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Reinforcement Learning Discussion

This week Edward Balaban will lead an introductory discussion on reinforcement learning (RL). Reinforcement learning is a class of algorithms that falls in-between supervised and unsupervised learning. Whereas a supervised learning algorithm is trained on examples of inputs with explicit labels and an unsupervised learning algorithm tries to identify patterns in the data, an RL algorithm learns from the consequences of its interactions with the environment. Many types of complex processes (such as those in biological systems, telecommunications, or financial markets) can be modeled through reinforcement learning.

This discussion is intended to be informal and interactive. Some background in machine learning is desired, but not required. Edward will go through the motivations and mathematical foundations of reinforcement learning, followed by a few application examples.

Bio: Edward Balaban is a research engineer at NASA Ames, working on aircraft and spacecraft system health management.

Some background on reinforcement learning:

http://webdocs.cs.ualberta.ca/~sutton/book/ebook/the-book.html
http://www.cs.indiana.edu/~gasser/Salsa/rl.html
http://en.wikipedia.org/wiki/Reinforcement_learning


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