ML Agents

Intro

Unity Machine Learning - Reinforcement Learning

There are strong parallels between robots and characters in a game. In fact, in many ways, one can view a non-playable character (NPC) as a virtual robot, with its own observations about the environment, its own set of actions and a specific objective. Thus it is natural to explore how we can train behaviors within Unity using reinforcement learning. This is precisely what the ML-Agents Toolkit offers.

Resources

• Unity- ML-Agents Overview
• https://github.com/Unity-Technologies/ml-agents
• CLI + MCP + AI IDE (ChatGPT Codex/Cursor/Antigravity/Claude Code/Windsurf): Next Steps for Unity Workflow Automation

Unity ML-Agents Toolkit

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning. https://github.com/Unity-Technologies/ml-agents

Setup

https://docs.unity3d.com/Packages/com.unity.ml-agents@4.0/manual/Installation.html

Environment Setup

• Create a Conda environment

conda create -n mlagents python=3.10.12 && conda activate mlagents

Install ML-Agents (Advanced)

• Clone repo

git clone --branch release_23 https://github.com/Unity-Technologies/ml-agents.git

• Open the repo’s Project folder in Unity
• Install the ML-Agents Unity package by adding the com.unity.ml-agents package
• Install the ML-Agents Python package (with Conda env)

# cd /path/to/ml-agents
python -m pip install ./ml-agents-envs
python -m pip install ./ml-agents

• Verify installation

mlagents-learn --help

Open Example Environment

https://docs.unity3d.com/Packages/com.unity.ml-agents@4.0/manual/Examples-setup.html

• Open Assets/ML-Agents/Examples to access an example (e.g. 3D ball example workflow )