A Complete Reinforcement Learning System (Capstone) 

Course 4 Introduction

Meet your instructors!

Reinforcement Learning Textbook

Pre-requisites and Learning Objectives

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

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

Choosing the Right Algorithm

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

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

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