Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
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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.