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Deep Reinforcement Learning and Game Optimization

  1. 0

    Cartpole: The “Hello World” of Reinforcement Learning

    In this article, you will be up and running, and will have done your first piece of reinforcement learning.
    Added 26 Jun 2020
  2. 1

    Cartpole: Tweaking the Options

    In this article, we will see what’s going on behind the scenes and what options are available for changing the reinforcement learning.
    Added 26 Jun 2020
  3. 2

    Introduction to OpenAI Gym: Atari Breakout

    In this article, we start to look at the OpenAI Gym environment and the Atari game Breakout.
    Added 29 Jun 2020
  4. 3

    Learning Breakout More Quickly

    In this article, we will see how you can use a different learning algorithm (plus more cores and a GPU) to train much faster on the mountain car environment.
    Added 30 Jun 2020
  5. 4

    Learning Breakout From RAM – Part 1

    In this article we will learn from the contents of the game’s RAM instead of the pixels.
    Added 2 Jul 2020
  6. 5

    Learning Breakout From RAM – Part 2

    In this article, we will see how we can improve by approaching the RAM in a slightly different way.
    Added 3 Jul 2020
  7. 6

    Learning Breakout: Advanced Topics

    In this final article in this series, we will look at slightly more advanced topics: minimizing the "jitter" of our Breakout-playing agent, as well as performing grid searches for hyperparameters.
    Added 6 Jul 2020