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Matalb Academy - Reinforcement Learning Onramp: submission failed

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Isuru Jayarathne
Isuru Jayarathne on 15 Mar 2021
Commented: Matt Tearle on 7 Apr 2021
I am taking the course titled Reinforcement Learning Onramp on MATLAB Academy and everything works fine until section 3.6 (Creating Default Agent Representations).
When I submit my answer, the system showed the given answer is wrong.
Then I tried the code from the solution, but the result was the same.
So, I cannot proceed from here.
Screenshot has been attached.

Answers (1)

Matt Tearle
Matt Tearle on 18 Mar 2021
There was a change in R2021a that caused an incompatibility. We have a fix ready that will go out with the next update to the training course content. In the meantime, use this workaround to enable you to keep working through the Onramp:
layers = [
imageInputLayer([28 28 1],'Name','input','Normalization',"none")
averagePooling2dLayer(2,'Stride',2,'Name','avpool1')
averagePooling2dLayer(2,'Stride',2,'Name','avpool2')
averagePooling2dLayer(2,'Stride',2,'Name','avpool3')
fullyConnectedLayer(2,'Name','fc','Weights',zeros(2,9),'Bias',zeros(2,1))
softmaxLayer('Name','softmax')
classificationLayer('Name','classOutput',"Classes",["a" "b"])];
lgraph = layerGraph(layers)
actnet = assembleNetwork(lgraph)
  8 Comments
Matt Tearle
Matt Tearle on 7 Apr 2021
Those are just different kinds of neural network layers. It's probably worth clarifying that this is a temporary hack to fool the grading code until the incompatibility is resolved in the next release - these are not layers you'd actually use in practice (in this application). They are layers more commonly used in deep learning for a task like image classification.
As an aside, there are cool RL applications that combine deep learning for images with RL, such as playing video games or controlling a robot/vehicle from video input (eg self-driving cars).

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