Hi @ajwagen, thanks a lot for open sourcing the code!
I'm trying to reproduce the results for the ablation study "Does the quality of data πdp is trained on affect DSRL?". In my fork of the repo, I slightly modified the process_robomimic_dataset.py file to allow the creation of train.npz files for data splits from the Robomimic MH datasets. I've also created YAML (pre_diffusion_mlp_worse.yaml) files with the same configurations as in the robomimic can YAMLs to pretrain and eval $\pi_\texttt{dp}$.
However, I always see
[agent.eval.eval_diffusion_agent][INFO] - eval: num episode 120 | success rate 0.000 | avg episode reward 0.000 | avg best reward 0.000
for various training configurations, and surprisingly, data splits (better, okay, worse, worse_okay).
Can you please direct me on how to reproduce those results by providing the required YAML and NPZ files, the $\pi_\texttt{dp}$ checkpoints or clearer guidelines on how to run those experiments?
Thank you!
Hi @ajwagen, thanks a lot for open sourcing the code!
I'm trying to reproduce the results for the ablation study "Does the quality of data πdp is trained on affect DSRL?". In my fork of the repo, I slightly modified the process_robomimic_dataset.py file to allow the creation of$\pi_\texttt{dp}$ .
train.npzfiles for data splits from the Robomimic MH datasets. I've also created YAML (pre_diffusion_mlp_worse.yaml) files with the same configurations as in the robomimic can YAMLs to pretrain and evalHowever, I always see
for various training configurations, and surprisingly, data splits (better, okay, worse, worse_okay).
Can you please direct me on how to reproduce those results by providing the required YAML and NPZ files, the$\pi_\texttt{dp}$ checkpoints or clearer guidelines on how to run those experiments?
Thank you!