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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