Comments (5)
Hi SynecFD,
Have you looked at the results obtained with the VAE? In particular, have you checked that the images generated by the VAE looks like game images?
If the VAE is working correctly and you kept the same hyperparameters than ours for the controller, the training of the controller with CMA should work fine, whatever your hardware is.
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Hi leonardblier,
I also thought about that and checked the sample images and they do look very much like the game. To make sure I retrained the VAE a couple of times. The best I could get was a test loss of about 31, which seemed high at first, but I don't have much to compare these numbers to anything and I know an AutoEncoder can never be perfect anyway.
I left the hyperparameters exactly the same, just decreased the amount of workers. Maybe a new version of pytorch or pycma breaks it? I do get warnings that some functions are deprecated when starting the controller.
I could try to rerun everything with an older version and see if that fixes it.
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Hi SynecFD,
I am quite sure the pipeline worked with the hyperparameters in the README : --n-samples 4 --pop-size 4
, but it was a long time ago.
If you want to try with other hyperparameter, the last time I trained it (for additional experiments), I used --n-samples 16 --pop-size 64
and it worked perfectly well (on a recent pytorch version). It will be slower, but you can try it. I reached 800 in about 50 epochs.
If this works better, I will change the README.
The number of workers will have no effect on the training (except on the time per epoch).
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You are completely right! With those hyperparameters it is working now.
Like you said it's slow, but after 8 hours of training I'm already at 500. I consider this issue solved now. Thank you very much.
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Which hyperparameters are you using to get 500? --n-samples 4 --pop-size 4
or --n-samples 16 --pop-size 64
? And how many epoch do you train the controller ?
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Related Issues (20)
- Shouldn't this be outside the for loop? HOT 3
- Data generation script: No module named 'utils' HOT 3
- Different transform in trainvae.py & trainmdrnn.py HOT 2
- sleep(0.1) leads to infty loops HOT 3
- inconsisent MDRNN / MDRNNCell behavoir HOT 3
- Error training MD-rnn HOT 3
- Splitting of Train and Validation / Test set HOT 1
- one question about gmm_loss function HOT 1
- Possible error when predicting next action (class RolloutGenerator) HOT 7
- a multi-process problem in the controller
- The definition of GMM linear layer may wrong? Or I have missed something? HOT 4
- Multiprocessing very slow HOT 2
- issue about gmm_loss HOT 2
- Controller Input HOT 1
- the train_controller always break off when trainning about 15min
- MDRNN losses extremely low due to numerical instability?
- problem about training VAE
- MDRNN doesn't train properly on carracing?
- Worker dying issue with controller training
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