Introduction to Reinforcement Learning
In this video I give a brief introduction to Reinforcement Learning.
reinforcement-learning code notebook video tutorial

This notebook provides a brief introduction to reinforcement learning, eventually ending with an exercise to train a deep reinforcement learning agent with the dopamine framework.

The notebook is roughly organized as follows:

  1. A simple motivating example is presented to illustrate some of the main points and challenges.
  2. Markov decision processes (MDPs), the mathematical formalism used to express these problems, is introduced.
  3. Exact tabular methods are presented, which can be used when the environment is fully known. These methods form the foundation for the learning algorithms in uncertain environments.
  4. Value-based learning algorithms are introduced, and related algorithms are also presented.
  5. The methods introduced are put to the test on a simple MDP.
  6. Deep reinforcement learning is introduced, along with some implementation details.
  7. We use deep reinforcement learning to learn a policy on a larger MDP.
  8. Resources for further study are provided.

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señor swesearcher at Google Brain. Musician.
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