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Markov Decision Processes

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May 1, 2024 Updated June 5, 2025 28 minute read

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Markov Decision Processes: A Comprehensive Guide to Sequential Decision Making Under Uncertainty

Markov Decision Processes (MDPs) offer a powerful mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. At a high level, an MDP helps an agent (which could be a robot, a software program, or even a human) figure out the best sequence of actions to take in an environment to achieve a specific goal, especially when the results of those actions are not entirely predictable. This framework is a cornerstone of reinforcement learning, a vibrant area of artificial intelligence, and has found applications in diverse fields ranging from robotics to finance and healthcare.

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Sorted from most relevant to least relevant:

Decision Making and Reinforcement Learning

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Fundamentals of Reinforcement Learning

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Artificial Intelligence: Reinforcement Learning in Python

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Data Science Decisions in Time: Using Data Effectively

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Introduction to Deep Reinforcement Learning

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Special Topics in Deep Reinforcement Learning

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Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Markov Decision Processes.

Markov Decision Processes

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Provides a comprehensive overview of Markov decision processes (MDPs), including theory, algorithms, and applications. It is especially useful for readers who are interested in the mathematical foundations of MDPs and their use in solving complex decision-making problems.

Markov Decision Processes: Discrete Stochastic...

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Markov Decision Processes: Discrete Stochastic...

Kindle Edition

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Reinforcement Learning, second edition

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Provides a comprehensive introduction to reinforcement learning, which type of machine learning that is used to solve sequential decision-making problems. It covers the basics of reinforcement learning, as well as more advanced topics such as deep reinforcement learning and multi-agent reinforcement learning.

Reinforcement Learning, second edition: An...

Hardcover

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Reinforcement Learning, second edition: An...

Kindle Edition

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Value-Based Planning for Teams of Agents in...

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Provides a Bayesian perspective on Markov decision processes, which allows for the incorporation of uncertainty into the decision-making process. It is especially useful for readers who are interested in using MDPs to solve problems in which there is uncertainty about the state of the world.

Value-Based Planning for Teams of Agents in...

Paperback

$$$

An Introduction to Stochastic Modeling

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Provides an introduction to Markov decision processes (MDPs) and their applications in engineering. It covers the basics of MDPs, as well as more advanced topics such as reinforcement learning and planning.

An Introduction to Stochastic Modeling

Hardcover

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An Introduction to Stochastic Modeling

Kindle Edition

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Stationary Processes and Discrete Parameter Markov...

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Provides a comprehensive overview of Markov decision processes (MDPs) in discrete time. It covers the basics of MDPs, as well as more advanced topics such as reinforcement learning and planning.

Stationary Processes and Discrete Parameter Markov...

Hardcover

Markov Decision Processes

Markov Decision Processes: A Comprehensive Guide to Sequential Decision Making Under Uncertainty

Path to Markov Decision Processes

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