E2C implementation in PyTorch

Last update: Dec 12, 2022

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Overview

Embed to Control implementation in PyTorch

Paper can be found here: https://arxiv.org/abs/1506.07365

You will need a patched version of OpenAI Gym in order to generate the dataset. See https://github.com/ethanluoyc/gym/tree/pendulum_internal

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)

Owner

Yicheng Luo

PhD student at UCL AI Center. Previous Research Engineering intern at DeepMind and Prowler.io.

GitHub Repository

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E2C implementation in PyTorch

Related tags

Overview

Embed to Control implementation in PyTorch

What's included ?

TODO

Owner

Yicheng Luo

Lex Rosetta: Transfer of Predictive Models Across Languages, Jurisdictions, and Legal Domains

Toolkit for collecting and applying prompts

Language Models for the legal domain in Spanish done @ BSC-TEMU within the "Plan de las Tecnologías del Lenguaje" (Plan-TL).

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