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Experiment of observation conditioned decoder #6
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Thank you for your kind words!
Yes, to ensure a fair comparison we used same hyperparameters as the ones reported in Appendix B1. In our current implementation we construct one token per observation timestep in stage 2, so for this ablation we just append that to the skill tokens from the encoder and let the decoder cross attend to all tokens (obs+skill tokens) combined.
Can you confirm if this is during stage 0,1 or 2 training? Also by increase do you mean it's blowing up to some very high value? |
Thank you for your answering !
For various experiments, I conditioned the decoder on observations during the stage 0 training process, and I ended up with results that contradicted those obtained using the provided code. Additionally, when I used this pretrained autoencoder in stage 1, the success rate was nearly zero. There could be multiple reasons behind this, but I wanted to ask if you might have any insights into possible causes. |
Something feels off. Could you please elaborate more on what results exactly contradicted? If success rate is zero, you should checkout the rollout videos, by default it should save some in the evaluation run directory, or else try setting n_video param under rollout in eval config. |
Thank you for your great work!
I'm conducting various experiments to condition the decoder on observations.
In your ablation study for the observation-conditioned decoder, were all hyperparameters same with the released code? Also, how were the observation tokens constructed?
In some conditioning experiments, I've observed cases where the autoencoder's grad_norm increases. Could this indicate potential issues with training?
Congratulations on having your paper accepted at a top conference!
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