For convenience. Original: However reinforcement learning presents several challenges from a deep learning perspective. Firstly, most successful deep learnin

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.).