Active Inference in Python

Active Inference in Python

Overview

This skill provides a complete Active Inference implementation in Python, demonstrating Bayesian belief updating, variational free energy calculation, and expected free energy-based policy selection.

Core Algorithms

1. Belief Updating: Bayesian inference using observation likelihoods to update posterior beliefs
2. Free Energy Calculation: KL divergence between posterior beliefs and prior distribution
3. Policy Selection: Softmax action selection over expected free energy per action
4. Perception-Action Loop: Iterative sense → infer → act cycle with generative model

Key Files

• config_manager.py — Source implementation
• serializer.py — Source implementation
• student_teacher.py — Source implementation
• teacher_wrapper.py — Source implementation
• test_student_teacher.py — Source implementation
• run.sh — Execution script (handles compilation if needed)
• README.md — Usage documentation and requirements

Usage

cd 0_CONTEXT/Computer_Languages/Python/
./run.sh

Language-Specific Features

• Rapid prototyping and iteration
• Dynamic typing flexibility
• Rich standard library

Integration

• Tested via master_controller.py test python
• Benchmarked via benchmark_suite.py
• Listed in languages.json under category "Scripting"

Prerequisites

See README.md for Python-specific installation requirements.