Get Started with ML-Agents in Unity - Part 2: Basic Concepts

In this tutorial, we explore how Unity’s ML-Agents framework implements Reinforcement Learning. We break down the 'PushBlock' example, covering key RL concepts such as observations, actions, and rewards. Ready to build your own ML-Agents project? Stay tuned for Part 3, where we’ll start creating a custom project from scratch! → Next Video • Get Started with ML-Agents in Unity - Part 3: Creating the 'Turtle Agent' Project: https://youtu.be/xOt9JsTjhJ8 ← Previous Video • Get Started with ML-Agents in Unity - Part 1: Setup & Installation: https://youtu.be/ZtbjlrmRbyc 💬 Join our Discord community (free!): https://discord.gg/E3TKMv4tfV ▬ Support My Work ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ❤️ Support me on Patreon: https://www.patreon.com/ludicworlds ☕ Buy me a coffee: https://ko-fi.com/ludicworlds Thank you for your support! ▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 0:00 - Intro 0:57 - What is RL? 2:10 - ML-Agents Examples Project 2:46 - PushBlock Scene 4:11 - PushAgentBasic Script 5:25 - Initialization 6:00 - Episodes 7:33 - Observations 10:47 - Actions 12:10 - Rewards & Penalties 13:35 - Outro ▬ Useful Links ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ► ML-Agents Github documentation: https://docs.unity3d.com/Packages/[email protected]/manual/ ► Example Learning Environments: https://github.com/Unity-Technologies/ml-agents/blob/develop/docs/Learning-Environment-Examples.md ▬ Credits ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ ► Music by - CO.AG Music: https://www.youtube.com/@co.agmusic #unity #ai #mlagents #reinforcementlearning