Deep Reinforcement Learning project with Atari games for the Reinforcement Learning course at CentraleSupelec
For this project, we implemented two different models: DQN and MNF-DQN.
Thanks to OpenAI's gym
environment, any Atari environment can be used to train one of the two aforementioned models.
- Install the required libraries
pip install -r requirements.txt
-
Set the configuration file
cfg.yml
to run the desired experiment -
Install ROMs
In order to import ROMS, you need to download Roms.rar
from the Atari 2600 VCS ROM Collection and extract the .rar
file. Once you've done that, run:
python -m atari_py.import_roms <path to folder>
This should print out the names of ROMs as it imports them. The ROMs will be copied to your atari_py
installation directory.
- Run the experiment
python main.py
Note: in our experiments we used only three Atari environments (Freeway-v0
, Skiing-v0
, MsPacman-v0
); but it is possible to run an experiment with any other Atari game (as long as it is available in the gym environment).
In this project, we used:
- some functions from OpenAI's baselines;
- code from PyTorch's tutorial on the DQN model;
- code from Facebook Research's MNF-DQN model.