Opposition-based discrete action reinforcement learning automata algorithm case study: Optimal design of a PID controller

Abstract

In this paper, the discrete action reinforcement learning automata (DARLA) method is expressed. The performance of the reinforcement learning algorithm is improved using the opposite concepts. This is an automatic method that can nd the global optima without any knowledge about the parameters of the research space. To nd the global optimal point, the interval that contains the optima is determined by DARLA as the cost function is minimized. In the opposition-based DARLA method, learning is performed based on opposition. The main idea in the opposition is to consider the search direction and its opposite at the same time to reach the candidate solution. This concept has increased the convergence rate and accuracy, and this algorithm can be used for many real-time applications. To prove this, the opposition-based DARLA is proposed to design a proportional-integral-derivative (PID) controller for the automatic voltage regulator system. The experimental results for the optimizing PID controller problem demonstrate the superior performance of the proposed approach.