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[Coursera] Fundamentals of Reinforcement Learning

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What you’ll learn

  • Formalize issues as Markov Determination Processes

  • Perceive primary exploration strategies and the exploration / exploitation tradeoff

  • Perceive worth capabilities, as a general-purpose instrument for optimum decision-making

  • Know find out how to implement dynamic programming as an environment friendly answer strategy to an industrial management drawback

Description

Reinforcement Studying is a subfield of Machine Studying, however can also be a common goal formalism for automated decision-making and AI. This course introduces you to statistical studying strategies the place an agent explicitly takes actions and interacts with the world. Understanding the significance and challenges of studying brokers that make selections is of important significance as we speak, with an increasing number of corporations focused on interactive brokers and clever decision-making.

This course introduces you to the basics of Reinforcement Studying. Once you end this course, you’ll: – Formalize issues as Markov Determination Processes – Perceive primary exploration strategies and the exploration/exploitation tradeoff – Perceive worth capabilities, as a general-purpose instrument for optimum decision-making – Know find out how to implement dynamic programming as an environment friendly answer strategy to an industrial management drawback This course teaches you the important thing ideas of Reinforcement Studying, underlying basic and fashionable algorithms in RL. After finishing this course, it is possible for you to to begin utilizing RL for actual issues, the place you’ve gotten or can specify the MDP. That is the primary course of the Reinforcement Studying Specialization.

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