Last time, I showcased some basics of Evolutionary Algorithms, which can elegantly solve black box problems. Today I extend it to train agents using reinforcement learning and neural networks. If you want to check it out for yourself, feel free to look at the source code. Reinforcement learning is a branch of machine learning where agents aim to learn some behavior that maximizes the reward obtained in some environment by taking the right actions given a particular state.

Many problems are difficult to solve using classic algorithms. This is where state of the art artificial intelligence techniques come into play. While everyone talks about large language models and diffusers nowadays, I think a different branch called evolutionary algorithms is much more interesting. Originally inspired by biological evolution, this branch of artificial intelligence aims to solve problems using elegant concepts. While many types of evolutionary algorithms exist, they almost all share the notion of using a population to converge to an optimal solution.