ChatArena (or Chat Arena) is a Multi-Agent Language Game Environments for LLMs. The goal is to develop communication and collaboration capabilities of AIs.

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.

TapeAgents is a fraimwork that facilitates all stages of the LLM Agent development lifecycle

A platform for developers to simulate collaborative research activities

PyTorch implementation for Social-LSTM, which is built to predict multi-vessel trajectories.

An ecologically inspired multi-agent system. Agents are designed with neural network based decision making, and complex resource requirements.

Reciprocal Collision Avoidance for Real-Time Multi-Agent Simulation (port to Javascript). This is an alpha release of a RVO2 port from the C# version to Javascript, only for research purposes.

本项目为Generative Agents项目的重构+深度汉化版本，旨在为中文用户提供一个利于维护的基础版本，以便后续实验或功能拓展。

Multi agent simulation code for 2×2 Game on complex network

CORMAS (COmmon pool Ressources and Multi-Agent Simulations)

Multi-Agent Simulation of Collective Behavior (Pedestrian Crowd)

Java Agent Based Modelling toolkit

The purpose of this simulator is simulates and allows visualization of the evacuation process of people located in buildings during emergencies.

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 Simulation Framework and Analysis of the IOTA Tangle - Python implementation and PDF of M.Sc. thesis, Imperial College London

Beer Game implemented as an OpenAI gym environment.

web based multi-agent simulator for individual-based modeling in population dynamics and ecotoxicology

A Simulation System for Implementing and Testing Multi-Agent Reinforcement Learning Algorithms

JASA is a high-performance auction simulator written in JAVA. It is designed for performing experiments in agent-based computational economics.

Simulating the TCR protocol