Understanding Algorithms for Reinforcement Learning from Pluralsight

Reinforcement learning is a type of machine learning which allows decision makers to operate in an unknown environment. In the world of self-driving cars and exploring robots, RL is an important field of study for any student of machine learning.

Traditional machine learning algorithms are used for predictions and classification. Reinforcement learning is about training agents to take decisions to maximize cumulative rewards. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. First, you'll discover the objective of reinforcement learning; to find an optimal policy which allows agents to make the right decisions to maximize long-term rewards. You'll study how to model the environment so that RL algorithms are computationally tractable. Next, you'll explore dynamic programming, an important technique used to cache intermediate results which simplify the computation of complex problems. You'll understand and implement policy search techniques such as temporal difference learning (Q-learning) and SARSA which help converge on to an optimal policy for your RL algorithm. Finally, you'll build reinforcement learning platforms which allow study, prototyping, and development of policies, as well as work with both Q-learning and SARSA techniques on OpenAI Gym. By the end of this course, you should have a solid understanding of reinforcement learning techniques, Q-learning and SARSA and be able to implement basic RL algorithms.

What's inside

Syllabus

Course Overview

Understanding the Reinforcement Learning Problem

Implementing Reinforcement Learning Algorithms

Using Reinforcement Learning Platforms

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops RL algorithms, which are core skills for computer science

Emphasizes the objective of maximizing cumulative rewards, a foundational concept of RL

Teaches Q-learning, a popular algorithm for RL

Examines dynamic programming, an important technique for solving RL problems

Provides hands-on experience through building RL platforms in OpenAI Gym

Covers the taxonomy of RL, providing a comprehensive understanding of the field

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Understanding Algorithms for Reinforcement Learning with these activities:

Review Basics of Dynamic Programming

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Refreshing your knowledge of dynamic programming will provide a stronger foundation for understanding reinforcement learning, which utilizes dynamic programming techniques.

Browse courses on Dynamic programming

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Review lecture notes or online resources
Solve practice problems and exercises
Discuss the concepts with peers or a mentor

Review Sutton and Barto's 'Reinforcement Learning'

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Reading and reviewing this foundational textbook will provide a comprehensive understanding of the principles and algorithms of reinforcement learning.

View Reinforcement Learning, second edition: An... on Amazon

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Acquire a copy of the book
Read and take notes on each chapter
Complete the exercises and review questions
Discuss the concepts with peers or a mentor

Follow Tutorials on Temporal-Difference Learning

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Following tutorials on temporal-difference learning will provide a structured approach to understanding and implementing this important RL technique.

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Identify reputable online tutorials
Follow the tutorials step-by-step
Experiment with different parameters
Replicate the results and apply the techniques

Five other activities

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Discuss Policy Search Techniques

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Engaging in discussions with peers will foster critical thinking and enhance your understanding of policy search techniques in reinforcement learning.

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Prepare by reviewing course materials on policy search
Join a study group or forum
Actively participate in discussions
Share your own insights and knowledge

Implement Q-Learning Algorithm

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Implementing the Q-Learning algorithm will solidify your understanding of its mechanics and strengthen your practical programming skills in this domain.

Browse courses on Q-Learning

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Understand Q-Learning concepts and theory
Gather environment information and define states
Define actions and calculate rewards
Iterate through episodes to train the algorithm
Evaluate performance and adjust parameters

Build a Reinforcement Learning Simulation

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Building a reinforcement learning simulation will provide hands-on experience in designing, implementing, and evaluating reinforcement learning algorithms.

Browse courses on Reinforcement Learning

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Define the simulation environment and objectives
Choose and implement a reinforcement learning algorithm
Train and evaluate the algorithm in the simulation
Analyze results and refine the model

Write a Blog Post on Reinforcement Learning

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Creating a blog post will encourage you to synthesize and articulate your understanding of reinforcement learning, solidifying your knowledge.

Browse courses on Reinforcement Learning

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Choose a specific topic within reinforcement learning
Research and gather information
Write a draft and organize your content
Refine and edit your writing
Publish and share your blog post

Contribute to a Reinforcement Learning Open-Source Project

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Contributing to an open-source project will provide hands-on experience in applying your reinforcement learning knowledge and collaborating with others.

Browse courses on Reinforcement Learning

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Identify an open-source project related to reinforcement learning
Review the project's documentation and codebase
Identify an area where you can contribute
Create a pull request with your contribution
Collaborate with the project maintainers to refine your contribution

Career center

Learners who complete Understanding Algorithms for Reinforcement Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers use their expertise in reinforcement learning to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Software Engineer.

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Research Scientist

Research Scientists use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Research Scientist.

See salaries and explore the career path for Research Scientist

Data Analyst

Data Analysts use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Quantitative Analyst.

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Business Analyst

Business Analysts use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Business Analyst.

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Product Manager

Product Managers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Product Manager.

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Consultant

Consultants use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Consultant.

See salaries and explore the career path for Consultant

Financial Analyst

Financial Analysts use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Marketing Manager

Marketing Managers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Marketing Manager.

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Sales Manager

Sales Managers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Sales Manager.

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Project Manager

Project Managers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as a Project Manager.

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Operations Manager

Operations Managers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course can help you build a foundation for a successful career as an Operations Manager.

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Human Resources Manager

Human Resources Managers use their expertise in reinforcement learning algorithms to create self-driving cars and explore robots. Reinforcement learning is a type of machine learning that allows decision-makers to operate in an unknown environment. In this course, Understanding Algorithms for Reinforcement Learning, you'll learn basic principles of reinforcement learning algorithms, RL taxonomy, and specific policy search techniques such as Q-learning and SARSA. This course may help you build a foundation for a successful career as a Human Resources Manager.

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