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🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Robust Speech Recognition via Large-Scale Weak Supervision
CLI platform to experiment with codegen. Precursor to: https://lovable.dev
A collection of design patterns/idioms in Python
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
A toolkit for developing and comparing reinforcement learning algorithms.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Pretrain, finetune ANY AI model of ANY size on 1 or 10,000+ GPUs with zero code changes.
Graph Neural Network Library for PyTorch
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
An API standard for single-agent reinforcement learning environments, with popular reference environments and related utilities (formerly Gym)
Hackable and optimized Transformers building blocks, supporting a composable construction.
A Python framework for accelerated simulation, data generation and spatial computing.
Unified framework for robot learning built on NVIDIA Isaac Sim
Isaac Gym Reinforcement Learning Environments
A collection of high-quality models for the MuJoCo physics engine, curated by Google DeepMind.
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
Modern Robotics: Mechanics, Planning, and Control Code Library --- The primary purpose of the provided software is to be easy to read and educational, reinforcing the concepts in the book. The code…
A modular active learning framework for Python
Clean PyTorch implementations of imitation and reward learning algorithms
A large-scale benchmark and learning environment.
A standard framework for modelling Deep Learning Models for tabular data
High-quality implementations of standard and SOTA methods on a variety of tasks.
Code accompanying the paper "Learning Agile Robotic Locomotion Skills by Imitating Animals"