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Building LLM-Enabled Multi Agent Applications from Scratch

Simulation environment for multiagent drone systems using Ardupilot, ROS 2, and Gazebo enabling users to spawn and control multiple drones, configure sensors, and test autonomous behaviors in a reproducible and extensible setup.

The Multiagent Decision Process (MADP) Toolbox - planning and learning in multiagent systems.

Modified MDP and Q-Learning Algorithm | Multiagent optimization | Path Planning | Navigation | Reinforcement Learning | Stochastic Systems | Swarm Robotics

Deep Reinforcement Learning (DRL) algorithms have been successfully applied to a range of challenging simulated continuous control single agent tasks. These methods have further been extended to multiagent domains in cooperative, competitive or mixed environments. This paper primarily focuses on multiagent cooperative settings which can be modeled for several real world problems such as coordination of autonomous vehicles and warehouse robots. However, these systems suffer from several challenges such as, structural credit assignment and partial observability. In this paper, we propose Recurrent Multiagent Deep Deterministic Policy Gradient (RMADDPG) algorithm which extends Multiagent Deep Determinisitic Policy Gradient algorithm - MADDPG \cite{lowe2017multi} by using a recurrent neural network for the actor policy. This helps to address partial observability by maintaining a sequence of past observations which networks learn to preserve in order to solve the POMDP. In addition, we use reward shaping through difference rewards to address structural credit assignment in a partially observed environment. We evaluate the performance of MADDPG and R-MADDPG with and without reward shaping in a Multiagent Particle Environment. We further show that reward shaped RMADDPG outperforms the baseline algorithm MADDPG in a partially observable environmental setting.

Independent Generative Adversarial Self-Imitation Learning In Cooperative Multiagent Systems

Fundamentals of Multiagent Systems Textbook

Add Behavior Trees to Groups and create swarms, flocks and multiagent systems

二阶系统比例一致性

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Lab-Resources for the course: Intelligent Agents and Multiagent Systems

Code to do multi agent systems research on StarCraft II. It contains methods to deal with the Raw Interface from the SC2 API, which has not been officially adapted for Python. This means that, the programmer can now access the objects at the game and the 2D original representation of the map

Magentix2 is an agent platform for open Multiagent Systems. Its main objective is to bring agent technology to real domains: business, industry, logistics, e-commerce, health-care, etc.

A Lua Library to build Multiagent Systems (inspired in NetLogo)

Simulation environment for multiagent drone systems using PX4, ROS 2, and Gazebo enabling users to spawn and control multiple drones, configure sensors, and test autonomous behaviors in a reproducible and extensible setup. Support Micro-XRCE agent and MAVROS

Codebase for two projects, one on the Consensus Algorithm in multi agent systems and another on State Variable Feedback control in multiagent systems

Multiagent systems' testbed based on Diplomacy. http://www.dipgame.org

Multiagent systems course, cellular automaton example

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems (AAAI 2020)

Code for the paper "Planning Not to Talk: Multiagent Systems that are Robust to Communication Loss".

ECEE 5448 Game Theory and Multiagent Systems Fall 2015

llm intergration with multiagent systems for webdev solutions

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Predictive Control of Networked Multiagent Systems via Cloud Computing 论文复现

Multi Agent Resource Scheduling System - VERSION 1.0 To see VERSION 0.0 go to github.com/granales/MARSS This program provides real time resource scheduling with short reaction times to disruptions such as machinery faults. Reaction times to reschedule becomes feasible taking minutes. This project does not aim to reduce uncertainty, it provides a tool to manage it. The dynamic scheduling auction-based multiagent system is thought to provide stability to a production facility as it can dynamically and periodically create schedules. This program conforms a decentralized decision-making system. It is developed to monitor production processes and rescheduling the orders if necessary. The main characteristic of the system is that it is based on the existing historical data. The system is thought to help the planners monitor the order production process and develop a rescheduling decision when the production is interrupted. The system generates a feasible solution in the short time available. In order to increase the feasibility and flexibility of such systems, agents are assigned with different roles. With the use of multi agent systems, the steel production scheduling problem becomes populated by several autonomous and heterogeneous intelligent agents with various goals, constraints, and capabilities. These agents can cooperate and coordinate their local schedules to generate desirable global schedules. Agents negotiate in auctions to assign materials to-be processed to processing resources.

Multiagent simulation of message broadcasting in network models of transporation systems

School project aiming to model different multi-agent systems based on simple AI. This project was coded in Java. The available systems are Conway's Game of Life, the Immigration Game, the Schelling model (both of them are variations of the Game of Life), a 2D ball movement simulation, a boids simulation, and the prey-predator system.

Used for sharing simulations and programs related to the Multi Agent Systems at UvA, Amsterdam

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