RL Atari experiments

Deep Reinforcement Learning project with Atari games for the Reinforcement Learning course at CentraleSupelec

Description

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.

How to run

• 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).

References

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.