Learn how to frame reinforcement learning problems, tackle classic examples, explore basic algorithms from dynamic programming, temporal difference learning, and progress towards larger state space using function approximation and DQN (Deep Q Network).
Reinforcement Learning (RL) is an area of machine learning, where an agent learns by interacting with its environment to achieve a goal.
In this course, you will be introduced to the world of reinforcement learning. You will learn how to frame reinforcement learning problems and start tackling classic examples like news recommendation, learning to navigate in a grid-world, and balancing a cart-pole.
You will explore the basic algorithms from multi-armed bandits, dynamic programming, TD (temporal difference) learning, and progress towards larger state space using function approximation, in particular using deep learning. You will also learn about algorithms that focus on searching the best policy with policy gradient and actor critic methods. Along the way, you will get introduced to Project Malmo, a platform for Artificial Intelligence experimentation and research built on top of the Minecraft game.
What you will learn
- Reinforcement Learning Problem
- Markov Decision Process
- Dynamic Programming
- Temporal Difference Learning
- Approximate Solution Methods
- Policy Gradient and Actor Critic
- RL that Works