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Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
A toolkit for developing and comparing reinforcement learning algorithms.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Code and documentation to train Stanford's Alpaca models, and generate the data.
DSPy: The framework for programming—not prompting—language models
Open standard for machine learning interoperability
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
Hierarchical Reasoning Model Official Release
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Running large language models on a single GPU for throughput-oriented scenarios.
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
Unified framework for robot learning built on NVIDIA Isaac Sim
Google DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
A library of reinforcement learning components and agents
Isaac Gym Reinforcement Learning Environments
A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
A TensorFlow implementation of the Differentiable Neural Computer.
Simple and easily configurable grid world environments for reinforcement learning
robosuite: A Modular Simulation Framework and Benchmark for Robot Learning
A JAX research toolkit for building, editing, and visualizing neural networks.
Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula"
An offline deep reinforcement learning library
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos