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neat-braitenberg-vrep's Introduction

neat-braitenberg-vrep

Evolution of a Braitenberg like vehicle using NEAT and VREP.

Project

The goal of the project is to showcase how to evolve Braitenberg like agents in parallel that could navigate within a specific environment without hitting obstacles using neuroevolution, particularly neat-python. The algorithm maintains a population of neural networks which is subjected to natural selection and mutation. In this work V-REP robotic simulator is used to create and simulate the environment. It exposes a remote API that allows controlling the simulation from the external client-side application - robot.py robot controll module.

neat-python library implements parallel.ParallelEvaluator however it had to be extended in order to be able use it properly with V-REP. Each worker has to connect to an appropriate V-REP instance.

How To Run?

Install V-REP and change the vrep_abspath in neuroevolution.py vrep absolute path to V-REP accordingly. Optional, configure the neat algorithm in config.ini and evolutionary params in settings.py.

# install dependencies
pipenv install

# run neuroevolution
pipenv run python neuroevolution.py


Neuroevolutionary program started!

 ****** Running generation 0 ******

thread_id = Worker Thread #0 client_id = 0
thread_id = Worker Thread #1 client_id = 1
thread_id = Worker Thread #2 client_id = 2
thread_id = Worker Thread #3 client_id = 3
Worker Thread #0 genome_id: 1 fitness: 53.822867
Worker Thread #3 genome_id: 4 fitness: 35.223197
Worker Thread #1 genome_id: 2 fitness: 12.990576
Worker Thread #2 genome_id: 3 fitness: 53.216213
...

alt text

NOTE: Don't use VREP for neuroevoltion for simple applications - it's really heavy, many times crashes on Mac...

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