VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

Backend for columnar, fully orchestrated HEP analyses with pure Python, law and order.

[CVPR 2023] vMAP: Vectorised Object Mapping for Neural Field SLAM

stable-baselines3 reinforcement learning on SUMO traffic light system

Plot in NumPy arrays directly, overlay NumPy plots straight over video real-time, plot in Jupyter without a single loop

Sentiment analysis of kinopoisk (https://www.kinopoisk.ru/) reviews. For analysis i used: classification methods (random forest, svc, k-neighbors) neural networks (lstm, mlp).

Vectorized Associative Arrays for Python

Implementation and optimization of the Mandelbrot set for CE8 Numerical Scientific Computing Course. Features: Video generation of zooming and iterations.