강의 계획서

1. Welcome

   1. Introduction

   2. Applications of Reinforcement Learning

   3. Overview of Modules and Labs

   4. Pre-course Survey

2. Introduction to Reinforcement Learning

   1. What is Reinforcement Learning

   2. Comparisons

   3. Elements of RL

   4. Lab

   5. Knowledge Checks

3. Bandits

   1. Bandits Framework

   2. Regret Minimization

   3. Bridge to Reinforcement Learning

   4. Lab

   5. Knowledge Checks

4. The Reinforcement Learning Problem

   1. Agent and Environment Interface

   2. Markov Decision Process

   3. Lab

   4. Knowledge Checks

5. Dynamic Programming

   1. Basics of Dynamic Programming

   2. DP Observations

   3. Lab

   4. Knowledge Checks

6. Temporal Difference Learning

   1. Policy Evaluation

   2. Policy Optimization

   3. Lab

   4. Knowledge Checks

7. Function Approximation

   1. Why Use Function Approximation

   2. Linear Function Approximation

   3. Lab

   4. RL with Deep Neural Networks

   5. Lab

   6. Extensions to Deep Q-Learning

   7. Knowledge Checks

8. Policy Gradient and Actor Critic

   1. Introduction to Policy Optimization

   2. Likelihood Ratio Methods

   3. Lab

   4. Variance Reduction

   5. Lab

   6. Actor Critic

   7. Lab

   8. Knowledge Checks

9. Post-Course Survey

   1. Post-Course Survey

10. Get Microsoft Course Completion Certificate

    1. Instructions