Deep Learning - Reinforcement Learning Part 5

In the last video on reinforcement learning, we look into the deep reinforcement learning techniques. We start looking into how Deep Mind beat Atari Games and in particular breakout. Furthermore, we look into the technology behind AlphaGo and AlphaGoZero to play Go, Chess, and Shogi on world-class level.

Links
Link to Sutton's Reinforcement Learning in its 2018 draft, including Deep Q learning and Alpha Go details

Video References:
Breakout Example 1
Breakout Example 2
AlphaGo Lee Sedol Match 3
AlphaGo Lee Sedol Match 4

Further Reading:
A gentle Introduction to Deep Learning

References
[1] David Silver, Aja Huang, Chris J Maddison, et al. “Mastering the game of Go with deep neural networks and tree search”. In: Nature 529.7587 (2016), pp. 484–489.
[2] David Silver, Julian Schrittwieser, Karen Simonyan, et al. “Mastering the game of go without human knowledge”. In: Nature 550.7676 (2017), p. 354.
[3] David Silver, Thomas Hubert, Julian Schrittwieser, et al. “Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm”. In: arXiv preprint arXiv:1712.01815 (2017).
[4] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, et al. “Human-level control through deep reinforcement learning”. In: Nature 518.7540 (2015), pp. 529–533.
[5] Martin Müller. “Computer Go”. In: Artificial Intelligence 134.1 (2002), pp. 145–179.
[6] Richard S. Sutton and Andrew G. Barto. Introduction to Reinforcement Learning. 1st. Cambridge, MA, USA: MIT Press, 1998.