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Learning automata in feedforward connectionist systems

Phansalkar, VV and Thathachar, MAL (1996) Learning automata in feedforward connectionist systems. In: International Journal of Systems Science, 27 (2). pp. 145-150.

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

Item Type: Journal Article
Publication: International Journal of Systems Science
Publisher: Taylor and Francis Group
Additional Information: Copyright of this article belongs to Taylor and Francis Group.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 29 Apr 2011 08:13
Last Modified: 29 Apr 2011 08:13
URI: http://eprints.iisc.ac.in/id/eprint/37334

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