OpenDILab Decision AI Engine
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Updated
May 29, 2024 - Python
OpenDILab Decision AI Engine
Predator-Prey-Grass gridworld environment using PettingZoo, with dynamic deletion and spawning of partially observant agents.
A suite of test scenarios for multi-agent reinforcement learning.
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.
MARL explores cooperation & competition in gridworlds. Batman & Robin team up (DQN, CQL, MAD-DQN, REINFORCE). Adversaries use MADDPG with CLDE for strategy.
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.
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.
Algorithms to solve the DSSE environment, focusing on optimizing drone swarm search and navigation for critical applications.
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.
Paper list of multi-agent reinforcement learning (MARL)
This repository presents a multi-agent reinforcement learning approach for energy-efficient collaborative control of base stations in 5G massive MIMO cellular networks.
Language-Guided Pattern Formation for Swarm Robotics with Multi-Agent Reinforcement Learning.
Very simple implementation of Neural Fictitious Self-Play
Code for our paper: Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation
AI Manager for Devin AI
For deep RL and the future of AI.
IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
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