#
Interesting Environments to try

Here we provide a list of interesting environments you can try to train your agents on:

##
MineRL

MineRL is a Python library that provides a Gym interface for interacting with the video game Minecraft, accompanied by datasets of human gameplay. Every year there are challenges with this library. Check the website

To start using this environment, check these resources:

- What is MineRL?
- First steps in MineRL
- MineRL documentation and tutorials

##
DonkeyCar Simulator

Donkey is a Self Driving Car Platform for hobby remote control cars. This simulator version is built on the Unity game platform. It uses their internal physics and graphics and connects to a donkey Python process to use our trained model to control the simulated Donkey (car).

To start using this environment, check these resources:

- DonkeyCar Simulator documentation

- Learn to Drive Smoothly (Antonin Raffin’s tutorial) Part 1

- Learn to Drive Smoothly (Antonin Raffin’s tutorial) Part 2

- Learn to Drive Smoothly (Antonin Raffin’s tutorial) Part 3

- Pretrained agents:

  - https://huggingface.co/araffin/tqc-donkey-mountain-track-v0
  - https://huggingface.co/araffin/tqc-donkey-avc-sparkfun-v0
  - https://huggingface.co/araffin/tqc-donkey-minimonaco-track-v0

##
Starcraft II

Starcraft II is a famous real-time strategy game. DeepMind has used this game for their Deep Reinforcement Learning research with Alphastar

To start using this environment, check these resources:

- Starcraft gym
- A. I. Learns to Play Starcraft 2 (Reinforcement Learning) tutorial

##
Author

This section was written by Thomas Simonini