**Title:** VBAF -- educational AI and reinforcement learning framework in pure PS 5.1

Hello,

I have been building an educational framework for learning AI concepts in PowerShell 5.1 and wanted to share it with this community.

VBAF (Visual AI & Reinforcement Learning Framework) implements neural networks, Q-learning, DQN, PPO and A3C from scratch -- no Python, no external dependencies, no cloud services.

The goal is to make AI concepts accessible to PowerShell developers. Every algorithm has full comments explaining the mathematics in plain English, with references to the original research papers.

Quick start:

```powershell
Install-Module VBAF -Scope CurrentUser
. .\VBAF.LoadAll.ps1

# See a neural network learn XOR
& .\VBAF.Core.Example-XOR.ps1

# Train a DQN agent on CartPole
$agent = (Invoke-DQNTraining -Episodes 50 -FastMode)[-1]
$agent.PrintStats()
```

The framework also includes a multi-agent market simulation where four company agents compete using Q-learning -- price wars, innovation races and tacit collusion emerge naturally without being programmed.

For teachers: docs/teaching/ contains a 4-week course outline, lab exercises and exam questions.

GitHub: https://github.com/JupyterPS/VBAF
PSGallery: https://www.powershellgallery.com/packages/VBAF

Happy to answer any questions about the implementation choices or the PS 5.1 class system quirks.

Henning -- Roskilde, Denmark