VBAF.psd1
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@{ RootModule = 'VBAF.psm1' ModuleVersion = '5.0.1' GUID = 'a1b2c3d4-e5f6-7890-abcd-ef1234567890' Author = 'Henning' CompanyName = 'VBAF Project' Copyright = '(c) 2025-2026 Henning. All rights reserved.' PowerShellVersion = '5.1' CompatiblePSEditions = @('Desktop') DotNetFrameworkVersion = '4.5' RequiredAssemblies = @( 'System.Windows.Forms', 'System.Drawing' ) Description = 'VBAF (Visual AI and Reinforcement Learning Framework) - An educational AI framework built entirely in PowerShell 5.1. No Python. No dependencies. Implements neural networks, Q-learning, DQN, PPO and A3C from scratch with full educational comments and original paper references. Includes 6 runnable examples, interactive console teacher, experiment playground, multi-agent market simulation, and 14 enterprise automation pillars. Runs on any Windows PC. GitHub: https://github.com/JupyterPS/VBAF' FunctionsToExport = @('*') CmdletsToExport = @() VariablesToExport = @() AliasesToExport = @() PrivateData = @{ PSData = @{ Tags = @('AI','MachineLearning','ReinforcementLearning','NeuralNetwork','QLearning','DQN','PPO','A3C','MultiAgent','Automation','Visualization','PowerShell','Education','MLOps','DeepLearning','CNN','RNN','LSTM','AutoML','DataPipeline') LicenseUri = 'https://github.com/JupyterPS/VBAF/blob/master/LICENSE' ProjectUri = 'https://github.com/JupyterPS/VBAF' ReleaseNotes = 'v5.0.1 (June 2026) - Educational repositioning. 6 runnable examples with launchers. Full docs: Theory, Architecture, API-Reference, GettingStarted. Educational tools: Start-VBAFTeach, Start-VBAFPlayground, Invoke-VBAFAgentBenchmark. DQN, PPO, A3C implemented and benchmarkable. Multi-agent market simulation. 14 enterprise automation pillars. Runs on any Windows PC with no dependencies.' } } } |