This is the official repository for the paper Deep Reinforcement Learning via Object-Centric Attention.
This repository includes different wrappers to be used with OCAtari to generate different object-centric masked input representations.
pip install "gymnasium[atari, accept-rom-license]"
pip install -r requirements.txt
pip install .
from ocatari_wrappers import BinaryMaskWrapper
from ocatari import OCAtari
env = OCAtari("ALE/Frostbite")
env = BinaryMaskWrapper(env)
obs, info = env.reset()
done = False
while not done:
action = env.action_space.sample()
obs, reward, terminated, truncated, info = env.step(action)
done = terminated or truncatedFirst test if the Backend is set up correctly
python scripts/run.py -g Pong -hu
Now we test if the wrappers are also set up
python scripts/print_state.py
If everything works as intended you should now have an svg showing you the binary mask in the game of Freeway after 100 steps.
Please cite as stated.