In this course, you will :
- Build a Reinforcement Learning system for sequential decision making..
- Understand the space of RL algorithms (Temporal- Difference learning, Monte Carlo, Sarsa, Q-learning, Policy Gradients, Dyna, and more)..
- Understand how to formalize your task as a Reinforcement Learning problem, and how to begin implementing a solution..
- Understand how RL fits under the broader umbrella of machine learning, and how it complements deep learning, supervised and unsupervised learning.