theory will be also given
We introduce a class of algorithms, called Trajectory Following Dynamic Programming (TFDP) algorithm...
International audienceThis paper is devoted to the convergence analysis of stochastic approximation ...
Stochastic approximation is a framework unifying many random iterative algorithms occurring in a div...
We consider stochastic approximation algorithms with Markovian dynamics introduced by Benveniste, Mé...
IIn this paper, we extend the framework of the convergence ofstochastic approximations. Such a proce...
Recent developments in the area of reinforcement learning have yielded a number of new algorithms fo...
It is shown that the stability of the stochastic approximation algorithm is implied by the asymptoti...
We consider discrete-time xed gain stochastic approxi-mation processes that are dened in terms of a ...
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptot...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
Piecewise deterministic Markov processes (PDMPs) are a class of stochastic processes with applicatio...
Abstract. In this paper we address the problem of the stability of the stochastic approximation proc...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
Another approach to finite differences is the well developed Markov Chain Approximation (MCA) of Kus...
The topic of this thesis is the study of approximation schemes of jump processes whose driving noise...
We introduce a class of algorithms, called Trajectory Following Dynamic Programming (TFDP) algorithm...
International audienceThis paper is devoted to the convergence analysis of stochastic approximation ...
Stochastic approximation is a framework unifying many random iterative algorithms occurring in a div...
We consider stochastic approximation algorithms with Markovian dynamics introduced by Benveniste, Mé...
IIn this paper, we extend the framework of the convergence ofstochastic approximations. Such a proce...
Recent developments in the area of reinforcement learning have yielded a number of new algorithms fo...
It is shown that the stability of the stochastic approximation algorithm is implied by the asymptoti...
We consider discrete-time xed gain stochastic approxi-mation processes that are dened in terms of a ...
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptot...
To sample from distributions in high dimensional spaces or finite large sets di-rectly is not feasib...
Piecewise deterministic Markov processes (PDMPs) are a class of stochastic processes with applicatio...
Abstract. In this paper we address the problem of the stability of the stochastic approximation proc...
Stochastic approximation algorithms are iterative procedures which are used to approximate a target ...
Another approach to finite differences is the well developed Markov Chain Approximation (MCA) of Kus...
The topic of this thesis is the study of approximation schemes of jump processes whose driving noise...
We introduce a class of algorithms, called Trajectory Following Dynamic Programming (TFDP) algorithm...
International audienceThis paper is devoted to the convergence analysis of stochastic approximation ...
Stochastic approximation is a framework unifying many random iterative algorithms occurring in a div...