George614/neural_active_inference
Official implementation of paper "A neural active inference model of perceptual-motor learning" published on Computational Neuroscience in 2023.
This project offers a way to explore how a simulated brain might learn and adapt to new situations, particularly in tasks requiring movement and perception. You input data from simple environments like 'Mountain Car' or 'Cartpole', and it produces a model that demonstrates how an agent can learn to anticipate and react to changes. It's designed for computational neuroscientists or researchers studying brain-inspired AI.
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Use this if you are a computational neuroscientist or AI researcher investigating how an agent, inspired by active inference, learns perceptual-motor skills in simulated environments.
Not ideal if you're looking for a practical, ready-to-deploy solution for real-world robotics or complex industrial control systems.
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Language
Python
License
BSD-3-Clause
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Last pushed
Aug 24, 2024
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