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Releases: takuseno/minerva

Release v0.40

29 May 09:21
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Support d3rlpy v0.90!

MINERVA now supports d3rlpy v0.90 and there are the highlights for MINERVA.

  • more precise CQL and BEAR implementations
  • predict_value is fixed for unscaled actions
  • CRR algorithm is available

Release v0.32

06 Mar 04:37
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a way to upload images is changed

Previously, the image files are uploaded via selecting the directory that contains all the files. However this is an easy way, the browser limits the number of files due to the efficiency issue. So, from this version, you can upload zipped file that contains the image files. This is the more efficient and fast way.
image

Release v0.31

06 Mar 02:22
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enhancements

  • improve temporary directory for multiple OS support

bugfix

  • update documentation
  • fix epoch count

Release v0.30

05 Mar 14:49
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Support d3rlpy v0.70!

MINERVA now supports d3rlpy v0.70, which has a lot of progress from the v0.41 that is the previous dependency.

These are some highlights related to MINERVA.

  • the continuous action is automatically normalized based on the dataset statistics
  • discrete action option has been removed since the action-space is automatically detected
  • many other internal enhancements

Release v0.20

21 Dec 15:25
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Support d3rlpy v0.41!

MINERVA now supports d3rlpy v0.41, which has a lot of progress from the v0.23 that is the previous dependency.

These are the some highlights related to MINERVA.

  • extremely fast mini-batch creation
  • extremely fast frame stacking for image observation
  • extremely fast N-step TD calculation
  • new metrics
  • etc

Algorithm Selection

MINERVA now provides many many algorithms for both discrete and continuous control datasets. You can choose an algorithm at the project creation dialog.

discrete algorithms

  • DQN
  • Double DQN
  • AWR
  • CQL
  • BCQ
  • SAC

continuous algorithms

  • DDPG
  • TD3
  • SAC
  • BCQ
  • BEAR
  • CQL
  • AWR
  • AWAC
  • PLAS

Of course, there is the Q function option to incorporate arbitrary algorithms with the powerful distributional Q functions.

Q functions

  • mean
  • Quantile Regression
  • Implicit Quantile Network
  • Fully parameterized Quantile Function

Release v0.12

09 Sep 05:03
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Hotfix

  • Fix reserved keyword confliction with Python 3.7

First release!

09 Sep 03:51
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Dataset Dashboard

  • upload datasets
    • discrete/continuous action-space
    • vector/image observations

Project Dashboard

  • train with CQL with detailed configurations.

Export Policy

  • Support TorchScript and ONNX.