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Tools for effectively storing and sampling from episodes for RL experiments.
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examples
spacelib
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readme.md
setup.py

readme.md

spacelib

spacelib is a small collection of tools for off-policy reinforcement learning, particularly with recurrent agents and particularly for learning directly from pixel observations.

The package makes pretty heavy use of pytorch tensors and the openai gym APIs.

Features:

  • efficiently store completed episodes on disk in memory-mapped numpy arrays
  • transparently handle hierarchically structured action/observation spaces
  • simple interface for batch sampling of sequences from experience history
  • store hidden states for recurrent models

For a usage example, see the example notebook.

TODO

  • stop one-hot encoding discrete spaces
  • test/add an example for hidden state storage

This project is licensed under the terms of the MIT license Please let me know if you find it useful!

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