41 datasets found

A Drone Swarm Search environment, based on PETTINGZOO, that is to be used in conjunction with multi-agent (or single-agent) reinforcement learning algorithms.

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...

The dataset used in this paper is a nonlinear positive multi-agent system with sector input nonlinearities.

This paper introduces Trajectron, a probabilistic multi-agent trajectory modeling framework.

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.

Pommerman is a 2-vs-2 game environment introduced in 2018, and is a modified version of the popular game 'Bomberman'. It is a good testbed for RL, because the environment...

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...

The dataset used in the paper is a real-world packet routing system with limited-bandwidth restriction.

The dataset used in the paper is a negotiation team dataset, where team members negotiate against different types of opponents (competitors, matchers, and conceders).

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...

The SpecTGNN dataset is used for trajectory prediction tasks. It contains historical observations of multiple interactive agents and their environment.

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...

The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal.

The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal.

The dataset used in the paper is a multi-agent environment, where agents need to communicate with each other to achieve a common goal.

The Google Research Football environment is a reinforcement learning experimental platform focused on training agents to play football.

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.

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...

The CARLA dataset is a complex urban-like environment with multi-agent dynamics, pedestrians, intersections, cross-traffic, roundabout, and changing weather conditions.

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...