Reinforcement Learning

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About this learning path

Reinforcement Learning

Machine learning paradigm concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward.

It comes from

  • ↑ Machine Learning
  • includes the learning paths

  • - Dynamic Programming
  • and can be deepened into

  • ↓ Deep Reinforcement Learning
  • Web resources
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    Flexible schedule
    Set and adhere to flexible deadlines.

    Approx. 4 months to complete
    Recommended 12 hours/week

    Mostly English


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    Reinforcement Learning yet!

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    You go station by station. In each station you'll find resources containing:

    1. Information

    2. Educational content

    3. Places to exchange infos

    4. Applications, Tools

    ( 5. Entertaining content )

    After studying a resource, you check it.

    Between stations, sometimes there are learning paths. They need to be studied as well as they pose a qualification for upcoming content. After station 1, the learning path for Dynamic Programming needs to be studied.

    After finishing this learning path, you can specialize further or apply what you learned in another learning path.

    Learning Path






    Hidden Markov Models Model


    Markov Decision Process


    Gym Library


    Reinforcement Learning Lectures


    Reinforcement learning


    Reinforcement Learning: An Introduction Non-fictional




    Markov Chain Monte Carlo Method


    Reinforcement Learning State-of-the-Art Non-fictional


    Algorithms for Reinforcement Learning