Abstract
Dynamic Programming (DP) has been widely used as an approach solving the Markov Decision Process problem. This paper takes a well-known gambler’s problem as an example to compare different DP solutions to the problem, and uses a variety of parameters to explain the results in detail. Ten C++ programs were written to implement the algorithms. The numerical results from gamble’s problem and graphical output from the tracking car problem support the conceptual definitions of RL methods.
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Richard S. Sutton and Andrew G. Barto (1998), Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA.
Martin L. Puterman (1994), Markov Decision Processes: Discrete Stochastic Dynamic Programming. A Wiley-Interscience Publication, New York.
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© 2005 International Federation for Information Processing
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Li, C., Pyeatt, L. (2005). A Short Tutorial on Reinforcement Learning. In: Shi, Z., He, Q. (eds) Intelligent Information Processing II. IIP 2004. IFIP International Federation for Information Processing, vol 163. Springer, Boston, MA. https://doi.org/10.1007/0-387-23152-8_63
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DOI: https://doi.org/10.1007/0-387-23152-8_63
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-23151-8
Online ISBN: 978-0-387-23152-5
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