Open bit0123 opened 1 month ago
Hi, Thanks for the interesting paper and releasing the code. I am facing the same issue ("TypeError: h5py objects cannot be pickled") while running with num_worker=4. However, the code works when i set num_worker=0 with some additional transformation of input image in the policy file.
The loss becomes negative after the first epoch for different algorithm with 0 success. Consequently, the AoC is 0. Could you please comment on that. Thanks
Hi!Have you solved this?
I encountered the same problem. All the evaluation indicators were 0, including AOC, Success Rate ...
What confuses me is that the following two projects based on LIBERO also have success rate = 0 and Aoc = 0. No matter how we train, the result is 0. Is it because the details of our configuration are incorrect? I would like to ask for your help. Thank you very much! @Cranial-XIX https://github.com/UT-Austin-RPL/Lotus
After 20 epochs of training, the success rate would become larger than 0. And after 50 epochs of training, the average success rate is around 0.7. (for libero_object
tasks)
经过 20 个时期的训练后,成功率将大于 0。经过 50 个时期的训练后,平均成功率约为 0.7。(对于
libero_object
任务)
Have you ever tried libero-90? For libero-90, it seems the problem still exists for me. Thank you very much!
The success rate on libero-90 is low by design (since there are 90 tasks), and in general, you might need over 20 epochs (like 25 - 35) to reach the peak result.
Hi, Thanks for the interesting paper and releasing the code. I am facing the same issue ("TypeError: h5py objects cannot be pickled") while running with num_worker=4. However, the code works when i set num_worker=0 with some additional transformation of input image in the policy file.
The loss becomes negative after the first epoch for different algorithm with 0 success. Consequently, the AoC is 0. Could you please comment on that. Thanks