katerakelly / oyster

Implementation of Efficient Off-policy Meta-learning via Probabilistic Context Variables (PEARL)
MIT License
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Default Parameters Paper <-> Repository #24

Closed 0pss closed 3 years ago

0pss commented 3 years ago

Hello, First, thank you for your research! I am currently trying (and struggling a little) to reproduce your results on the HalfCheetahVel environment. I noticed some differences between the algorithm in your paper (https://arxiv.org/pdf/1903.08254.pdf, page 5) and the default settings in this repository, i.e.

I would highly appreciate if you can make some statement about the effect of those differences. Is my assumption correct, that your results were produced with the default parameters from the repo?

Thank you in advance!

katerakelly commented 3 years ago

Hello,

The results in the paper were produced with the default settings in this repository. If I remember correctly, the weight on the KL loss does make quite a difference, while adding the next observation in an environment where it's not needed (as in Cheetah-vel) doesn't make a difference - note that it is included in the Walker experiment in which the dynamics change across tasks.