A New Learning Automaton for Selecting an Arbitrary Subset of Actions

As a powerful reinforcement learning method, the learning automaton (LA) has been studied, analyzed, and applied to various engineering systems for decades. However, the state-of-the-art LA-based methods can select only the optimal action or optimal subset and cannot select an arbitrary target subset like selecting the best and worst actions or the ones in a given rank range. In order to solve the problem of selecting a given arbitrary subset of actions, this work proposes a novel pursuit learning scheme, called a discretized equal pursuit reward-inaction algorithm for arbitrary subset selection (
${\text {DEP}_{RI}}$
-AS). The proposed scheme pursues the currently estimated arbitrary action subset and makes the probabilities of selecting each action in the subset equal, so as to increase the convergence speed. The proof of its
$\epsilon $
-optimality property is presented. Simulation results of comparison experiments, parameter analysis, and a real-world application demonstrate its power in selecting a given subset of user-desired actions.