Code accompanying the OptionGAN paper.
☆44Aug 30, 2018Updated 7 years ago
Alternatives and similar repositories for OptionGAN
Users that are interested in OptionGAN are comparing it to the libraries listed below
Sorting:
- Model-Based Generative Adversarial Imitation Learning☆89Mar 29, 2021Updated 4 years ago
- Repo for code for the NIPS paper entitled "An Architecture for Deep, Hierarchical Generative Models"☆14Oct 27, 2016Updated 9 years ago
- ☆15Mar 25, 2018Updated 7 years ago
- Lifelong Variational Autoencoder☆15Dec 6, 2017Updated 8 years ago
- Run Pytorch graphs inside Theano graph (and pytorch wrapper for AIS for generative models).☆18Oct 19, 2017Updated 8 years ago
- MetaC provides a read-eval-print loop (a REPL) and notebook interactive development environment (a NIDE) for C programming. MetaC also …☆12Feb 16, 2026Updated last week
- Code release for the paper "Calibrating Energy-based Generative Adversarial Networks"☆24Oct 31, 2017Updated 8 years ago
- ☆58Aug 28, 2018Updated 7 years ago
- ☆43Feb 9, 2017Updated 9 years ago
- Public accompanying repository for Universite de Montreal's IFT 6757: Autnonomous Vehicles, Fall 2019.☆11Jun 21, 2022Updated 3 years ago
- Pytorch official implementation for Imitating Unknown Policies via Exploration.☆14Oct 3, 2023Updated 2 years ago
- Code Release for Task Agnostic Dynamics Priors for Deep Reinforcement Learning☆12Jun 13, 2019Updated 6 years ago
- Implementation of the Option-Critic Architecture on the Atari (ALE) environment☆182Sep 21, 2017Updated 8 years ago
- E2C implementation in PyTorch☆43Jul 5, 2017Updated 8 years ago
- [NIPS 2017] InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations☆184Nov 14, 2024Updated last year
- Implements an infinite sum of poisson-weighted convolutions☆27Aug 22, 2018Updated 7 years ago
- Make python virtual environment setup on old servers less painful☆10Nov 5, 2017Updated 8 years ago
- Code for "Adversarial Constraint Learning for Structured Prediction"☆14May 30, 2018Updated 7 years ago
- PyTorch implementation of Memory Augmented Self-Play☆52Oct 26, 2020Updated 5 years ago
- Ice is a rapid information extraction customizer☆15Apr 26, 2021Updated 4 years ago
- Various experiments on the [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset from Zalando☆31Sep 28, 2017Updated 8 years ago
- Pytorch implementation of Value Iteration Networks (NIPS 2016 best paper)☆319Oct 2, 2020Updated 5 years ago
- Lagrangian VAE☆28Jul 27, 2018Updated 7 years ago
- GAKE: Graph Aware Knowledge Embedding(COLING2016)☆28Jan 19, 2019Updated 7 years ago
- An attempt at a PyTorch Implementation of "Zero-Shot" Super-Resolution using Deep Internal Learning by Shocher et al. CVPR 2018☆14Aug 30, 2018Updated 7 years ago
- ☆17Feb 14, 2018Updated 8 years ago
- NIPS 2017 Value Prediction Network☆167Jan 12, 2018Updated 8 years ago
- Tensorflow Implementation on "The Cramer Distance as a Solution to Biased Wasserstein Gradients" (https://arxiv.org/pdf/1705.10743.pdf)☆123Dec 10, 2017Updated 8 years ago
- Code to build VAE models that are jointly conditioned.☆36Dec 17, 2017Updated 8 years ago
- [Reimplementation Ross et al 2011] An implementation of DAGGER using ConvNets for driving from pixels.☆85Feb 22, 2018Updated 8 years ago
- RWA in pytorch☆14May 7, 2017Updated 8 years ago
- ☆13Feb 17, 2018Updated 8 years ago
- Reimplementation of the clockwork recurrent neural network in Torch7☆14Feb 4, 2016Updated 10 years ago
- ☆14Feb 1, 2017Updated 9 years ago
- Implementation of Visual Feature Attribution using Wasserstein GANs (VAGANs, https://arxiv.org/abs/1711.08998) in PyTorch☆93May 31, 2023Updated 2 years ago
- Tensorflow Implementation of Programmable Agents☆35Sep 25, 2017Updated 8 years ago
- Logging utility for ML experiments☆16Jun 18, 2022Updated 3 years ago
- SuperpixelGridMasks is an approach for sensor-based data augmentation towards image classification tasks and so on.☆14Jan 18, 2023Updated 3 years ago
- Code for Deep RL from Human Preferences [Christiano et al]. Plus a webapp for collecting human feedback☆562Jan 24, 2023Updated 3 years ago