Markov Decision Processes | Free Reinforcement Learning Course Module 2

#reinforcementlearning #artificialintelligence

Welcome to module 2 of our free course in reinforcement learning.

Today we’re going to discover what a Markov Decision Process is, and how this relates to reinforcement learning. In a nutshell, processes that have the Markov property are amenable to a mathematical framework that makes calculating expected future rewards straightforward.

We’ll get back to the Bellman equation, and see how it relates to the Markov property.

In tomorrow’s module, we’ll handle the explore exploit dilemma, so that we’re set up to start solving the Bellman equation using dynamic programming.

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-OCT-21

Actor Critic Methods:
https://www.udemy.com/course/actor-critic-methods-from-paper-to-code-with-pytorch/?couponCode=AC-OCT-21

Curiosity Driven Deep Reinforcement Learning
https://www.udemy.com/course/curiosity-driven-deep-reinforcement-learning/?couponCode=ICM-OCTOBER-21

Natural Language Processing from First Principles:
https://www.udemy.com/course/natural-language-processing-from-first-principles/?couponCode=NLP1-OCT-21Reinforcement Learning Fundamentals
https://www.manning.com/livevideo/reinforcement-learning-in-motion

Here are some books / courses I recommend (affiliate links):
Grokking Deep Learning in Motion: https://bit.ly/3fXHy8W
Grokking Deep Learning: https://bit.ly/3yJ14gT
Grokking Deep Reinforcement Learning: https://bit.ly/2VNAXql

Come hang out on Discord here:
https://discord.gg/Zr4VCdv

Website: https://www.neuralnet.ai
Github: https://github.com/philtabor
Twitter: https://twitter.com/MLWithPhil

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