We’ll learn how to solve the multi-armed bandit problem (maximizing success for a given slot machine) using a reinforcement learning technique called policy gradients.
Code for this video:
https://github.com/llSourcell/how_to_win_slot_machines
Mike’s winning code:
https://github.com/xkortex/Siraj_Chatbot_Challenge
Vishal’s runner up code:
https://github.com/erilyth/DeepLearning-Challenges/tree/master/Text_Based_Chatbot
this coding challenge was really close, so i’m also going to put code for 3rd place just this time (Eibriel):
https://github.com/Eibriel/ice-cream-truck
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More Learning resources:
http://karpathy.github.io/2016/05/31/rl/
http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html
http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html
http://kvfrans.com/simple-algoritms-for-solving-cartpole/
View at Medium.com
https://dataorigami.net/blogs/napkin-folding/79031811-multi-armed-bandits
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http://wizards.herokuapp.com/
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https://www.patreon.com/user?u=3191693