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A Complete Reinforcement Learning System (Capstone)

Approx. 16 hours to complete

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Course Summary

This course teaches students how to build a complete reinforcement learning system from scratch, including the algorithms, environments, and agents. You will learn the fundamental concepts and techniques of reinforcement learning and apply them to real-world scenarios.

Key Learning Points

Learn how to build a complete reinforcement learning system from scratch
Understand the fundamental concepts and techniques of reinforcement learning
Apply reinforcement learning to real-world scenarios

Job Positions & Salaries of people who have taken this course might have

- USA: $112,000
- India: ₹1,500,000
- Spain: €42,000
- USA: $112,000
- India: ₹1,500,000
- Spain: €42,000
- USA: $96,000
- India: ₹950,000
- Spain: €34,000
- USA: $112,000
- India: ₹1,500,000
- Spain: €42,000
- USA: $96,000
- India: ₹950,000
- Spain: €34,000
- USA: $130,000
- India: ₹2,000,000
- Spain: €50,000

Learning Outcomes

Build a complete reinforcement learning system from scratch
Apply reinforcement learning algorithms to real-world scenarios
Understand the fundamental concepts and techniques of reinforcement learning

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Familiarity with machine learning concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced
Video lectures
Hands-on exercises

Similar Courses

Practical Reinforcement Learning
Advanced Machine Learning

Related Education Paths

Related Books

Description

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.

Outline

Welcome to the Final Capstone Course!
Course 4 Introduction
Meet your instructors!
Reinforcement Learning Textbook
Pre-requisites and Learning Objectives

Milestone 1: Formalize Word Problem as MDP
Initial Project Meeting with Martha: Formalizing the Problem
Andy Barto on What are Eligibility Traces and Why are they so named?
Let's Review: Markov Decision Processes
Let's Review: Examples of Episodic and Continuing Tasks

Milestone 2: Choosing The Right Algorithm
Meeting with Niko: Choosing the Learning Algorithm
Let's Review: Expected Sarsa
Let's Review: What is Q-learning?
Let's Review: Average Reward- A New Way of Formulating Control Problems
Let's Review: Actor-Critic Algorithm
Csaba Szepesvari on Problem Landscape
Andy and Rich: Advice for Students

Milestone 3: Identify Key Performance Parameters
Agent Architecture Meeting with Martha: Overview of Design Choices
Let's Review: Non-linear Approximation with Neural Networks
Drew Bagnell on System ID + Optimal Control
Susan Murphy on RL in Mobile Health
Impact of Parameter Choices in RL

Milestone 4: Implement Your Agent
Meeting with Adam: Getting the Agent Details Right
Let's Review: Optimization Strategies for NNs
Let's Review: Expected Sarsa with Function Approximation
Let's Review: Dyna & Q-learning in a Simple Maze
Meeting with Martha: In-depth on Experience Replay
Martin Riedmiller on The 'Collect and Infer' framework for data-efficient RL

Milestone 5: Submit Your Parameter Study!
Meeting with Adam: Parameter Studies in RL
Let's Review: Comparing TD and Monte Carlo
Joelle Pineau about RL that Matters
Meeting with Martha: Discussing Your Results
Course Wrap-up
Specialization Wrap-up

Summary of User Reviews

Learn the complete system of Reinforcement Learning with this course on Coursera. Users have given high praise for the course's thoroughness and practical application. One key aspect that many users appreciate is the hands-on approach to learning, which allows for a deeper understanding of the concepts involved.

Pros from User Reviews

Thorough and comprehensive course material
Hands-on approach to learning
Practical applications of Reinforcement Learning
Engaging and knowledgeable instructors
Good pacing and structure of lessons

Cons from User Reviews

Requires some prior knowledge of machine learning
Some users found the course challenging
Lack of interaction with instructors
No certificate of completion for audit users
Not suitable for beginners in the field of machine learning

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