Reinforcement Learning Algorithms with Python

The core of EAs

EAs are inspired by biological evolution and implement techniques and mechanisms that simulate biological evolution. This means that EAs go through many trials to create a population of new candidate solutions. These solutions are also called individuals (in RL problems, a candidate solution is a policy) that are better than the previous generation, in a similar way to the process within nature wherein only the strongest survive and have the possibility to procreate.

One of the advantages of EAs is that they are derivative-free methods, meaning that they don't use the derivative to find the solution. This allows EAs to work very well with all sorts of differentiable and non-differentiable functions, including deep neural ...