Deep Q-learning agent for replicating DeepMind's results in paper "Human-level control through deep reinforcement learning". It is designed to be simple, fast and easy to extend. In particular:

• It's Python :).
• New ALE Python interface is used.
• Fastest convolutions from Neon deep learning library.
• Every screen is kept only once in replay memory, fast minibatch sampling with Numpy array slicing.
• The number of array and datatype conversions is minimized.

Restriction from Neon is, that it currently works only on Maxwell architecture GPU-s. Hopefully this will change. You can still simulate playing from pretrained models using just CPU, see the example below.

Currently only instructions for Ubuntu are provided. For OS X refer to ALE and Neon documentation.

Install prerequisites:

sudo apt-get install libhdf5-dev libyaml-dev libopencv-dev pkg-config
sudo apt-get install python python-dev python-pip python-virtualenv

Check out and compile the code:

git clone https://github.com/NervanaSystems/neon.git
cd neon
make

Neon installs itself into virtual environment in .venv. You need to activate that to import Neon in Python:

source .venv/bin/activate

Install prerequisites:

sudo apt-get install cmake libsdl1.2-dev

Check out and compile the code:

git clone https://github.com/mgbellemare/Arcade-Learning-Environment.git
cd Arcade-Learning-Environment
cmake -DUSE_SDL=ON -DUSE_RLGLUE=OFF -DBUILD_EXAMPLES=ON .
make -j 4

Install Python library (assuming you have activated Neon virtual environment):

pip install .

Prerequisities:

pip install numpy argparse logging

Neon virtual environment already contains those libraries, but they are listed here, just in case.

Also you need OpenCV, which is pain to install to virtual environment. I ended up with this hack:

sudo apt-get install python-opencv
ln -s /usr/lib/python2.7/dist-packages/cv2.so NEON_HOME/.venv/lib/python2.7/site-packages/

Then just check out the code:

git clone https://github.com/tambetm/simple_dqn.git
cd simple_dqn

To run training for Pong:

./train.sh roms/pong.bin

There are plethora of options, just run ./train.sh --help to see them. While training, the network weights are saved to snapshots folder after each epoch. Name of the file is <game>_<epoch_nr>.pkl. Training statistics are saved to results/<game>.csv, see below how to produce plots from it.

You can resume training by running

./train.sh roms/pong.bin --load_weights snapshots/pong_2.pkl

Pay attention, that exploration rate starts from 1 and replay memory is empty. You may want to start with lower exploration rate, e.g. for epoch 2 usual exploration rate would be 1 - (1 - 0.1) * (2 * 250000 / 1000000) = 0.55. Add --exploration_rate_start 0.55 --exploration_decay_steps 500000 to the command line.

To run only testing on pre-trained model:

./test.sh roms/pong.bin --load_weights snapshots/pong_49.pkl

To see the game screen while playing run

./play.sh roms/pong.bin --load_weights snapshots/pong_49.pkl

You can do this even without GPU, by adding --backend cpu to command line. During gameplay you can use following keys: 'a' - slow down, 's' - speed up, 'm' - manual control mode, '[' - volume down, ']' - volume up. Visualization works even in text terminal!

You will need avconv for this to work:

sudo apt-get install libav-tools

To play one game and record video

./record.sh roms/pong.bin --load_weights snapshots/pong_49.pkl

First game frames are extracted to videos/pong folder as PNG files. Then avconv is used to convert these into video.

You will need matplotlib for Python:

pip install matplotlib

To plot results:

./plot.sh results/pong.csv

This produces results/pong.png, which includes four main figures: average reward per game, number of games per phase (training, test or random), average Q-value of validation set and average network loss. You can customize the plotting result with --fields option - list comma separated names of CSV field names (the first row). For example default results are achieved with --fields average_reward,meanq,nr_games,meancost. Order of figures is left to right, top to bottom.

There are two additional scripts for profiling:

• profile_train.sh - runs Pong game 1000 steps in training mode. This is for figuring out bottlenecks in minibatch sampling and network training code. Prediction is disabled by setting exploration rate to 1.
• profile_test.sh - runs Pong game 1000 steps in testing mode. This is for figuring out bottlenecks in prediction code. Exploration is disabled by setting exploration rate to 0.
• profile_random.sh - runs Pong game 1000 steps with random actions. This is for measuring performance of ALE interface, network is not used at all.

This wouldn't have happened without inspiration and preceding work from my fellow PhD students Kristjan Korjus, Ardi Tampuu, Ilya Kuzovkin and Taivo Pungas from Computational Neuroscience lab run by Raul Vicente in University of Tartu, Estonia. Also I would like to thank Nathan Sprague and other nice folks at Deep Q-Learning list.