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 framework that facilitates all stages of the LLM Agent development lifecycle

A platform for developers to simulate research community

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.

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

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。

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

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

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.

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