Welcome to the Reinforcement Learning course.
Here you will find out about:
– foundations of RL methods: value/policy iteration, q-learning, policy gradient, etc.
— with math & batteries included
– using deep neural networks for RL tasks
— also known as “the hype train”
– state of the art RL algorithms
— and how to apply duct tape to them for practical problems.
– and, of course, teaching your neural network to play games
— because that’s what everyone thinks RL is about. We’ll also use it for seq2seq and contextual bandits.
Jump in. It’s gonna be fun!
Do you have technical problems? Write to us: firstname.lastname@example.org
Intro: why should I care?
In this module we gonna define and "taste" what reinforcement learning is about. We'll also learn one simple algorithm that can solve reinforcement learning problems with embarrassing efficiency.
At the heart of RL: Dynamic Programming
This week we'll consider the reinforcement learning formalisms in a more rigorous, mathematical way. You'll learn how to effectively compute the return your agent gets for a particular action - and how to pick best actions based on that return.
This week we'll find out how to apply last week's ideas to the real world problems: ones where you don't have a perfect model of your environment.
Approximate Value Based Methods
This week we'll learn to scale things even farther up by training agents based on neural networks.
We spent 3 previous modules working on the value-based methods: learning state values, action values and whatnot. Now's the time to see an alternative approach that doesn't require you to predict all future rewards to learn something.
In this final week you'll learn how to build better exploration strategies with a focus on contextual bandit setup. In honor track, you'll also learn how to apply reinforcement learning to train structured deep learning models.