Dynamic Programming | Free Reinforcement Learning Course Module 4

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In module 4 we're going to cover some of the basic theory of dynamic programming. This is a model based class of algorithms for solving reinforcement learning problems, by iteratively solving the Bellman equation.

We'll cover policy evaluation, policy improvement, and value iteration as solutions to the Bellman equation.

We also have our first homework assignment, for which I'll provide the solution in module 5.

#reinforcementlearning #artificialintelligence #dynamicprogramming

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This content is sponsored by my Udemy courses. Level up your skills by learning to turn papers into code. See the links in the description.

As promised here are the time stamps for the algorithms for your assignment.
Policy Evaluation 02:47
Policy Iteration 03:58
Value Iteration 04:45

MachineLearningwithPhil

Nice video!! When to use value iteration and when policy iteration? What are the advantages and disadvantages?

stefanherbek

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