Practical Reinforcement Learning

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  • Intro: why should I care?
    • In this module we are 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.
  • Model-free methods
    • 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.
  • Policy-based methods
    • 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.
  • Exploration
    • 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.