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Machine Learning - University of British Columbia

www.cs.ubc.ca

One way to formalize the problem is as function approximation. We assume y = f(x) for some unknown function f, and the goal of learning is to estimate the function f given a labeled training set, and then to make predictions using yˆ = fˆ(x). (We use the hat symbol to denote an estimate.)

  Learning, Functions, Approximation, Function approximation

Reinforcement Learning: An Introduction - Inspiring …

www.csee.umbc.edu

The Reinforcement Learning Problem II. Elementary Solution Methods 4. Dynamic Programming 5. Monte Carlo Methods 6. Temporal-Difference Learning III. A Unified View 7. Eligibility Traces 8. Generalization and Function Approximation 9. Planning and Learning 10. Dimensions of Reinforcement Learning 11. Case Studies

  Introduction, Learning, Functions, An introduction, Reinforcement, Approximation, Reinforcement learning, Function approximation

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Learning, Function Approximation, Function, Reinforcement Learning: An Introduction, Reinforcement Learning