Sample-based Learning Methods from Coursera

In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment---learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.

By the end of this course you will be able to:

- Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience

- Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model

- Understand the connections between Monte Carlo and Dynamic Programming and TD.

- Implement and apply the TD algorithm, for estimating value functions

- Implement and apply Expected Sarsa and Q-learning (two TD methods for control)

- Understand the difference between on-policy and off-policy control

- Understand planning with simulated experience (as opposed to classic planning strategies)

- Implement a model-based approach to RL, called Dyna, which uses simulated experience

- Conduct an empirical study to see the improvements in sample efficiency when using Dyna

What's inside

Syllabus

Welcome to the Course!

Welcome to the second course in the Reinforcement Learning Specialization: Sample-Based Learning Methods, brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, and get a flavour of what the course has in store for you. Make sure to introduce yourself to your classmates in the "Meet and Greet" section!

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Examines model-based and model-free approaches to reinforcement learning

Teaches foundational concepts in RL, including TD learning, Monte Carlo methods, and Dyna

Provides hands-on experience implementing TD and Q-learning algorithms

Taught by experienced RL researchers with a strong track record in the field

Focuses on practical applications, with examples from simulated domains

Assumes some prior knowledge of RL concepts and Python programming

Reviews summary

Foundational sample-based reinforcement learning

According to learners, this course offers a positive and challenging exploration of sample-based reinforcement learning methods including Monte Carlo, Temporal Difference learning, Q-learning, and Dyna. Many students highlight the clear explanations that help build a strong theoretical foundation in these core algorithms. While the course is seen as demanding and requires significant effort, particularly with the coding assignments, reviewers consistently describe the assignments as essential for gaining practical insight and solidifying understanding. Overall, it's considered a valuable part of the specialization for those seeking a deep dive into the fundamentals.

Requires significant time and background.

"This course requires a lot of effort and dedication."

"It's a rigorous and challenging course; be prepared for a steep learning curve."

"Requires a solid understanding of prerequisite knowledge from the prior course."

"Not recommended if you are looking for an easy overview; it's a deep dive."

Assignments deepen understanding through implementation.

"Assignments are hard but they really make you understand the concepts deeply."

"I learned the most from tackling the coding assignments; they make the theory concrete."

"Requires significant effort on the assignments, but it's absolutely essential."

"The assignments are very challenging but ultimately rewarding and key to solidifying learning."

Concepts explained clearly and in depth.

"Provides a really solid foundation on TD and MC methods from first principles..."

"The concepts are explained clearly and build well on prior material."

"I got a deep understanding of the core algorithms discussed."

"Excellent explanation of theory behind the algorithms like Q-learning and Sarsa."

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 Sample-based Learning Methods with these activities:

Review basic probability and statistics concepts

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Refresh your knowledge of probability and statistics to strengthen your foundation for understanding reinforcement learning concepts.

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Review probability distributions
Practice solving basic statistical problems

Review basic linear algebra concepts

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Refresh your knowledge of linear algebra to enhance your understanding of the mathematical underpinnings of reinforcement learning.

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Review vector spaces and matrices
Practice solving systems of linear equations

Participate in online discussion forums

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Engage in discussions with peers to clarify concepts, share insights, and get feedback on your understanding of the course material.

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Join online discussion forums
Participate in discussions by asking questions
Provide thoughtful responses to others' questions

Four other activities

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Practice coding with Q-learning and Expected Sarsa

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Practice implementing and applying temporal difference learning algorithms to better understand how they work and reinforce your understanding of the concepts.

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Implement the Q-learning algorithm
Implement the Expected Sarsa algorithm
Test both algorithms on a simple environment

Compile a list of resources for further learning

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Gather and organize a collection of resources such as articles, videos, or websites that can supplement your learning and provide additional insights into the course topics.

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Search for relevant resources
Organize and categorize the resources
Share the compilation with others

Explore applications of Monte Carlo and Temporal Difference Learning

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Research and learn about practical applications of Monte Carlo and Temporal Difference Learning to gain a broader perspective on their usefulness and potential impact.

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Review real-world applications of Monte Carlo methods
Explore case studies and examples of Temporal Difference Learning in practice

Create a simulation-based RL project

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Develop a project that demonstrates your understanding of simulation-based RL techniques and showcases your ability to apply them to solve a real-world problem.

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Define the problem and environment
Design and implement the simulation
Apply RL algorithms to the simulated environment
Evaluate and analyze the results

Career center

Learners who complete Sample-based Learning Methods will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models for a wide range of applications. These models can be used to automate tasks, improve decision-making, and gain insights from data. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that are efficient, accurate, and robust.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists use their knowledge of statistics, machine learning, and data analysis to extract insights from data. They work in a variety of industries, including finance, healthcare, and retail. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to solve complex problems and make better decisions.

See salaries and explore the career path for Data Scientist

Software Engineer

Software Engineers design, develop, and maintain software applications. They work in a variety of industries, including finance, healthcare, and technology. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop software applications that are efficient, reliable, and user-friendly.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. They work in a variety of financial institutions, including banks, hedge funds, and asset management companies. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to identify investment opportunities and make better decisions.

See salaries and explore the career path for Quantitative Analyst

Operations Research Analyst

Operations Research Analysts use mathematical and statistical models to solve complex problems in a variety of industries, including logistics, manufacturing, and healthcare. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to improve efficiency, reduce costs, and make better decisions.

See salaries and explore the career path for Operations Research Analyst

Business Analyst

Business Analysts use their knowledge of business processes and data analysis to help organizations improve their performance. They work in a variety of industries, including finance, healthcare, and manufacturing. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to improve efficiency, reduce costs, and make better decisions.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers design, build, and maintain data pipelines that collect, store, and process data. They work in a variety of industries, including finance, healthcare, and technology. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to improve the efficiency and accuracy of data pipelines.

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Product Manager

Product Managers are responsible for the development and launch of new products and features. They work in a variety of industries, including technology, finance, and healthcare. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and launch products that meet the needs of users and achieve business goals.

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Risk Analyst

Risk Analysts identify, assess, and mitigate risks for organizations. They work in a variety of industries, including finance, insurance, and healthcare. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to identify and mitigate risks.

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Consultant

Consultants provide advice and guidance to organizations on a variety of topics, including business strategy, operations, and technology. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to solve complex problems and make better decisions.

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Researcher

Researchers conduct original research in a variety of fields, including science, engineering, and medicine. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to solve complex problems and make new discoveries.

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Teacher

Teachers educate students in a variety of subjects, including science, math, and social studies. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deliver lesson plans that are engaging and effective.

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Policy Analyst

Policy Analysts develop and evaluate policies for governments and organizations. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and evaluate policies that are effective and evidence-based.

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Financial Analyst

Financial Analysts analyze financial data to make investment recommendations. They work in a variety of financial institutions, including banks, hedge funds, and asset management companies. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to identify investment opportunities and make better decisions.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical models to assess and manage risk for insurance companies and other financial institutions. The University of Alberta's Sample-based Learning Methods course can provide you with the skills you need to succeed in this role by teaching you about the fundamental concepts of machine learning, including temporal difference learning, Monte Carlo methods, and model-based reinforcement learning. This knowledge will help you to develop and deploy machine learning models that can be used to assess and manage risk more effectively.

See salaries and explore the career path for Actuary