ashoknar/dqn-ddqn-on-space-invaders

Implementation of Double Deep Q Networks and Dueling Q Networks using Keras on Space Invaders using OpenAI Gym. Code can be easily generalized to other Atari games.

DQN-DDQN-on-Space-Invaders

Implementation of Double Deep Q Networks and Dueling Q Networks using Keras on Space Invaders using OpenAI Gym. Code can be easily generalized to other Atari games.

Prerequistes

You can install all the prerequistes for code use using

pip install -r requirements.txt

Instructions on Use

Details about the code are covered in the blog here

To run the code use

  python main.py

with arguments where arguments are given by

usage: main.py [-h] -n NETWORK -m MODE [-l LOAD] [-s SAVE] [-x] [-v]

Train and test different networks on Space Invaders

optional arguments:
  -h, --help            show this help message and exit
  -n NETWORK, --network NETWORK
                        Please specify the network you wish to use, either DQN
                        or DDQN
  -m MODE, --mode MODE  Please specify the mode you wish to run, either train
                        or test
  -l LOAD, --load LOAD  Please specify the file you wish to load weights
                        from(for example saved.h5)
  -s SAVE, --save SAVE  Specify folder to render simulation of network in
  -x, --statistics      Specify to calculate statistics of network(such as
                        average score on game)
  -v, --view            Display the network playing a game of space-invaders.
                        Is overriden by the -s command

For example, to test the pre-trained Double Deep Q Network architecture and view the network playing space invaders use

  python main.py -n DDQN -m test -l saved.h5 -v

or to train the Dueling Q Network architecture and then save the resulting video of the network playing in the test/ directory use

 python main.py -n DQN -m train -s test

Note that as the model is trained, every 10000 images, the program saves the network weights in either saved.h5 of duel_saved.h5 for DDQN and DQN respectively