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lusob/space-invaders-v0-double-qlearning-model-nips-writeup.md

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space-invaders-v0-double-qlearning-model-nips-writeup.md

Code used to obtain these results can be found at the url https://github.com/lusob/DQN_TensorFlow_OpenAI_Gym

I used TensorFlow for network training and a double deep Q network with memory replay algorithm to optimize a 4 layers neural network.

Intall dependencies

sudo apt-get -y -qq install python-pip python-dev libopencv-dev python-opencv python-scipy python-numpy python-pygame build-essential cmake zlib1g-dev libjpeg-dev libboost-all-dev gcc

Install OpenAI Gym with atari env

pip install gym[atari]

Install TensorFlow

TensorFlow CPU version sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl Or uncommented below line to install TensorFlow GPU version sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl

Clone this project

git clone https://github.com/lusob/DQN_TensorFlow_OpenAI_Gym ~/DQN_TF_OpenAI

Start training

python main_multithread_openai.py -visualize n

To eval and record on video a trained model

python main_multithread_openai.py -visualize n -record_eval y -only_eval y -weight ckpt/model_nips_310161_Epoch_26_space_invaders_23h

You can also set -visualize y to enable video rendering, but it will be slow.

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