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Notes for the Reinforcement Learning course by David Silver along with implementation of various algorithms.
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Week 1 - Intro To RL REN: rename folders Feb 11, 2018
Week 2 - Markov Decision Process REN: rename folders Feb 11, 2018
Week 3 - Planning by Dynamic Programming REN: rename folders Feb 11, 2018
Week 4 - Model Free Prediction DEL: remove notebook Feb 12, 2018
Week 5 - Model Free Control DOC: update README Feb 17, 2018
Week 6 - Value Function Approximations DOC: update README Feb 19, 2018
Week 7 - Policy Gradient Methods
lib NEW: added monte carlo prediction Feb 12, 2018



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This repository contains the notes for the Reinforcement Learning course by David Silver along with the implementation of the various algorithms discussed, both in Keras (with TensorFlow backend) and OpenAI's gym framework.


  • Week 1: Introduction to Reinforcement Learning [slide][video]

  • Week 2: Markov Decision Processes [slide][video]

  • Week 3: Planning by Dynamic Programming [slide][video]

  • Week 4: Model-Free Prediction [slide][video]

  • Week 5: Model-Free Control [slide][video]

  • Week 6: Value Function Approximation [slide][video]

  • Week 7: Policy Gradient Methods [slide][video]

  • Week 8: Integrating Learning and Planning [slide][video]

  • Week 9: Exploration and Exploitation [slide][video]

  • Week 10: Case Study: RL in Classic Games [slide][video]


  • TensorFlow
  • Keras
  • Gym
  • Numpy

Install them using pip.


Please feel free to create a Pull Request for adding implementations of the algorithms discussed in different frameworks like PyTorch, Caffe, etc. or improving the existing implementations. If you are a beginner, you can refer this for getting started.


If you found this useful, please consider starring(★) the repo so that it can reach a broader audience.


This project is licensed under the MIT License - see the LICENSE file for details.


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