[Question] Parallel Sampling about minari HOT 2 OPEN

First, thanks for using Minari! And these questions are really helpful for us; it's difficult to refine a product without hearing from users.

OK so for the first part. We are working on a optional https://docs.mosaicml.com/projects/streaming/en/stable/ streamingdataset backend. We are open to design suggestions for parallel sampling for both a streaming dataset and h5py backend(I'm not sure what we need to do to get true parallelism in python in the setting of memory shared between physical threads, maybe this is easy). It may be that a parallel sampling implementation could be a strict improvement over our current implementation on any machine with more than once physical CPU core.

For the second one. We don't have any built-in features for sampling from multiple datasets at once. The closest thing that comes to mind is generating the list of indices to sample externally to minari, then sampling from each dataset using iterate_episodes with that list as an argument (you can also use square brackets directly on the MinariDataset object to get a episode by index). That will give you fine-grained enough control to sample without replacement, or sample the same indices from different datasets, etc... We have sub-episode trajectory sampling code in development also.

We are open to feature requests, so feel free to propose any features you think would support your use-case.

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Thanks for the response! Regarding the development of the streaming dataset backend, is this currently in a public fork? Curious to just to take a look and see if I could patch together something similar in the mean time before this becomes an official feature.

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