TF-Ale-Op is a TensorFlow Op that makes it possible to include the Arcade Learning Environment into the computational graph without the overhead of dealing with python interface and feed_dict.
It accepts a single scalar tensor as action and returns a reward, end-of-episode flag, observation tuple. The Op is thread-safe and multiple environments can be run in parallel.
- install TensorFlow
- clone the Arcade-Learning-Environment repository
- compile and install ALE:
sudo apt-get install libsdl1.2-dev libsdl-gfx1.2-dev libsdl-image1.2-dev cmake
cd Arcade-Learning-Environment
mkdir build && cd buildIf you installed tensorflow binary package and are using GCC version 5 or later run:
cmake -D CMAKE_CXX_FLAGS="-D_GLIBCXX_USE_CXX11_ABI=0" -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=ON ..If you compiled tensorflow from source or are using GCC version 4 run:
cmake -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=ON ..make -j && make install- clone this repository
python setup.py install
import tensorflow as tf
from aleop import ale
session = tf.Session()
a = tf.placeholder(tf.int32)
step = ale(a, "pong", frameskip_min=2, frameskip_max=5)
reward, done, screen = session.run(step, feed_dict={a: 3})