About this Course

Reinforcement learning (RL) is an area of machine learning concerned with how agents take actions in an environment so as to maximize some notion of cumulative reward. In real word applications, RL is about the optimal control of an autonomous system. Reinforcement learning is considered as one of three machine learning paradigms, alongside supervised learning and unsupervised learning.

Main Contents

The main contents for this level is as follows:

Part 1

Introduction to Reinforcement Learning

Value and Policy Iteration

Prediction Problems

Policy Control Problems

Improvements to DQN


Part 2

Policy Gradient Methods