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Your work is exceptionally well done, truly fantastic! I would like to inquire if the source policy can be trained directly using train.py? During the training process, I encountered an issue where the reward function remains at 0, and I noticed that the task registration name in train.py does not match the registration name of the source policy.
The text was updated successfully, but these errors were encountered:
Thanks for your kind complements! Unfortunately the source policy training environments are a little bit tricky to integrate. If you'd like to train bipedal walking, you can directly use this repo: https://github.com/HybridRobotics/SymmLoco and use the checkpoint in our repo. The source policy performance will be plausible.
If you'd like to train other skills, please refer to https://github.com/Alescontrela/AMP_for_hardware, though I'd encourage you to try some newer RL frameworks, as our source policies trained with this AMP pipeline was not that great....
Your work is exceptionally well done, truly fantastic! I would like to inquire if the source policy can be trained directly using
train.py
? During the training process, I encountered an issue where the reward function remains at 0, and I noticed that the task registration name intrain.py
does not match the registration name of the source policy.The text was updated successfully, but these errors were encountered: