강좌: A Complete Reinforcement Learning System (Capstone). 되돌아가려면 여기을 클릭하세요.

- 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

- 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
- 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