AnuraagRath / SelfDrivingCar-using-NEAT

Using NEAT(Neuroevolution of Augmenting Topologies), a simulated agent learns to drive a car through a series of simple obstacles by Mutating and adopting the 'genotypes' of 'parents'. The environment is set up in such a way where the agent uses multiple copies of itself to explore the environment. The copies that do not progress further are eliminated. This process repeats through a number of generations, until the agent is able to navigate through this environment without hitting the obstacles.

SelfDrivingCar Simulation using NEAT:

Using NEAT(Neuroevolution of Augmenting Topologies), a simulated agent learns to drive a car through a series of simple obstacles by Mutating and adopting the 'genotypes' of 'parents'. The environment is set up in such a way where the agent uses multiple copies of itself to explore the environment. The copies that do not progress further are eliminated. This process repeats through a number of generations, until the agent is able to navigate through this environment without hitting the obstacles.

Live Demo: Self_Driving_Car_Sim

The Environment:

A simple environment has been created where a car just tries to avoid the generic obstacles.

The Initial State:

Multiple versions of the Agent are made to explore the environment to navigate and learn from it.

The Algorithm:

The Algorithm used is NEAT. Neuroevolution of Augmenting Topologies.

Refer the paper here: NEAT

• Mutation:

• Basic NN Architecture - The Sequential Model:

• 5 input Nodes, 8 Hidden Nodes and 2 output nodes:

The 5 input nodes take in - "the x pos from height, y pos from nearest width, top pos, bottom pos, velocity of the car"

Final result:

• The Car moves independently without hitting the generic obstacles

Thank you

Anuraag Rath

:P