REINFORCE algorithm- unable to compute gradients on latest toolbox version
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Bhooshan V
el 4 de Abr. de 2022
Comentada: Bhooshan V
el 6 de Abr. de 2022
The LSTM actor network inputs 50 timestep data of three states. Therefore a state is of dimension 3x50.
For computing gradients, the input data in the forllowing format
num_states x batchsize x N_TIMESTEPS = (3x1)x50x50.
In Reinforcement Learning toolbox version 1.3, the following line works perfectly.
% actor- the custom actor network , actorLossFunction- custom loss fn, lossData- custom variable
actorGradient = gradient(actor,@actorLossFunction,{reshape(observationBatch,[3 1 50 50])},lossData);
However, when I run the same code in the latest RL toolbox version 2.2, I get the following error:
------------------------------------------------------------------------------------------------------------------------------------------------------
Error using rl.representation.rlAbstractRepresentation/gradient
Unable to compute gradient from representation.
Error in simpleRLTraj (line 184)
actorGradient= gradient(actor,@actorLossFunction,{reshape(observationBatch,[3 1 50 50])},lossData);
Caused by:
Error using extractBinaryBroadcastData
dlarray is supported only for full arrays of data type double, single, or logical, or for full gpuArrays of
these data types.
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I tried tracing back to the error but it get more complicated. How do I get an error for a code that works perfectly on the earlier version of RL toolbox?
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Joss Knight
el 5 de Abr. de 2022
Editada: Joss Knight
el 5 de Abr. de 2022
What is
underlyingType(observationBatch)
underlyingType(lossData)
?
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