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Error encountered on 'Unreal Stacked Lstm example' #80
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@JacobHanouna ,
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Thanks for the tip. I will use it :)
OK you are right this is not PPO related. I get the same error also when using the Tried also using
All the errors happen only if I change to the above settings on the strategy. |
@JacobHanouna, as traceback says, it failed to load saved model weights to new graph; usually appears when you have changed tf.graph definition (like switched from PPO to A3C or did some other alterations to graph) and tried to load previously saved model. Discard old checkpoints and start from scratch. |
@Kismuz |
what it tries restoring than? have you got clean manual env. run with strategy settings altered your way?
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You were right, probably didn't clear that result. using
can you share
after commenting out this line I get a different error |
it's here, update BTgym:
well, time to manually check environment run. |
I ran it manually but no error |
@JacobHanouna , can you share exact env. setup code so I can replicate a error? |
All env setting are the one used in 'unreal example'. only changed the strategy a bit as follows:
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@JacobHanouna,
ugly, I know - i'll try to address it in future; |
OK Thanks :) |
@Kismuz, I came across another issue while playing around with this example. Under the
I checked the code and on |
@JacobHanouna, |
I encountered an error and I'm trying to figure if it is wrong setting on my end or possible bug.
I took the 'Unreal Stacked Lstm example' and set the trainer to work with PPO.
on the strategy params I made the following changes:
I'm experimenting with
skip_frame
andtime_dim
to try and compare two models that are trained with different time frames. with the following settings:First model -
one year data of 1 min resolution, skip_frame ~ one hour (60 frames), time_dim ~ 2 hours (or above)
Second model
one year data of 1 hour resolution, skip_frame ~ one hour (1 frame), time_dim ~ 120 hours (or above)
With this setting the models are making decisions at the same time (every hour) but after learning 2 different representation of the same data.
I'm curious in seeing if the '1 min model' can learn also the representation of the '1 hour model'
I get this error only after changing to the above values (no error on the default values: skip_frame=10, time_dim=30, avg_period=20)
this is the error:
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