Reinforcement learning is hot right now! Policy gradients and deep q learning can only get us so far, but what if we used two networks to help train and AI instead of one? Thats the idea behind actor critic algorithms. I'll explain how they work in this video using the 'Doom" shooting game as an example.
Code for this video:
https://github.com/llSourcell/actor_critic
i-Nickk's winning code:
https://github.com/I-NicKK/Tic-Tac-Toe
Vignesh's runner up code:
https://github.com/tj27-vkr/Q-learning-conv-net
Taryn's Twitter:
https://twitter.com/tarynsouthern
More learning resources:
https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf
http://rll.berkeley.edu/deeprlcourse/f17docs/lecture_5_actor_critic_pdf.pdf
http://web.mit.edu/jnt/www/Papers/J094-03-kon-actors.pdf
http://mlg.eng.cam.ac.uk/rowan/files/rl/06_actorcritic.pdf
http://mi.eng.cam.ac.uk/~mg436/LectureSlides/MLSALT7/L5.pdf
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