Uploaded October 2025 | Updated September 2026, 3 hours ago
In this, you're going to learn (through some of my painful mistakes):
1. How to hook up a Stable Baselines 3 Agent to GoDotRL
2. Best practices when it comes to reward shaping for Reinforcement Learning
3. Building optimal gaming environments in GoDot for RL training (def some gems in here)
All the code, logs and stuff is available via GitHub as always:
Code: github.com/nicknochnack/ReinforcementLearningBrackeysGame
Brackeys Original Tutorial: youtu.be/LOhfqjmasi0?si=3ib-ZIREVJq4P_GY
P.s. I'm still talking crap to Claude.
Although we're on much better terms now. Learning and recording this was an absolute blast, it's 8:29pm on a Sunday right now as I'm typing this but I had a ton of fun building this out. I feel like it's laid the groundwork to do a lot more with Reinforcement Learning and custom environments, especially those built with GoDot.
I was presenting at this think tank and saw a gun engineer demo using Reinforcement Learning for mining optimization. Got me thinking about how we could start applying this to simulated environments...what better way to start than by making a damn hard game 😅.
Hope you like it, I'm grateful for each and every one of you. I'm flying out to present in the states tomorrow so if you're around Florida, hit me up.
Code is in the GitHub repo there's like 14 final versions right now. 👀 I need to add the environments to Git LFS, if you want them let me know they're like 167MB for each of the dmg files but I can push them up if you want them.
Also a HUGEEE shout out to Ed Beeching the creator of GoDotRL and of course @Brackeys (you are hilarious and an absolute legend!)
Oh, and don't forget to connect with me!
LinkedIn: bit.ly/324Epgo
Facebook: bit.ly/3mB1sZD
GitHub: bit.ly/3mDJllD
Patreon: bit.ly/2OCn3UW
Join the Discussion on Discord: bit.ly/3dQiZsV
Happy coding!
Nick
In this, you're going to learn (through some of my painful mistakes):
1. How to hook up a Stable Baselines 3 Agent to GoDotRL
2. Best practices when it comes to reward shaping for Reinforcement Learning
3. Building optimal gaming environments in GoDot for RL training (def some gems in here)
All the code, logs and stuff is available via GitHub as always:
Code: github.com/nicknochnack/ReinforcementLearningBrackeysGame
Brackeys Original Tutorial: youtu.be/LOhfqjmasi0?si=3ib-ZIREVJq4P_GY
P.s. I'm still talking crap to Claude.
Although we're on much better terms now. Learning and recording this was an absolute blast, it's 8:29pm on a Sunday right now as I'm typing this but I had a ton of fun building this out. I feel like it's laid the groundwork to do a lot more with Reinforcement Learning and custom environments, especially those built with GoDot.
I was presenting at this think tank and saw a gun engineer demo using Reinforcement Learning for mining optimization. Got me thinking about how we could start applying this to simulated environments...what better way to start than by making a damn hard game 😅.
Hope you like it, I'm grateful for each and every one of you. I'm flying out to present in the states tomorrow so if you're around Florida, hit me up.
Code is in the GitHub repo there's like 14 final versions right now. 👀 I need to add the environments to Git LFS, if you want them let me know they're like 167MB for each of the dmg files but I can push them up if you want them.
Also a HUGEEE shout out to Ed Beeching the creator of GoDotRL and of course @Brackeys (you are hilarious and an absolute legend!)
Oh, and don't forget to connect with me!
LinkedIn: bit.ly/324Epgo
Facebook: bit.ly/3mB1sZD
GitHub: bit.ly/3mDJllD
Patreon: bit.ly/2OCn3UW
Join the Discussion on Discord: bit.ly/3dQiZsV
Happy coding!
Nick
