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Re:Deep Reinforcement Learning to Play Space Invaders [pdf] by ap46 on Monday April 10, 2017 @10:11PM (#14084320)
PLAGIARISM

Third paragraph of Intro in original DeepMind paper https://arxiv.org/pdf/1312.5602.pdf

&

Second paragraph of Intro(5th line) of this project. http://nihit.github.io/resources/spaceinvaders.pdf

They didn't even bother to do something original except for using the RAM state instead of pixel values like the original paper. It's something that's easily doable with the openAI gym. This seems like a course project at stanford.

Re:Deep Reinforcement Learning to Play Space Invaders [pdf] by tMMkeJRMD0 on Tuesday April 11, 2017 @12:31AM (#14084857)
Hi, one of the authors here. Yes this is a school project. This pdf was a draft, not the final version for the class submission (the paragraph in question never made it to the final version). Don't know who shared the link here, but it is updated now.
Re:Deep Reinforcement Learning to Play Space Invaders [pdf] by ramblenode on Monday April 10, 2017 @11:28PM (#14084603)
For convenience.

Original:

However reinforcement learning presents several challenges from a deep learning perspective. Firstly, most successful deep learning applications to date have required large amounts of hand-labelled training data. RL algorithms, on the other hand, must be able to learn from a scalar reward signal that is frequently sparse, noisy and delayed.

New:

However reinforcement learning presents challenges from a deep learning perspective - most supervised deep learning applications require large amounts of labeled training data whereas reinforcement learning algorithms must be able to learn from scalar rewards that are often delayed.

Immediately after the plagiarized section is a citation to the paper which was plagiarized (Mnih et al.).