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Slow sampling from expert dataset in IRL traning loop

See original GitHub issue

Hi. After implementing pre-recorded expert data with the size of 1e6. I have realized that np.random.choice with replace=Fasle is extremely slow to the point of unusable. (batch size 100)

I am wondering if it can be replaced with something faster.

Thanks for the great project.

  # Do not allow duplication!!!
  indices = np.random.choice(
      self._random_range, self._irl.batch_size, replace=False)
  self._irl.train(

P.S. I am using the dataset from Berkeley’s D4RL project.

Screenshot from 2021-06-02 19-37-23

Issue Analytics

State:
Created 2 years ago
Comments:6 (3 by maintainers)

Top GitHub Comments

1reaction

ymd-hcommented, Sep 4, 2021

I did additional investigation (after PR merged).

Unlike the legacy free function (aka. np.random.choice), the recommended generator object method (aka. np.random.Generator.choice) has heuristic algorithm.

https://github.com/numpy/numpy/blob/410a89ef04a2d3c50dd2dba2ad403c872c3745ac/numpy/random/_generator.pyx#L795-L837

In my opinion, we don’t need to open a new issue at NumPy repository.

To determine the fastest method for us, we need additional study. (However, I think the study is low priority as long as the current implementation is sufficient.)

If anyone have problem with the current implementation, please feel free to tell us.

Ref: Non-repetitive random number in numpy | Stack Overflow

0reactions

majiangcommented, Aug 30, 2021

I’ve found this via @ymd-h 's blog article. Do you know if there’s numpy’s official resource about this? I’d open an issue or PR if not.

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