Train Navigation AI → Test in Unity → Deploy to Production
A workflow for game developers to create and deploy hierarchical AI agents that can navigate complex game environments with learned high-level behaviors.
Workflow Steps
ML-Agents Toolkit
Train hierarchical navigation model
Set up Unity ML-Agents environment with hierarchical reinforcement learning configuration. Train agent on basic navigation tasks (walking, crawling, jumping) using curriculum learning across multiple difficulty levels.
Unity 3D
Test agent in game environments
Import trained model into Unity game scenes. Create test scenarios with obstacles, multi-level terrain, and time-sensitive navigation challenges to validate learned behaviors.
TensorBoard
Analyze performance metrics
Monitor agent performance across different navigation scenarios. Track success rates, completion times, and behavior emergence patterns to optimize model parameters.
Unity Cloud Build
Deploy to production builds
Package optimized AI model into production game builds. Configure runtime inference settings and deploy across target platforms (PC, mobile, console).
Workflow Flow
Step 1
ML-Agents Toolkit
Train hierarchical navigation model
Step 2
Unity 3D
Test agent in game environments
Step 3
TensorBoard
Analyze performance metrics
Step 4
Unity Cloud Build
Deploy to production builds
Why This Works
Hierarchical RL naturally maps to game navigation challenges, and Unity's ML-Agents provides the perfect development-to-production pipeline for game AI
Best For
Game studios developing AI-driven NPCs or player assistance systems that need sophisticated navigation capabilities
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