Control Applications Using Reinforcement Learning: An Overview

This article presents the general formulation and terminology of reinforcement learning (RL) from the perspective of Bellman’s equations based on a reward function, its learning methods and algorithms. The important key in RL is the calculation of value-state and value state-action functions, useful to find, compare and improve policies for learning agent through different methods based on values and policies such as Q-learning. The deep deterministic policy gradient (DDPG) learning algorithm based on an actor-critic structure is also described as one of the ways of training the RL agent. RL algorithms can be used to design closed loop controllers. Through simulation, using the DDPG algorithm, an example of the application of the inverted pendulum is proposed in simulation, demonstrating that the training is carried out in a reasonable time, showing the role and importance of RL algorithms, like tools that combined with control can address this type of problems.