how you can bring your own digital assistant to life! - window-tip

Hello, dear tech enthusiasts! 😊

Have you ever dreamed of building your own AI companion on Windows using Unity and ML-Agents? Whether you're a beginner or someone curious about combining game development with artificial intelligence, this guide will walk you through every essential step.

Let’s explore together how you can bring your own digital assistant to life!

📋 Table of Contents

1. Setting Up Your Environment

2. Installing ML-Agents in Unity

3. Designing Your AI Companion

4. Training the AI Model

5. Testing and Fine-Tuning

6. Integrating with Windows Features

7. Final Deployment & What’s Next

1. Setting Up Your Environment

Before you start developing your AI companion, it’s essential to get your system ready. Setting up the right environment will save you time and headaches later. You’ll need to install several tools to proceed smoothly with Unity and ML-Agents.

1. Install Unity Hub

   Download and install Unity Hub. Then, install the Unity Editor (preferably version 2021.3 LTS or later).

2. Install Python

   ML-Agents requires Python. Use Python 3.8 to 3.10, and ensure pip is updated.

3. Install Visual Studio

   Unity needs Visual Studio for scripting. Make sure to check the “Game Development with Unity” workload during installation.

4. Set Up a Virtual Environment

   Create a virtual Python environment and activate it. This helps isolate dependencies for your ML project.

💡 TIP: Ensure that all paths (Python, Unity, etc.) are added to your system’s environment variables for easy CLI access.

2. Installing ML-Agents in Unity

ML-Agents is Unity’s machine learning plugin that enables agents to learn behaviors using reinforcement learning. Setting it up might look complex at first, but following the steps carefully will ensure a smooth setup.

1. Clone the ML-Agents GitHub Repository

   Visit the official ML-Agents GitHub page and clone the repository locally.

2. Install Required Python Packages

   Inside your virtual environment, run:
   pip install -e ./ml-agents
   pip install -e ./ml-agents-envs

3. Link ML-Agents to Unity

   Open Unity and load the “Project” folder from the ML-Agents repo. Make sure to import the ML-Agents package into your project.

👉 Having issues importing?

Check if you have all dependencies in place. Unity might show console errors if packages are missing or incompatible.

3. Designing Your AI Companion

Now the fun begins! Designing your AI companion involves creating a character or entity in Unity that will be “intelligent.” This step includes setting up its physical body and defining its behavior goals.

✅ Agent Setup: Use the Agent component provided by ML-Agents on your character prefab.
✅ Brain Type: Configure the Brain to be “External” so it communicates with Python training scripts.
✅ Observations: Define what your AI can sense—positions, velocities, distances, etc.
✅ Actions: Define what actions the agent can take—moving, rotating, interacting.

Think of this as giving your AI both “eyes” and “hands.” It can now see the world and interact with it through coded logic and learned behaviors.

4. Training the AI Model

Training is where your agent learns how to act. You’ll use reinforcement learning to teach your AI companion how to behave in a virtual world.

mlagents-learn config/your-config.yaml --run-id=your_run_id --train




    While training, Unity should be open in “play” mode. The AI receives rewards or penalties based on its actions. Over time, the model learns to maximize positive outcomes.



    

        💎 Core Tip:

        Adjust the reward system carefully. Too strict and your agent will give up; too loose and it may behave randomly.
    

(계속 작성 중...)


5. Testing and Fine-Tuning


    Once training is complete, it’s time to evaluate how well your AI companion behaves. Import the trained `.onnx` model into Unity and assign it to the agent.



    ✅ Playtest: Observe the agent’s movements. Does it act naturally? Is it achieving goals?

    ✅ Tweak rewards: If the AI behaves oddly, revisit your reward function.

    ✅ Environment updates: Sometimes agents get too used to specific environments. Add variations for robustness.



    Testing is a continuous loop. Don’t expect perfection on the first run. It’s okay to go back and retrain with improved parameters or more episodes.



    
        The best models are often the result of dozens of training cycles, not just one lucky shot.
    


6. Integrating with Windows Features


    To elevate your AI companion beyond Unity’s boundaries, you can integrate it with Windows features. This brings your project closer to being a real desktop assistant.



    ✅ Speech Recognition: Use Microsoft's Speech SDK to capture user voice input.

    ✅ System Control: Interface with Windows API to open files, control apps, or read system status.

    ✅ Notifications: Use toast notifications to let the AI “communicate” proactively.



    For example, your AI could remind you of meetings or suggest breaks based on activity. With C# or Python scripts running in the background, this integration is very feasible.



    

        💡 TIP: Use Unity's System.Diagnostics namespace or integrate with external Python scripts for OS-level control.
    


7. Final Deployment & What’s Next


    You’ve built, trained, and tested your AI companion. Now it’s time to deploy. Unity allows you to export as a Windows standalone executable so your AI can run like a regular app.



    ✅ Build the Project: Go to File > Build Settings and choose Windows.

    ✅ Add Startup Behavior: Code logic for initializing the AI when app launches.

    ✅ Prepare for the Future: You can expand features later, like emotion recognition or camera input.



    This is just the beginning. Your AI companion is functional, but future upgrades could include neural speech synthesis, real-time interaction logging, or even integration with smart devices.


Related Resources


    
Unity ML-Agents GitHub
    
Official ML-Agents Documentation
    
Microsoft Speech to Text API


Tags
Unity, ML-Agents, AI Companion, Windows Development, Reinforcement Learning, Speech Recognition, Unity3D, Python AI, Agent Training, Desktop Assistant