Fast Reinforcement Learning is a specialized Python framework designed to accelerate the development and execution of reinforcement learning agents. It offers out-of-the-box support for popular algorithms such as PPO, A2C, DDPG and SAC, combined with high-throughput vectorized environment management. Users can easily configure policy networks, customize training loops and leverage GPU acceleration for large-scale experiments. The library’s modular design ensures seamless integration with OpenAI Gym environments, enabling researchers and practitioners to prototype, benchmark and deploy agents across a variety of control, game and simulation tasks.

Cleora is a powerful machine learning tool designed for generating top-notch graph embeddings, which allow for the efficient and scalable learning of stable and inductive entity embeddings for heterogeneous relational data. Ideal for large-scale datasets, Cleora facilitates the embedding of users, products, and more, aiding in enhanced data analysis and decision-making processes. Prominent for its speed and ease of production, Cleora is geared towards data scientists and analytics teams who need to process large amounts of data without requiring high-end hardware.