LEARNING AUTOMATA IN FEEDFORWARD CONNECTIONIST SYSTEMS

This paper analyses the behaviour of a general class of learning autom ata algorithms for feedforward connectionist systems in an associative reinforcement learning environment. The type of connectionist system considered is also fairly general. The associative reinforcement learn ing task is first posed as a constrained maximization problem. The alg orithm is approximated hy an ordinary differential equation using weak convergence techniques. The equilibrium points of the ordinary differ ential equation are then compared with the solutions to the constraine d maximization problem to show that the algorithm does behave as desir ed.