HIVEX: A High-Impact Environment Suite for Multi-Agent Research
Autonomous ecologies of construction: Collaborative modular robotic material eco-systems with deep multi-agent reinforcement learning
Learning to Communicate and Collaborate in a Competitive Multi-Agent Setup to Clean the Ocean from Macroplastics
Robotic Reconfiguration with Deep Multi-Agent Reinforcement Learning
Dynamic Collaborative Multi-Agent Reinforcement Learning Communication for Autonomous Drone Reforestation
Multi-Agent Collaboration for Wild Fire Management Resource Distribution
Competitive Multi-Agent Collaboration for Drone Control and Package Delivery Tasks
Multi-Agent Collaboration for Wind Farm Control
Using Reinforcement Learning to Evaluate Navigability of Masterplan
Using Mulit-Agent Reinforcement Learning to Build a Tree
Average Exterior Visibility Prediction using Polynomial Linear Regression
Procedural Programming to Generate Grid Worlds
Using Mulit-Agent Reinforcement Learning to Optimize Core and Atria Placement
Using Genetic Algorithm to Optimize Building Topology
Predicting City Soundscape for Designers using Neural Network
Virtual Reality as Human Scale Design Tool for Workspace
Quantifying Architectural, Human Centric Qualities of Space
Sensing Behaviour and Movement using OpenSensors with ModCam
Sensing Indoor Environmental Qualities to Predict Productivity
Automating Furniture Distribution using Circle Packing
