Unity Post Your ML-Agents Project

Hi, here are a couple of my project videos. There are source files available for most of them, but I haven't really kept those up to date, some are using old versions of ML-Agents. Also, since I'm still finding my way around Unity, I would approach some things differently now than I did back when I created these projects...
You can find more info in the video descriptions and a few other Unity and ML-Agents related videos on my channel.

https://github.com/mbaske/ml-motorcycles

https://github.com/mbaske/ml-dogfight

https://github.com/mbaske/ml-hover-bike-race

https://github.com/mbaske/robot-ants

 

https://forum.unity.com/threads/self-play-with-automatic-curriculum.921923/
https://github.com/mbaske/ml-selfplay-fighter

EDIT
THANK YOU for featuring the self-play fighter on the Unity blog.
I'm embarrassed to admit I only saw it this week, should really check the blog more often...

https://github.com/mbaske/angry-ai

https://github.com/mbaske/ml-table-football

https://github.com/mbaske/ml-simple-driver

https://github.com/mbaske/ml-drone-collection

 

I've put together a project that makes the machine learning process into the game itself! I've set up a twitch stream where you can watch creatures learn to navigate their environment to reach an exit and bet game coins on which ones you think will make it the furthest. It's running at https://twitch.tv/orbward

 

@mbaske Your project videos are always incredible. Also, that boxing match is hilarious!

 

Thanks!

 

Not as impressive as the above projects , but I wrote a tutorial on self driving car racing.

(already made September 2019, I just updated it now to 1.0.0 ML-Agents).

 

Hi everyone! I've been playing with ML agents again over the holidays and trying to see if I can build a small RTS game where units are trained agents.

Duel training with self-play:

Resource gathering:

I'm experimenting with giving player control over high-level policies (e.g. which resource to gather, whether to prioritize attack or survival), curriculum learning (I've set up a system of lessons and scenarios to learn complex behavior step by step), various perception sensors (e.g. basic "range" sensor with OverlapSphere, "smell map" sensor with decaying influence that could give rough pathfinding data).

My overall goal is to build a small prototype/proof of concept to understand how RL can be part of a designer's toolbox and learn best practices along the way.

I started looking into ML for Unity several years ago but had no time to fully flesh out something. I initially tried to set up my own interface to python, then the first version of ML-Agents came out but it was a bit rough around the edges. I'm very impressed with how far it has come and found it quite easy to understand and use. Thanks for making it!

 

I'm working on getting Unity ML to help train the AI for a tactics game (turn based strategy game). First I did TicTacToe. Code available on my github.


Next I implemented a GridWorld mini-game in the game.

.

I tried to do a more advanced mini-game (one vs. one duel) but it ran way too slowly for the agent to learn anything. I'm working on speeding up the code. I can't duplicate the game board and have many games run in parallel (like TicTacToe and the Unity examples) without a lot of changes to my code. I can get by without having to render anything so I'm trying to do some research to see how to go about creating a 'fast' mode to the game.

 

RoboLeague - a car soccer environment inspired by Rocket League for deep reinforcement learning experiments in an adversarial self-play setting. The project is open source - https://github.com/roboserg/RoboLeague

 

Hi everyone! I am experimenting with ML-Agents on Unity's Boat Attack environment. I have reduced the environmental objects for a smooth training process.
To project is open source and to get the assets and build versions of this environment, check out the following links:
Github env build repo - https://github.com/Dhyeythumar/BoatAttack-with-ML-Agents-build-versions
Github env assets repo - https://github.com/Dhyeythumar/BoatAttack-with-ML-Agents

Check out the following video of the trained agent:

The following video contains the complete training process streamed from Google Colab to the twitch server:

 

Hi, you can now find updated "Angry AI" source files working with the latest (1.7.2-preview) ML-Agents release on Github.
The project uses imitation learning, multi-stage training, hierarchical/tiered agent control and grid sensor observations.

https://github.com/mbaske/angry-ai

 

WWWoooWW!! Great job guys! Here you are another one. An Air Hockey simulator with 12 brains trained using self-play. It is available in the play store.

https://play.google.com/store/apps/details?id=com.OnMyOwnStudio.AIAirHockey

 

I've been experimenting with agent flocking behaviors:

Demo Link