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rlpython

2020-01-06 | |  83 |   0 |   0

Introduction to Reinforcement Learning in Python

Course materials I have used for a 12 lecture course on Reinforcement Learning and Game Theory.

Using environments from OpenAI Gym

Topics covered (as of early 2018):

  • Markov Decision Processes.

  • Dynamic Programming.

  • Monte Carlo Learning.

  • Temporal Difference Learning (Q-Learning, Sarsa).

  • Function approximation methods / Deep Q-Learning.

  • Policy approximation via black-box optimization.

  • Actor-critic methods.

  • Zero-sum games. Regret matching and fictitious play.

Some great resources:

No time for the full David Silver's course? Try:

For the applications in online advertising (retargeting) check Nicholas Le Roux talk


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