Robot Control System Using Deep Reinforcement Learning

Robots are now an integral part of our living environments. In the industrial field, they represent a valid aid by replacing workers in heavy duty tasks. The task of a robot control system is to execute a planned sequence of movements and to identify an alternative path in the presence of obstacles. Neural networks are exceptionally effective at getting good characteristics highly structured data. We could, then, represent our Q function with a neural network, which takes the status and action as input and outputs for the corresponding Q value. Deep reinforcement learning methods use deep neural networks to approximate any of the following reinforcement learning components: value function, policy, and model. In this chapter, you will learn how to use deep reinforcement learning methods to control robot movements in a specific...