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 [23], 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 Lanctôt [16] extended the Pursuit Algorithm into the discretized world by presenting the Discretized Pursuit Algorithm, based on a Reward-Inaction learning philosophy. In this paper, we argue that the Reward-Penalty and RewardInaction 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 longterm and short-term perspectives of the Environment. In this paper, we present all the four resultant Pursuit algorithms, and also present a quantitative comparison between them. Although the present comparison is solely based on rigorous experimental results, we are currently investigating a formal convergence analysis of the various schemes.