
I Trained a Deep Q Network Built in TensorFlow to Play Atari Pong - superfx
https://www.reddit.com/r/MachineLearning/comments/3y16pa/i_trained_a_deep_q_network_built_in_tensorflow_to/
======
smhx
This is simply a transpile / reproduction of the original Torch version from
Deepmind, but in TensorFlow. It doesn't really do anything new or different
compared to the paper by [Mnih et.
al.]([http://www.nature.com/nature/journal/v518/n7540/full/nature1...](http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html))
.

~~~
tostitos1979
Replicating a previous result is still a worthwhile endeavor. Frankly, I don't
think the adjective "simply" applies here. This isn't a script kiddie running
a binary they found on the web.

P.S. I'm a professional scientist and I think the world would be better if
people replicated results more often, and even showcased negative results.

~~~
smhx
I didn't mean it in a condescending way. I was stating that this is not
algorithmically new, and is a reproduction of the paper. From the README of
the repository, it is not obvious that this is a reproduction. The information
is fairly hidden, and the DQN paper is given as a reference. A casual glance
might suggest that there is research innovation there.

Here are other reproductions just FYI:

Original Torch version from DeepMind: [github mirror]
[https://github.com/kuz/DeepMind-Atari-Deep-Q-
Learner](https://github.com/kuz/DeepMind-Atari-Deep-Q-Learner)

Caffe: [https://github.com/muupan/dqn-in-the-
caffe](https://github.com/muupan/dqn-in-the-caffe)

Theano:
[https://github.com/spragunr/deep_q_rl](https://github.com/spragunr/deep_q_rl)

Neon: [https://github.com/DoctorTeeth/dqn](https://github.com/DoctorTeeth/dqn)

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nullc
Would be interesting to see if it could be regularized to make it a bit less
twitchy. (E.g. by giving a fitness bonus to no action / and-or penalizing many
changes of direction within some time window.)

------
minimaxir
Github page:
[https://github.com/asrivat1/DeepLearningVideoGames](https://github.com/asrivat1/DeepLearningVideoGames)

