Two Approaches on Accelerating Bayesian Two Action Learning Automata

Bayesian Learning Automata (BLA) are demonstrated to be as efficient as the state-of-the-art automaton in two action environments, and it has parameter-free property. However, BLA need the explicit computation of a beta inequality, which is time-consuming, to judge its convergence. In this paper, the running time of BLA is concerned and two approaches are proposed to accelerate the computation of the beta inequality. One takes advantage of recurrence relation of the beta inequality, the other uses a normal distributions to approximate the beta distributions. Numeric simulation are performed to verify the effectiveness and efficiency of those two approaches. The results shows these two approaches reduce the running time substantially.