Add to ORKGWeak convergence methods are used to analyse generalized learning automata algorithms. The REINFORCE algorithm has been analysed. It is shown by an example that this algorithm can exhibit unbounded behaviour. A modification based on constrained optimization principles is proposed to overcome this problem. The relationship between the asymptotic behaviour of the modified algorithm and the Kuhn-Tucker points of the related constrained optimization problem is brought out
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
The fastest learning automata (LA) algorithms currently available fall in the family of estimator al...
Weak convergence methods are used to analyse generalized learning automata algorithms. The REINFORCE...
Analyzes the long-term behavior of the REINFORCE and related algorithms (Williams, 1986, 1988, 1992)...
Both stochastic learning automata and genetic algorithms have previously been shown to have valuable...
Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuabl...
This paper analyses the behaviour of a general class of learning automata algorithms for feedforward...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
A feedforward network composed of units of teams of parameterized learning automata is considered as...
A feedforward network composed of units of teams of parametrised learning autmata is considered as a...
A Learning Automaton is an automaton that interacts with a random environment, having as its goal th...
A model made of units of teams of learning automata is developed for the three layer pattern classif...
Automata models of learning systems introduced in the 1960s were popularized as learning automata (L...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
The fastest learning automata (LA) algorithms currently available fall in the family of estimator al...
Weak convergence methods are used to analyse generalized learning automata algorithms. The REINFORCE...
Analyzes the long-term behavior of the REINFORCE and related algorithms (Williams, 1986, 1988, 1992)...
Both stochastic learning automata and genetic algorithms have previously been shown to have valuable...
Stochastic learning automata and genetic algorithms (GAs) have previously been shown to have valuabl...
This paper analyses the behaviour of a general class of learning automata algorithms for feedforward...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
Learning algorithms for feedforward connectionist systems in a reinforcement learning environment ar...
A feedforward network composed of units of teams of parameterized learning automata is considered as...
A feedforward network composed of units of teams of parametrised learning autmata is considered as a...
A Learning Automaton is an automaton that interacts with a random environment, having as its goal th...
A model made of units of teams of learning automata is developed for the three layer pattern classif...
Automata models of learning systems introduced in the 1960s were popularized as learning automata (L...
The problem of optimization with noisy measurements is common in many areas of engineering. The only...
Stochastic automata operating in an unknown random environment have been proposed earlier as models ...
The fastest learning automata (LA) algorithms currently available fall in the family of estimator al...