Augmented-Random-Search-with-Python-OpenAI-and-Gym

Here is an implementation of a new type of artificial intelligence, which is almost as powerful as the algorithm used by Google Deep Mind to train an AI to walk and run through an environment! The name of this technique is Augmented Random Search, it was created in 2018 and is on average 15 times faster than traditional algorithms! This algorithm is within the Reinforcement Learning area, which is a type of learning used in multi-agent systems in which agents must interact in the environment and learn on their own, earning positive rewards when they perform correct actions and negative rewards when they perform actions. that don't lead to the goal. Artificial intelligence learns without any prior knowledge, adapting to the environment and finding solutions alone! The application consists of training a simulation of a robot that needs to learn to walk in an environment. We will use the ARS (Augmented Random Search) technique, Python as a programming language and the OpenAI Gym as a simulation environment.