xiaobanni/ILI

(KDD 2023) Internal Logical Induction for Pixel-Symbolic Reinforcement Learning

Installation

Install OpenJDK for python-weka-wrapper3 (Ubuntu for example)

sudo apt-get update
sudo apt-get install openjdk-11-jdk
export JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64

Install Ubuntu package

sudo apt install xvfb # for CarRacing Env
sudo apt install swig # for gym[box2d]==0.21

Install Python Package

conda create -n ILI python=3.10
conda activate ILI
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
conda install tensorboard tensorboardX scipy
pip install gym==0.21 'gym[box2d]==0.21' 'gym[atari]==0.21' 'gym[accept-rom-license]==0.21' 'gym[other]==0.21'
pip install UtilsRL==0.4.8 matplotlib imageio python-weka-wrapper3==0.2.11 nni pyglet==1.5.27 autopep8

We use gym 0.21 instead of the latest gymnasium 0.26 because gymnasium is incompatible with python-weka-wrapper3

Install modified Flappy Bird env (Changed the installation dependencies and state representation)

pip install -e envs/flappy-bird-gym

Install modified Slime Volleyball env (Changed the state representation)

pip install -e envs/slimevolleygym

Install modified Collect Health env (Changed the state representation)

pip install -e envs/Miniworld

[Optional] Use gym Wrapper RecordVideo

Set use_recordvideo = True in ./configs/config.py.

conda install -c conda-forge ffmpeg

and then run

xvfb-run -a python main.py

Experiment

The available options for xxx are FlappyBird, CarRacing-v1, and CollectHealth-v1.

Exp1

bash scripts/exp1/PLP.sh

Exp2

bash scripts/exp2/SymbolicDQN/xxx.sh
bash scripts/exp2/PixelDQN/xxx.sh
bash scripts/exp2/MixedDQN/xxx.sh
bash scripts/exp2/ILIDQN/xxx.sh

Citation

@inproceedings{DBLP:conf/kdd/0003CZYZ023,
  author       = {Jiacheng Xu and Chao Chen and Fuxiang Zhang and Lei Yuan and Zongzhang Zhang and Yang Yu},
  title        = {Internal Logical Induction for Pixel-Symbolic Reinforcement Learning},
  booktitle    = {Proceedings of the 29th {ACM} {SIGKDD} Conference on Knowledge Discovery
                  and Data Mining, {KDD} 2023, Long Beach, CA, USA, August 6-10, 2023},
  pages        = {2825--2837},
  publisher    = {{ACM}},
  year         = {2023},
  doi          = {10.1145/3580305.3599393},
}