• Atari Learning Environment
• MuJoCo
• PyBullet (including Racecar, Minitaur and Kuka)
• DeepMind Control Suite (via dm_control2gym)

All environments are operated using exactly the same Gym interface. See their documentations for a comprehensive list.

To use the DeepMind Control Suite environments, set the flag --env-name dm.<domain_name>.<task_name>, where domain_name and task_name are the name of a domain (e.g. hopper) and a task within that domain (e.g. stand) from the DeepMind Control Suite. Refer to their repo and their tech report for a full list of available domains and tasks. Other than setting the task, the API for interacting with the environment is exactly the same as for all the Gym environments thanks to dm_control2gym.

• Python 3 (it might work with Python 2, but I didn't test it)
• PyTorch
• Visdom
• OpenAI baselines

In order to install requirements, follow:

# PyTorch
conda install pytorch torchvision -c soumith

# Baselines for Atari preprocessing
git clone https://github.com/openai/baselines.git
cd baselines
pip install -e .

# Other requirements
pip install -r requirements.txt

# Run training
python3.6 -W ignore main.py --env-name "AssaultNoFrameskip-v0" --num-frames  10000  --log-evaluation  --lr 1e-3