Neuroevolution through Augmenting Topologies

Several approaches using deep reinforcement learning to play Super Mario Bros.

contains code related to all machine learning models being studied.

Not the typical snake game. The snake no longer needs you - it grows on its own (neuro-evolution at its best)

Which dynamical regime is beneficial for biological systems in the context of the criticality hypothesis? Agent-based evolutionary foraging game with experiments to evaluate generalizability, ability to perform complex tasks and evolvability of agents with respect to their dynamical regime. Paper: https://arxiv.org/abs/2103.12184

The project aims to teach a neural net how to play the famous game 'Flappy Bird'. To play the game deep learning and genetic algorithms are applied.

sNEAT: A simplified implementation of NEAT (Neuro-Evolution of Augmenting Topologies) in Python

Various studies show that criticality is an attractor in biological evolution. Which conditions have to be fulfilled, such that criticality acts as an attractor in our neuroevolution simulation? -- Masters Thesis Project ---

Developing an Intelligent Agent from scratch to play a game with Applying Neuroevolution to achieve high scores

"Neuro Evolution of Augmenting Topologies"

Implemented an intelligent game agent using an evolutionary algorithm to train a neural network.

Paper: https://doi.org/10.1162/isal_a_00412 Which dynamical regime is beneficial for biological systems? Agent-based evolutionary foraging game with experiments to evaluate generalizability, ability to perform complex tasks and evolvability.

Machine learning model that learns to play Flappy bird game developed using Neural networks and Genetic algorithm(NEAT).

neuro-evolution applied to the game of rock paper scissors

Genetic algorithm (NEAT) to generate and optimize deep neural networks for the game "Flappy Bird".

🦖 A fast & simple neuro-evolution library for Python