Watch the lectures from DeepMind research lead David Silver's course on reinforcement learning, taught at University College London.

[Video lectures]

• Lecture 1: Introduction to Reinforcement Learning
• Lecture 2: Markov Decision Processes
• Lecture 3: Planning by Dynamic Programming
• Lecture 4: Model-Free Prediction
• Lecture 5: Model-Free Control
• Lecture 6: Value Function Approximation
• Lecture 7: Policy Gradient Methods
• Lecture 8: Integrating Learning and Planning
• Lecture 9: Exploration and Exploitation
• Lecture 10: Case Study: RL in Classic Games

Kai Arulkumaran, Marc Peter Deisenroth, Miles Brundage, Anil Anthony Bharath

[Paper]

Educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). It includes the following resources:

• a short introduction to RL terminology, kinds of algorithms, and basic theory,
• an essay about how to grow into an RL research role,
• a curated list of important papers organized by topic,
• a well-documented code repo of short, standalone implementations of key algorithms,
• and a few exercises to serve as warm-ups.

[Webpage]

Lecture Series. Stanford CS234: Reinforcement Learning (Winter 2019) - with Prof. Emma Brunskill

[YouTube]

Vincent Francois-Lavet, Peter Henderson, Riashat Islam, Marc G. Bellemare, Joelle Pineau

[PDF Book manuscript, Nov 2018]

By Pieter Abbeel, Chelsea Finn, Peter Chen, Andrej Karpathy et al.

[Webpage]

Written by Richard Sutton and Andrew Barto.

[Webpage] [PDF] [Goodreads]

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course. All code is written in Python 3 and uses RL environments from OpenAI Gym. Advanced techniques use Tensorflow for neural network implementations.

[GitHub]