Let's teach our AI how to get from point A to point B of a Frozen Lake environment in the most efficient way possible using dynamic programming. This is considered reinforcement learning and we'll trying two popular techniques (policy iteration and value iteration). We'll use OpenAI's Gym environment and pure python to do this.
Code for this video:
https://github.com/llSourcell/navigating_a_virtual_world_with_dynamic_programming
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More learning resources:
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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