EPISODE · Nov 23, 2017 · 20 MIN
Navigating a Virtual World Using Dynamic Programming
from Siraj Raval
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 Please Subscribe! And like. And comment. That's what keeps me going. Want more inspiration & education? Connect with me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology 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 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
What this episode covers
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 Please Subscribe! And like. And comment. That's what keeps me going. Want more inspiration & education? Connect with me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology 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 Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
NOW PLAYING
Navigating a Virtual World Using Dynamic Programming
No transcript for this episode yet