Smart Insight

Today's post will introduce you to the model-free methods of Reinforcement Learning (RL). To have a model of the environment we need to store all the states and actions. To do so, we are limited to the infrastructure limits, that means we need to find another approach when the environment is too big and with too many variables. Therefore, model-free approaches can handle the problems where the world is too big to fit our infrastructures. To better comprehension, the Q-Learning algorithm will be presented and explained. Robot infinite environment - Photo by Dominik Scythe on Unsplash Terminologies Figure 1 - Agent-environment interaction Agent  — The learner and the one that makes actions. The agent's goal is to maximise the cumulative reward across a set of actions and states. Action  — A set of actions which the agent can perform. Different environments allow the agent to perform distinct kinds of actions. The set of all valid actions in a given environment is usu...

Today's post is about a Machine Learning area,  the Reinforcement Learning (RL). This article seeks to summarise the principal types of algorithms used for reinforcement learning. Here we will get an overview of the existing RL methods on an intuitive level. In further posts, we will go into more detail and code examples. Robot - Photo by Photos Hobby on Unsplash As other Artificial Intelligence's approaches, RL is not a new thing. The first studies and developments dating back to the 1850s and further advances on mid-1950s, where Richard Bellman has a huge impact [1]. Now, we are achieving several advances in the area and improving the results year after year. Reinforcement learning is nowadays the most virtuous way to suggest or find the machine’s creativity. Please note, different from human beings, theses algorithms can fetch experience from millions of parallel simulations if they are running on a powerful infrastructure. Terminologies Figure 1 - Agent-environment...