Reinforcement Learning

References

• http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
• https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PL7-jPKtc4r78-wCZcQn5IqyuWhBZ8fOxT
• http://videolectures.net/rldm2015_silver_reinforcement_learning/?q=david%20silver
• https://webdocs.cs.ualberta.ca/~sutton/book/the-book.html
• http://web.mit.edu/dimitrib/www/RLbook.html
• https://sites.ualberta.ca/~szepesva/RLBook.html
• http://banditalgs.com/print/
• http://karpathy.github.io/2016/05/31/rl/
• http://cs229.stanford.edu/notes/cs229-notes12.pdf
• http://cs.stanford.edu/people/karpathy/reinforcejs/index.html
• https://www.udacity.com/course/machine-learning-reinforcement-learning–ud820
• http://www.nature.com/nature/journal/v518/n7540/full/nature14236.html
• http://people.csail.mit.edu/regina/my_papers/TG15.pdf
• In http://karpathy.github.io/2015/05/21/rnn-effectiveness: For more about REINFORCE and more generally Reinforcement Learning and policy gradient methods (which REINFORCE is a special case of) David Silver's class, or one of Pieter Abbeel's classes. This is very much ongoing work but these hard attention models have been explored, for example, in Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets, Reinforcement Learning Neural Turing Machines, and Show Attend and Tell.
• In http://www.deeplearningbook.org/contents/ml.html: Please see Sutton and Barto (1998) or Bertsekasand Tsitsiklis (1996) for information about reinforcement learning, and Mnih et al.(2013) for the deep learning approach to reinforcement learning.