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skrl is an open-source modular library for Reinforcement Learning written in Python (using PyTorch) and designed with a focus on readability, simplicity, and transparency of algorithm implementation.
Section: 11. Specialized Domains · Modular reinforcement learning library implemented in PyTorch, JAX, and NVIDIA Warp with support for Gymnasium, NVIDIA Isaac Lab, MuJoCo Playground, and other environments. MIT licensed.
Section: Industry Strength Reinforcement Learning · skrl is an open-source modular library for Reinforcement Learning written in Python (using PyTorch) and designed with a focus on readability, simplicity, and transparency of algorithm implementation.
Section: Reinforcement Learning · Modular reinforcement learning library (on PyTorch and JAX) with support for NVIDIA Isaac Gym, Isaac Orbit and Omniverse Isaac Gym.
Section: Reinforcement Learning for Robotics · Modular reinforcement learning library with support for multiple ML frameworks.
Modern, comprehensive probabilistic programming framework in Python. Bayesian modeling with advanced MCMC sampling, variational inference, and seamless integration with ArviZ for visualization. Apache 2.0 licensed.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Quantitative finance: multi-agent AI hedge fund trading system featuring native JevLLM integration to execute fast typed decisions without parsing fragility.
FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework.
Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API.