Comments (2)
ctallec
commented on July 19, 2024
One possibility would be that it is much easier for the MDN-RNN to model a low-dimensional latent space than a high dimensional one. The scaling is only designed to make losses comparable, but won't compensate an easier or a harder task. In the original paper, they did not have this scaling factor because they did not model the reward signal, which we did by default in a previous version of the code, but no longer do. When there is no reward modelling contribution, RMSprop should directly negate the constant multiplicative factor that differs between the two losses.
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Fairasname
commented on July 19, 2024
Alright, thanks for the clarification!
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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
- Training the controller and getting stuck in local minima HOT 5
- 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
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