A comparison of continuous and discretized pursuit learning schemes

A learning automaton is an automaton that interacts with a random environment, having as its goal the task of learning the optimal action based on its acquired experience. Many learning automata have been proposed, with the class of estimator algorithms being among the fastest ones. Thathachar and Sastry (1986), through the pursuit algorithm, introduced the concept of learning algorithms that pursue the current optimal action, following a reward-penalty learning philosophy. Later, Oommen and Lanctot 1990) extended the pursuit algorithm into the discretized world by presenting the discretized pursuit algorithm, based on a reward-inaction learning philosophy. We argue that the reward-penalty and reward-inaction learning paradigms in conjunction with the continuous and discrete models of computation lead to four versions of pursuit learning automata. We contend that a scheme that merges the pursuit concept with the most recent response of the environment permits the algorithm to utilize the LA´s long-term and short-term perspectives of the environment. We present all the four resultant pursuit algorithms, and also present a quantitative comparison between them