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Learning automata with changing number of actions

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2 Author(s)
Thathachar, M.A.L. ; Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India ; Harita, B.R.

A reinforcement scheme that is based on the linear reward-inaction updating algorithm is presented for a learning automaton whose action set changes from instant to instant. A learning automaton using the algorithm is shown to be both absolutely expedient and ε-optimal. The simulation results verify the ε-optimality of the algorithm. The results can be extended to the design of general nonlinear absolutely expedient learning algorithms.

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Systems, Man and Cybernetics, IEEE Transactions on  (Volume:17 ,  Issue: 6 )