Can we train an AI to complete it's objective in a video game world without needing to build a model of the world before hand? The answer is yes using Q learning! I'll go through several use cases and show some python code of how Q learning works.
Code for this video:
https://github.com/llSourcell/Q_Learning_Explained/
Adnan's Winning code:
https://github.com/AdnanZahid/ReinforcementLearning
Alberto's runner up code:
https://github.com/alberduris
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More learning resources:
http://mnemstudio.org/path-finding-q-learning-tutorial.htm
https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf
http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf
https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html
http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html
https://www.quora.com/How-is-policy-iteration-different-from-value-iteration
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf
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