Markov decision problems
Miquel Noguer i Alonso, Daniel Bloch and David Pacheco Aznar
Markov decision problems
Preface
Introduction
Markov decision problems
Learning the optimal policy
Reinforcement learning revisited
Temporal difference learning revisited
Stochastic approximation in Markov decision processes
Large language models: reasoning and reinforcement learning
Deep reinforcement learning
Applications of artificial intelligence in finance
Pricing options with temporal difference backpropagation
Pricing American options
Daily price limits
Portfolio optimisation
Appendix
2.1 PRESENTATION OF THE PROBLEM(S)
2.1.1 Decision-making problems
In mathematics, decision problems typically appear in questions of decidability (that is, the question of the existence of an effective method to determine the existence of some object or its membership in a set).11 An effective method is a procedure for solving a problem by any intuitively effective means from a specific class of problems. It consists of a finite number of exact, finite instructions. It terminates after a finite number of steps and must produce a correct answer. Hence, one method may be effective with respect to one class of problems but not with respect to another one. An effective method for calculating the values of a function is an algorithm. Some of the most important problems in mathematics are undecidable. In computability theory, a decision problem is a problem that can be posed as a yes–no question of the input values (on a possibly infinite set). This field categorises decidable decision problems by how difficult they are to solve in terms of the computational resources needed for computation (Kroening and Strichman 2016). Decision problems are closely related to function problems, which can
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