Abstract
We give a short introduction to reinforcement learning. This includes basic concepts like Markov decision processes, policies, state-value and action-value functions, and the Bellman equation. We discuss solution methods like policy and value iteration methods, online methods like temporal-difference learning, and state fundamental convergence results.
It turns out that RL addresses the problems from Chap. 2. This shows that, in principle, RL is a suitable instrument for solving all of these problems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bertsekas, D.P., Tsitsiklis, J.N.: Neuro-Dynamic Programming. Athena Scientific, Belmont (1996)
Munos, R.: A study of reinforcement learning in the continuous case by the means of viscosity solutions. Mach. Learn. 40, 265–299 (2000)
Paprotny A.: Hierarchical methods for the solution of dynamic programming equations arising from optimal control problems related to recommendation. Diploma Thesis, TU Hamburg-Harburg (2010)
Sutton, R.S., Barto, A.G.: Reinforcement Learning. An Introduction. MIT Press, Cambridge/London (1998)
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Paprotny, A., Thess, M. (2013). Changing Not Just Analyzing: Control Theory and Reinforcement Learning. In: Realtime Data Mining. Applied and Numerical Harmonic Analysis. Birkhäuser, Cham. https://doi.org/10.1007/978-3-319-01321-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-01321-3_3
Published:
Publisher Name: Birkhäuser, Cham
Print ISBN: 978-3-319-01320-6
Online ISBN: 978-3-319-01321-3
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)