ArztSamuel/Applying_EANNs
A 2D Unity simulation in which cars learn to navigate themselves through different courses. The cars are steered by a feedforward neural network. The weights of the network are trained using a modified genetic algorithm.
This project helps demonstrate how artificial intelligence can learn to navigate complex environments. It takes a simulated car with sensor data and trains a neural network using an evolutionary algorithm to produce steering and engine commands. The primary users are researchers or students exploring concepts in AI, particularly genetic algorithms and neural networks for autonomous navigation.
1,564 stars. No commits in the last 6 months.
Use this if you want to observe or experiment with how evolutionary algorithms can train neural networks to control autonomous agents in a simulated 2D environment.
Not ideal if you need to deploy self-driving car AI in a real-world scenario or a high-fidelity 3D simulation.
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Language
ASP
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MIT
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Last pushed
May 23, 2025
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