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A universal supervisor controller and ER suite for Webots that can be adapted to any wheeled robot morphology with ease. The project is also setup to allow for easy Reinforcement Learning experimentation with some select algorithms (CMA-ES, Novlty Search, MAP-Elites) and neural networks (fixed and recurrent).
Using Genetic programming, to evolve stategies that preditors and preys will use in a simulated environment, such that Preditors get better at catching preys over time (generations).