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IJulia notebooks for AA228/CS238 Decision Making Under Uncertainty course at Stanford University
The (excellent) bandits notebook plots learning curves, but I think there is a mistake in the code.
To plot a conventional learning curve, I think this line:
Line 82 in a33ec9d
Should just be
wins[step] = win
For some reason the latex()
wrapped strings don't show up for me. I have temporarily removed them in bandits.jl
This problem has been fixed in the function 'banditTrial' in bandits.jl, however I wasn't sure how to fix the problem in the function 'banditEstimation'!
In the online POMDP notebook, BasicPOMCP does a pretty terrible job (because of the large action space. Someone should switch it to using ARDESPOT
Right now, in the grid world problem description, it says "We receive a cost of 1 for bumping against the outer border of the grid.", but in the problem implementation, it appears that a deterministic reward of -0.7 is hard coded when an action towards the edge is taken from an edge state (this is E[reward(s,a,sp)|s,a]).
Since, if a wall is bumped, the state remains the same, it is certainly possible to implement what the problem description actually says with reward(s,a,sp) by checking whether the state remains the same. This makes visualization difficult though.
A simpler alternative would be to say that a cost of 1 is accrued when an action that corresponds to moving out of bounds is taken. That way, the true reward can be captured with reward(s,a).
Can I change to a reward(s,a) formulation, or do we want to work through the difficulties associated with the current reward(s,a,sp) description?
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