/Deep_Reinforcement_Learning

Resources, papers, tutorials

Deep Reinforcement Learning

Introduction to Reinforcement Learning with David Silver, DeepMind

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

Deep Reinforcement Learning: A Brief Survey

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

[Paper]

Spinning Up in Deep RL

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]

Stanford CS234: Reinforcement Learning

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

[YouTube]

An Introduction to Deep Reinforcement Learning (2018)

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

[PDF Book manuscript, Nov 2018]

Deep RL Bootcamp, Berkeley (2017)

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

[Webpage]

Reinforcement Learning Book

Written by Richard Sutton and Andrew Barto.

[Webpage] [PDF] [Goodreads]

Denny Britz: Reinforcement Learning

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]