Deep reinforcement learning: Difference between revisions

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=== Deep learning ===

[[File:Neural_network_example.svg|thumb|241x241px|Depiction of a basic artificial neural network]]

[[Deep learning]] is a form of [[machine learning]] that utilizes a neural network to transform a set of inputs into a set of outputs via an [[artificial neural network]]. Deep learning methods, often using [[supervised learning]] with labeled datasets, have been shown to solve tasks that involve handling complex, high-dimensional raw input data (such as images,) with less manual [[feature engineering]] than prior methods, enabling significant progress in several fields including [[computer vision]] and [[natural language processing]]. In the past decade, deep RL has achieved remarkable results on a range of problems, from single and multiplayer games such as [[Go (game)|GO]], [[Atari Games]], and [[Dota 2]], to robotics.<ref>{{Cite web |last=Graesser |first=Laura |title=Foundations of Deep Reinforcement Learning: Theory and Practice in Python |url=https://openlibrary.telkomuniversity.ac.id/home/catalog/id/198650/slug/foundations-of-deep-reinforcement-learning-theory-and-practice-in-python.html |access-date=2023-07-01 |website=Open Library Telkom University}}</ref>

=== Reinforcement learning ===