Learn Blackjack Video Source & Info:
#OpenAIGym #ReinforcementLearning
If you had to bet your life savings on a game of blackjack, would you end up homeless?
In today’s installment of reinforcement learning in the OpenAI Gym, we’re going to use Monte Carlo control without exploring starts to teach an artificial intelligence to play the game of blackjack.
It works reasonably well, though probably not as well as Q learning or even SARSA would. Nevertheless, Monte Carlo methods are an important part of reinforcement learning and are therefore important to understand to get a full picture of artificial intelligence.
Learn how to turn deep reinforcement learning papers into code:
Deep Q Learning:
https://www.udemy.com/course/deep-q-learning-from-paper-to-code/?couponCode=DQN-JUN-20-1
Actor Critic Methods:
https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-JUN-1
Reinforcement Learning Fundamentals
https://www.manning.com/livevideo/reinforcement-learning-in-motion
Come hang out on Discord here:
https://discord.gg/Zr4VCdv
Website: https://www.neuralnet.ai
Github: https://github.com/philtabor
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Source: YouTube
For lines 16 – 26 would it be possible to use numpy.zeros_like or numpy.zeros somehow? I really liked what you did with recreating your own argmax to randomly break a tie rather than just using the existing argmax function.
Blackjack baby!!!
oh nice!
could u share this code?
I'm encountering an error but have no idea why