https://github.com/ethanluoyc/e2c-pytorch

E2C implementation in PyTorch
https://github.com/ethanluoyc/e2c-pytorch

Last synced: about 1 month ago
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E2C implementation in PyTorch

• Host: GitHub
• URL: https://github.com/ethanluoyc/e2c-pytorch
• Owner: ethanluoyc
• License: apache-2.0
• Created: 2017-07-05T17:29:38.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2017-07-05T17:31:39.000Z (almost 7 years ago)
• Last Synced: 2024-04-16T07:35:46.019Z (about 1 month ago)
• Language: Python
• Size: 65.4 KB
• Stars: 42
• Watchers: 6
• Forks: 9
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Embed to Control implementation in PyTorch

Paper can be found here: 

You will need a patched version of OpenAI Gym in order to generate the

dataset. See 

For the planar task, we use code from. The source code of the repository

has been modified for our needs and included under `e2c/e2c_tf`.

## What's included ?

* E2C model, VAE and AE baselines. Allow configuration for different

network architecture for the different setups (see Appendix of the paper).

## TODO

* Documentation, tests... (Soon to follow)