This repository contains the code to reproduce the results presented in the paper Boosting Reinforcement Learning with Unsupervised Feature Extraction presented at ICANN 2019.

The project is based on keras-rl by Matthias Plappert. We kindly thank him and all contributors for their great work.

The code can be installed by cloning this repository and executing pip install -e . in the directory containing the setup.py.

It is also necessary to install vizdoomgym. Make sure to check out the healthgathering branch to use our modified reward function for Health Gathering and Health Gathering Supreme.

Start training a DQN agent in the Basic scenario with randomly initilized filters for 1.5M time steps by executing python dqn.py --env-name=VizdoomBasic-v0 --filters=scratch --steps=1500000 in the examples directory.

The environments from the paper are VizdoomBasic-v0, VizdoomDefendCenter-v0, VizdoomHealthGathering-v0, VizdoomHealthGatheringSupreme-v0 and VizdoomMyWayHome-v0. Please refer to the documentation of vizdoomgym for more detailed information.

The filter option determines the feature extraction method that is used. Possible options are scratch, pretrained, convAE, SFA and combination.