ml-agents VS slimevolleygym

I am a beginner to using ML Agents to simulate an environment for DL i am trying to trial runs by tinkering through different values between the action space and keep encountering this issue when attempting to run a new trial. I've tried mlagents-learn --force and mlagents-learn --run-id=newtest but both prompt the same error message. Using linux, I am aware of a similar bug occuring in older versions (https://github.com/Unity-Technologies/ml-agents/issues/1505) but solutions didn't fix it.

The Github page for ML-Agents has a fairly straight forward example.

Very few commits in the last year and not many new features (https://github.com/Unity-Technologies/ml-agents/commits/develop)

and PR content: https://github.com/Unity-Technologies/ml-agents/commit/ed212103e451449bf84711a4a8f7bf11dfb1211a

Unity can build a stand-alone application or be used as a library. Javascript is deprecated, and Boo along with it although it was never really supported to begin with. Various types of machine learning are supported through the ML-Agent Toolkit and pretty well documented. The toolkit has a Python API but you should be careful about doing anything too unusual in Unity because the documentation tends to have a lot of dead-ends.

Op, I'd just pull the repo again to a new folder from https://github.com/Unity-Technologies/ml-agents (use SourceTree for simplicity if you don't know git).

My github issue on their documentation: https://github.com/Unity-Technologies/ml-agents/issues/5899

This is a good example for self-play training Slime Volleyball

Hi great that you are interested in the area, but as a beginner project is quite complex, having a team is a multi-agent task so not a small feat and i guess you want the same policy to play against itself? what is know as selfplay. which is not so hard to understand but a little bit in the tech part. Look a this 1v1 environment has a tutorial where they show selfplay and other single agent approaches using a well known RL Pytorch implementations. and for the policy optimization algorithm as the tutorial before you should go with PPO (which is a on-policy method like reinforce). there is something called HER for sparse reward but it works with off-policy methods like ddpg or sac. read a little bit more about this and then you will get the idea. My suggestion if you dont have extend experience try a supervise learning approach, where you have a dataset where the action is the label to be predicted and the observation is the input, MSE for the loss. like predicting the stering wheel angle from the image of the road kind of setup.

Inspired by Slime Volleyball Gym, I built a 3D Volleyball environment using Unity's ML-Agents toolkit. The full project is open-source and available at: 🏐 Ultimate Volleyball.