A Complete Reinforcement Learning System (Capstone)

A Complete Reinforcement Learning System (Capstone)

This course is part of Reinforcement Learning Specialization

Instructors: Martha White

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6 modules

Gain insight into a topic and learn the fundamentals.

651 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

6 modules

Gain insight into a topic and learn the fundamentals.

651 reviews

Intermediate level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

2 assignments

Taught in English

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This course is part of the Reinforcement Learning Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

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There are 6 modules in this course

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems.

Welcome to the final capstone course of the Reinforcement Learning Specialization!!

What's included

2 videos2 readings1 discussion prompt

This week you will read a description of a problem, and translate it into an MDP. You will complete skeleton code for this environment, to obtain a complete MDP for use in this capstone project.

What's included

4 videos1 programming assignment

This week you will select from three algorithms, to learn a policy for the environment. You will reflect on and discuss the appropriateness of each algorithm for this environment.

What's included

7 videos1 assignment

7 videos Total 40 minutes

Meeting with Niko: Choosing the Learning Algorithm 3 minutes
Let's Review: Expected Sarsa 4 minutes
Let's Review: What is Q-learning? 3 minutes
Let's Review: Average Reward- A New Way of Formulating Control Problems 10 minutes
Let's Review: Actor-Critic Algorithm 5 minutes
Csaba Szepesvari on Problem Landscape 9 minutes
Andy and Rich: Advice for Students 5 minutes

1 assignment

Choosing the Right Algorithm 0 minutes

This week you will identify key parameters that affect the performance of your agent. The goal is to understand the space of options, to later enable you to choose which parameter you will investigate in-depth for your agent.

What's included

4 videos1 assignment

This week, you will implement your agent using Expected Sarsa or Q-learning with RMSProp and Neural Networks. To use NNs, you will have to use a more careful stepsize selection strategy, which is why you will use RMSProp. You will also verify the correctness of your agent.

What's included

6 videos1 programming assignment

6 videos Total 29 minutes

Meeting with Adam: Getting the Agent Details Right 5 minutes
Let's Review: Optimization Strategies for NNs 5 minutes
Let's Review: Expected Sarsa with Function Approximation 2 minutes
Let's Review: Dyna & Q-learning in a Simple Maze 5 minutes
Meeting with Martha: In-depth on Experience Replay 5 minutes
Martin Riedmiller on The 'Collect and Infer' framework for data-efficient RL 7 minutes

1 programming assignment Total 480 minutes

Implement Your Agent 480 minutes

This week you will identify a parameter to study, for your agent. Once you select the parameter to study, we will provide you with a range of values and specific values for other parameters. You will write a script to run your agent and environment on the set of parameters, to determine performance across these parameters. You will gain insight into the impact of parameters on agent performance. You will also get to visualize the agents that you learn. Your parameter study will consist of an array of values that we will check for correctness.

What's included

6 videos1 programming assignment

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Instructors

Instructor ratings

(118 ratings)

Martha White

University of Alberta

4 Courses 113,897 learners

Adam White

University of Alberta

4 Courses 113,897 learners

Offered by

University of Alberta

Alberta Machine Intelligence Institute

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Reviewed on Apr 17, 2020

The project seems to be complicated at first glance, but the notebook will guide you through the implementation, and you will know what you are doing eventually.

Reviewed on Aug 2, 2020

One of the most amazing set of courses that I have ever been through. This neither makes the stuff look difficult nor does it compromise on quality, absolutely the best.

Reviewed on Jul 10, 2020

Strongly recommend this course to others. The project could be a little more challenging though. Thanks, Martha, Adam, and RAs, for your good teaching!

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