Learn how to utilize algorithms for reward-based learning, as part of Reinforcement Learning with R.
Master key concepts and practical skills through structured learning modules. By completing this curriculum, you'll gain valuable expertise applicable to real-world scenarios.
Understand and Implement the "Grid World" Problem in R
Utilize the Markov Decision Process and Bellman equations
Get to know the key terms in Reinforcement Learning
Dive into Temporal Difference Learning, an algorithm that combines Monte Carlo methods and dynamic programming
Take your Machine Learning skills to the next level with RL techniques
This comprehensive Reinforcement Learning with R: Algorithms-Agents-Environment curriculum is designed to take you from foundational concepts to advanced implementation. Each module builds upon the previous, ensuring a structured learning path that maximizes knowledge retention and practical application.
Understand what you need to succeed in this course and determine if it's the right fit for your learning goals
What you need before starting this Reinforcement Learning with R: Algorithms-Agents-Environment course:
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Everything you need to know about this online course, from duration to certification
Course Instructor
Packt Publishing
Expert instructor with industry experience
Course Language
English
All materials in English
This online course offers comprehensive training with expert instruction, practical exercises, and a certificate of completion. Join thousands of students advancing their careers through quality online education.
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