Python interface for (multi-agent) reinforcement learning to the CATS model [Delli Gatti (2011), Assenza (2017)].
-
Updated
May 30, 2024 - Python
Python interface for (multi-agent) reinforcement learning to the CATS model [Delli Gatti (2011), Assenza (2017)].
OpenDILab Decision AI Engine
The TTCP CAGE Challenges are a series of public challenges instigated to foster the development of autonomous cyber defensive agents. This CAGE Challenge 4 (CC4) returns to a defence industry enterprise environment, and introduces a Multi-Agent Reinforcement Learning (MARL) scenario.
A suite of test scenarios for multi-agent reinforcement learning.
Procedural Environment Generation for Accelerated Multi-Agent Reinforcement Learning
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
The Drone Swarm Search project provides an environment for SAR missions built on PettingZoo, where agents, represented by drones, are tasked with locating targets identified as shipwrecked individuals.
Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning Framework on a GPU (JMLR 2022)
This repository contains Dongming Shen's demonstration code and documentation for the research projects conducted at the IDM Lab, USC. The project focuses on integrating Multi-Agent Path Finding (MAPF) with Multi-Agent Reinforcement Learning (MARL) to explore efficient coordination strategies among autonomous agents in dynamic environments.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
Multi-agent RL environment for a pikachu-volleyball game based on Pettingzoo.
This program aims to compare the performance of the Multiagent Rollout algorithm against the Ordinary Rollout algorithm and the Base Policy in the context of the Spiders and Flies problem.
Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning.
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
A modular, flexible framework for developing performant algorithms in Reinforcement Learning.
We extend pymarl2 to pymarl3, equipping the MARL algorithms with permutation invariance and permutation equivariance properties. The enhanced algorithm achieves 100% win rates on SMAC-V1 and superior performance on SMAC-V2.
This repo is the implementation of paper ''SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning''.
PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.
Implementation of some Deep Reinforcement Learning algorithms and environments.
Add a description, image, and links to the multiagent-reinforcement-learning topic page so that developers can more easily learn about it.
To associate your repository with the multiagent-reinforcement-learning topic, visit your repo's landing page and select "manage topics."