Q-learning automaton

Reinforcement learning is the problem faced by a controller that must learn behavior through trial and error interactions with a dynamic environment. The controller´s goal is to maximize reward over time, by producing an effective mapping of states of actions called policy. To construct the model of such systems, we present a generalized learning automaton approach with Q-learning behaviors. Compared to Q-learning, the computational experiments of the pursuit problems show that the proposed reinforcement scheme obtains better results in terms of convergence speed and memory size.