Matalb Academy - Reinforcement Learning Onramp: submission failed

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Isuru Jayarathne
Isuru Jayarathne 2021 年 3 月 15 日
回答済み: Pavankumar Patil 2021 年 8 月 29 日
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.

回答 (2 件)

Matt Tearle
Matt Tearle 2021 年 3 月 18 日
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 件のコメント
Matt Tearle
Matt Tearle 2021 年 4 月 7 日
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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Pavankumar Patil
Pavankumar Patil 2021 年 8 月 29 日
Use the sim function to run multiple simulations of the model, using the simulation options in opts.The environment and RL agent are stored in the variables env and agent, respectively.save the result of the simulation to a variable called simout.

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