Deep Q-Learning with Pytorch

The implementation of Deep Q Learning with Pytorch.

• Replay Memory
• Simple Deep Q Learning (not using A3C or Dueling)
• Support for original DQN (the paper in Nature published by DeepMind) and LSTM-based DQN
• Used Pytorch
• Frame Skipping
• Target Network (for stability when training)
• Python 3.x (I used Python 3.6)

DQN Algorithm

The linked article explains how DQN works in detail.
http://andersonjo.github.io/artificial-intelligence/2017/06/03/Deep-Reinforcement-Learning/

Actual Playing game

The below image is actual result of the code here.

Installation

Requirements

1. Python 3
2. Pytorch
3. TorchVision
4. gym
5. opencv2
6. Scipy
7. ffmpeg (optional. if you want to record the gameplay)

Install PyGame

sudo pip3 install pygame

Install PyGame-Learning-Environment

git clone https://github.com/ntasfi/PyGame-Learning-Environment.git
cd PyGame-Learning-Environment/
sudo pip3 install -e .

Install Gym-Ple

git clone https://github.com/lusob/gym-ple.git
cd gym-ple/
sudo pip3 install -e .

Comparison

• Best Score is the average value of 10 times of games.

How to use

Training

Before training, you need to make a "dqn_checkpoints" directory for saving model automatically.

mkdir dqn_checkpoints
python3 dqn.py --mode=train

Training LSTM-based DQN

mkdir dqn_checkpoints
python3 dqn.py --mode=train --model=lstm

Playing

It automatically loads the latest checkpoint (it loads saved model parameters).
But first, you need to train it.
If there is no checkpoint (You might have not trained it yet), the play is just simply random walk.

python3 dqn.py --mode=play

playing LSTM-based DQN is like..

python3 dqn.py --mode=play --model=lstm

Recoding

If you want to record game play, just do like this.

python3 dqn.py --mode=play --record 

How to convert video to GIF file

mkdir frames
ffmpeg -i flappybird.mp4 -qscale:v 2  -r 25 'frames/frame-%05d.jpg'
cd frames
convert -delay 4 -loop 0 *.jpg flappybird.gif

FFMpeg and Imagemagic(Convert command) have the following options.

-r 5 stands for FPS value
    for better quality choose bigger number
    adjust the value with the -delay in 2nd step
    to keep the same animation speed

-delay 20 means the time between each frame is 0.2 seconds
   which match 5 fps above.
   When choosing this value
       1 = 100 fps
       2 = 50 fps
       4 = 25 fps
       5 = 20 fps
       10 = 10 fps
       20 = 5 fps
       25 = 4 fps
       50 = 2 fps
       100 = 1 fps
       in general 100/delay = fps

-qscale:v n means a video quality level where n is a number from 1-31, 
   with 1 being highest quality/largest filesize and 
   31 being the lowest quality/smallest filesize.

-loop 0 means repeat forever