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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.
ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.
A lightweight Python-based 3D network multi-agent simulator. Uses a cell-based congestion model. Calculates risk, loudness and battery capacities of the agents. Suitable for 3D network optimization tasks.
A multi-agent platform built on the top of the pystarworldsturbo library. Part of the Intelligent Agents course taught at Royal Holloway University of London.
The game is a simulation of multiple agents with conflicting goals. The agents try to survive and move as many cells as they can. On the other hand their adversaries try to interrupt them.
Détection d'un blob dans une image ainsi que détection des comprimés de nourriture. Simulation d'un blob dans PyGame à travers un système multi-agents à connaissance partagée.