The resources
Resources related to Deep Reinforcement Learning
The Blogs
- StudyWolf - Reinforcement learning part 1: Q-learning and exploration
- StudyWolf - Deep Learning Posts
- DeepMind - Deep Reinforcement Learning
- Demystifying Deep Reinforcement Learning
- Deep Reinforcement Learning: Pong from Pixels
- An overview of gradient descent optimization algorithms
The Books
- Reiforcement Learning - Richard Sutton
The Tutorials
The Videos
- Deep Learning Lecture 15: Deep Reinforcement Learning - Policy search
- Deep Reinforcement Learning (John Schulman, OpenAI)
- Bay Area Deep Learning School Day 2 at CEMEX auditorium, Stanford
- LM101-044: What happened at the Deep Reinforcement Learning Tutorial at the 2015 Neural Information Processing Systems Conference?
- John Schulman lectures on DRL
The Guys
The Courses
- CS 294: Deep Reinforcement Learning, Spring 2017 (Berkeley)
- CS294 Reddit discussions
- CS294-129 Designing, Visualizing and Understanding Deep Neural Networks
- CS 294-131: Special Topics in Deep Learning
- Deep Reinforcement Learning Workshop, NIPS 2015
- RL Course by David Silver (Youtube)
- UCL Course on RL by David Silver
- CS231n: Convolutional Neural Networks for Visual Recognition
- Nervana's Deep Learning Course
- BDU: Deep Learning 101
- BDU: Deep Learning with TensorFlow
- Udacity: Deep Learning by Google
The Software
- rllab (framework for developing and evaluating reinforcement learning algorithms)
- Keras - 'Abstraction layer' over Theano & TensorFlow
- Theano - Python library for multi-dimensional array computations
- Software compilation - Deeplearning.net
- Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
- OpenAI Gym - A toolkit for developing and comparing reinforcement learning algorithms
The Related Stuff
The Frameworks
The deep learning ecosystem has evolved a lot since then. Supposedly a new deep learning toolkit was released once every 22 days in 2015. Amongst the popular ones are both the old-timers like Theano, Torch7 and Caffe, as well as the newcomers like Neon, Keras and TensorFlow. New algorithms are getting implemented within days of publishing.