-
DSSE: a Drone Swarm Search Environment
A Drone Swarm Search environment, based on PETTINGZOO, that is to be used in conjunction with multi-agent (or single-agent) reinforcement learning algorithms. -
Multi-Agent Games Using Adaptive Feedback Control
The dataset used in this paper is a set of five game-theoretic tasks (Harmony Game, Hawk-Dove, Stag-Hunt, Prisoners Dilemma and Battle of the Exes) with seven different agent... -
Nonlinear Positive Multi-Agent System
The dataset used in this paper is a nonlinear positive multi-agent system with sector input nonlinearities. -
The Trajectron: Probabilistic Multi-Agent Trajectory Modeling with Dynamic Sp...
This paper introduces Trajectron, a probabilistic multi-agent trajectory modeling framework. -
Towards True Lossless Sparse Communication in Multi-Agent Systems
The dataset used in the paper is a multi-agent reinforcement learning environment, where agents need to communicate with each other to achieve their goals. -
Discrete-time Blended Dynamics Theorem
The dataset used in the paper is a discrete-time blended dynamics theorem, which is a discrete-time version of the blended dynamics theorem for the use of designing distributed... -
Packet Routing System
The dataset used in the paper is a real-world packet routing system with limited-bandwidth restriction. -
Intra-Team Strategies for Teams Negotiating Against Competitor, Matchers, and...
The dataset used in the paper is a negotiation team dataset, where team members negotiate against different types of opponents (competitors, matchers, and conceders). -
TripleSumo
A virtual multi-agent platform called RoboSumo is extended to create TripleSumo, a platform for investigating multi-agent cooperative behaviors in continuous action spaces, with... -
Spectral Temporal Graph Neural Network for Trajectory Prediction
The SpecTGNN dataset is used for trajectory prediction tasks. It contains historical observations of multiple interactive agents and their environment. -
Sample-Efficient Robust Multi-Agent Reinforcement Learning in the Face of Env...
To overcome the sim-to-real gap in reinforcement learning (RL), learned policies must maintain robustness against environmental uncertainties. While robust RL has been widely... -
Level-based Foraging
The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal. -
TrafficJunction
The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal. -
PredatorPrey
The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal. -
Google Research Football
The Google Research Football environment is a reinforcement learning experimental platform focused on training agents to play football. -
Robust Multi-agent Communication via Multi-view Message Certification
The dataset used in the paper is a multi-agent communication dataset, where agents learn to communicate with each other to achieve a common goal. -
Mesoscopic Transportation Network
The dataset used in the paper is a mesoscopic transportation network consisting of one origin node, one destination node, and a set of three available arcs for commuters to... -
AMiRo dataset
The dataset used in the paper is a comprehensive three-class example, where each class is represented by a set of sensor modalities (camera and LiDAR). The dataset is used to...