2016 ReinforcementRenaissance

• (Krakovsky, 2016) ⇒ Marina Krakovsky. (2016). “Reinforcement Renaissance.” In: Communications of the ACM Journal, 59(8). doi:10.1145/2949662

Subject Headings: Reinforcement Neural Learning.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Reinforcement+Renaissance
• http://dl.acm.org/citation.cfm?id=2975594.2949662&preflayout=flat#citedby

Quotes

Abstract

The power of deep neural networks has sparked renewed interest in reinforcement learning, with applications to games, robotics, and beyond.

Body

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The two types of learning — reinforcement learning and deep learning through deep neural networks — complement each other beautifully, says Sutton. “ Deep learning is the greatest thing since sliced bread, but it quickly becomes limited by the data, " he explains. “ If we can use reinforcement learning to automatically generate data, even if the data is more weakly labeled than having humans go in and label everything, there can be much more of it because we can generate it automatically, so these two together really fit well. "

Despite the buzz around DeepMind, combining reinforcement learning with neural networks is not new. TD-Gammon, a backgammon-playing program developed by IBM's Gerald Tesauro in 1992, was a neural network that learned to play backgammon through reinforcement learning (the TD in the name stands for Temporal-Difference learning, still a dominant algorithm in reinforcement learning). “ Back then, computers were 10,000 times slower per dollar, which meant you couldn't have very deep networks because those are harder to train, " says Jürgen Schmidhuber, a professor of artificial intelligence at the University of Lugano in Switzerland who is known for seminal contributions to both neural networks and reinforcement learning. “ Deep reinforcement learning is just a buzzword for traditional reinforcement learning combined with deeper neural networks, " he says.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 ReinforcementRenaissance	Marina Krakovsky			Reinforcement Renaissance				10.1145/2949662		2016