Hebbian Learning for Autonomous Agents

This project is part of my M.Sc. Thesis. It demonstrates that autonomous (driving) agents can be trained using Hebbian learning, and attempts to optimize its control.

It is based on the Novelty Real-Time Autoencoder Augmented Hebbian Network (RAAHN)-algorithm. Read about Novelty-RAAHN ▸

Name		Name	Last commit message	Last commit date
Latest commit History 8 Commits
.spyproject		.spyproject
Agents		Agents
Maps		Maps
Networks		Networks
Sensors		Sensors
TestCode		TestCode
Textures		Textures
__pycache__		__pycache__
Car.py		Car.py
CarMazeEnv.py		CarMazeEnv.py
Modulation.py		Modulation.py
README.md		README.md
RaahnXmlConfig.py		RaahnXmlConfig.py
RangeFinder.py		RangeFinder.py
Wall.py		Wall.py
buffer.py		buffer.py
controllers.py		controllers.py
functions.py		functions.py
lines.py		lines.py
net3.py		net3.py
raahn.py		raahn.py
sim-snapshot.PNG		sim-snapshot.PNG
sim-snapshot2.PNG		sim-snapshot2.PNG
sim-snapshot3.PNG		sim-snapshot3.PNG
training.py		training.py
unit_tests.py		unit_tests.py
visualnet.py		visualnet.py

AjdinHusic/aahlearning

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Hebbian Learning for Autonomous Agents

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