Artificial intelligence is one of the most exciting technology in the 21st century. Artificial intelligence, aimed at creating humanlike intelligence and human intelligence, including perception, decision-making and cognitive (from intuition to reasoning, planning, awareness, etc.). Among them, the perception resolve what, deep learning has gone beyond the human level; decisions to solve how, reinforcement learning achieved some success in the field of gaming and robotics; cognitive solve is studying why, mapping knowledge, causal reasoning and continuous learning. Reinforcement learning, learning by way of feedback sequence of decisions to solve the problem, and therefore must be universal access to artificial intelligence ultimate key.
Courses and video
Reinforcement Learning by David Silver (2015) [homepage] [youtube] [bilibili]
CS 188: Introduction to Artificial Intelligence [Fall 2012-Spring 2014] [Fall 2018] [Summer 2019] [Spring 2020]
CS 294: Deep Reinforcement Learning by Sergey Levine [Fall 2015] [Spring 2017] [Fall 2017] [Fall 2018]
CS 285: Deep Reinforcement Learning [Fall 2019] [youtube]
Advanced Deep Learning & Reinforcement Learning by DeepMind & UCL [youtube2018]
Deep Reinforcement Learning and Control [Spring 2017]
CS234: Reinforcement Learning [Winter 2019] [youtube]
Deep RL Bootcamp [August 2017]
Deep Reinforcement Learning by 李宏毅 [Spring 2018] [yourube2018]
Reinforcement Learning by 莫烦 [homepage]
books
Reinforcement Learning: An Introduction (1st Edition, 1998) [homepage]
Reinforcement Learning: An Introduction (2nd Edition, 2018) [homepage] [bookdraft2018jan1] [2018] [Python Code] [中文翻译]
Hands-On Reinforcement Learning With Python (2018) [homepage]
Reinforcement Learning With Open AI TensorFlow and Keras Using Python (2018) [homepage]
Algorithms for Reinforcement Learning (2010) [download]
"Neural networks and deep learning" [download]
Code
ShangtongZhang/Python Implementation of Reinforcement Learning: An Introduction (2nd Edition) [github]
berkeleydeeprlcourse [github]
tensorlayer/RLzoo github
rlcode/reinforcement-learning [github]
MorvanZhou/Reinforcement-learning-with-tensorflow [github]
dennybritz/reinforcement-learning [github]
Course
Spinning Up OpenAI [English] [Chinese version]
Speech
Rich Sutton, 2015, Introduction to Reinforcement Learning with Function Approximation
Andrew Barto, 2018, A history of reinforcement learning
David Silver, Principles of Deep RL
Benjamin Recht, 2018, Optimization Perspectives on Learning to Control
John Schulman, 2017, The Nuts and Bolts of Deep Reinforcement Learning Research
Joelle Pineau, Introduction to Reinforcement Learning
Deep Learning and Reinforcement Learning Summer School, 2018, 2017
Deep Learning Summer School, 2016, 2015
Yisong Yue and Hoang M. Le, Imitation Learning, ICML 2018 Tutorial
Overview
Li, Y. (2017). Deep Reinforcement Learning: An Overview. ArXiv. [paper]
Littman, M. L. (2015). Reinforcement learning improves behaviour from evaluative feedback. Nature, 521:445–451. [paper]
Kaelbling, L., Littman, M., and Moore, A. (1996). Reinforcement learning: A survey.Journalof Artificial Intelligence Research, 4:237–285. [paper]
algorithm
surroundings
Gym OpenAI
Google Dopamine 2.0
Emo Todorov Mujoco
common grid world class environment
frame
OpenAI Baselines
Baidu PARL
DeepMind OpenSpiel
researcher
Richard S. Sutton [homepage]
David Silver [homepage]
Pieter Abbeel [homepage]
Sergey Levine [homepage]
李宏毅 [homepage]
Conference / journal
会议: AAAI, NIPS, ICML, ICLR, IJCAI, AAMAS, IROS 等.
Journal: AI, JMLR, JAIR, Machine Learning, JAAMAS and so on.
Research institute
OpenAI
DeepMind
Berkeley Artificial Intelligence Research (BAIR) Lab
Blog
Keavnn’Blog
Medium : Reinforcement Learning
StackOverflow : Reinforcement Learning
Know almost
Lecture reinforcement learning knowledge
intelligence unit
reinforcement learning
No public
The depth of reinforcement learning laboratory
deep learning cutting edge of technology
AI Technology Review
New Ji-won
other
kmario23/deep-learning-drizzle [github] [webpage]
Mr.Jk.Zhang [CSDN]
