Decision Transformer Lili Chen*, Kevin Lu*, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas†, and Igor Mordatch† *equal contribution, †equal advising Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling. Contains scripts to reproduce experiments. Instructions We provide code in two sub-directories: atari containing code for Atari experiments and gym containing code for OpenAI Gym experiments.See corresponding READMEs in each folder for instructions; scripts should be run from the respective directories.It may be necessary to […]

score_sde_fast_sampling This repo contains the official implementation for the paper Gotta Go Fast When Generating Data with Score-Based Models, which shows how to generate data as fast as possible with score-based models using a well-designed SDE solver. See the blog post for more details. Pretrained checkpoints https://drive.google.com/drive/folders/10pQygNzF7hOOLwP3q8GiNxSnFRpArUxQ?usp=sharing References If you find the code useful for your research, please consider citing @article{jolicoeurmartineau2021gotta, title={Gotta Go Fast When Generating Data with Score-Based Models}, author={Alexia Jolicoeur-Martineau and Ke Li and R{‘e}mi Pich{‘e}-Taillefer and Tal […]

Generative Models as Distributions of Functions This repo contains code to reproduce all experiments in Generative Models as Distributions of Functions. Requirements Requirements for training the models can be installed using pip install -r requirements.txt. All experiments were run using python 3.8.10. Training a model To train a model on CelebAHQ64, run python main.py configs/config_celebahq64.json Example configs to reproduce the results in the paper are provided in the configs folder. Note that you will have to provide a path to […]