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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.
This repository contains an implementation of a deep learning architecture designed for unsupervised or self-supervised classification tasks. The architecture consists of two components: a classifier and an aligner.
Multiplayer Game, Security, DF Service Implementation, Genetic Algorithm Implementation, Multi-Agent Systems Enhanced With Q-Learning Implementation For Improved Decision-Making.