The Internet of things lives! More and more devices are coming prepackaged with internet access that wouldn’t normally be. That includes everything from smart salt shakers to smart tabletops. And because these devices have a connection to the world wide web, they can communicate with the outside world and each other, sharing information and even learning from one another. In this video, I'll explain how to use a reinforcement learning technique called "Monte Carlo" to optimize electricity consumption and cooling demands for a smart home. Enjoy!
Code for this video:
https://github.com/llSourcell/Internet_of_Things_Optimization
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Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus
More learning resources:
https://www.kth.se/social/files/58b941d5f276542843812288/RL04-Monte-Carlo.pdf
https://courses.cs.washington.edu/courses/csep573/12au/lectures/18-rl.pdf
https://stat.ethz.ch/education/semesters/ss2016/seminar/files/slides/RL_MCM_heinzer_profumo.pdf
https://medium.com/deep-math-machine-learning-ai/ch-12-1-model-free-reinforcement-learning-algorithms-monte-carlo-sarsa-q-learning-65267cb8d1b4
http://www-edlab.cs.umass.edu/cs689/lectures/RL%20Lecture%205.pdf
https://github.com/dennybritz/reinforcement-learning/tree/master/MC
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