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mdps so now I'm going to go into policy gradient methods which are one uh Broad
learning okay so that's uh that's just the that's it for the formalism of
and general class of reinforcement learning methods which are um quite effective so to give a brief overview of this
solving mdps these are sometimes called dynamic programming algorithms because they're actually quite related to the kind of dynamic programming algorithms
much of our world can be models mdps how much when when you wake up in the morning and making breakfast how do do you think of yourself as an mdp and so
how do you think about mdps and how they relate to our world well so there's a stance question right so a stance is a position that I take with respect to a
these are um able to ex exactly solve um mdps efficiently in uh the setting where
uncertainty of the world some really Force the world to be deterministic and so certainly mdps uh model the uncertainty of the
it first and abstraction in games is much trickier than abstraction in mdps
You have to actually go and, like, read MDPs, you know, read some math, Bellman equations, dynamic programming, model-based, model.
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