9 dataset results for Multi-agent Reinforcement Learning

CityFlow is a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions.

43 PAPERS • 2 BENCHMARKS

StarCraft II Learning Environment

The StarCraft II Learning Environment (S2LE) is a reinforcement learning environment based on the game StarCraft II. The environment consists of three sub-components: a Linux StarCraft II binary, the StarCraft II API and PySC2. The StarCraft II API allows programmatic control of StarCraft II. It can be used to start a game, get observations, take actions, and review replays. PyC2 is a Python environment that wraps the StarCraft II API to ease the interaction between Python reinforcement learning agents and StarCraft II. It defines an action and observation specification, and includes a random agent and a handful of rule-based agents as examples. It also includes some mini-games as challenges and visualization tools to understand what the agent can see and do.

24 PAPERS • NO BENCHMARKS YET

SMAC-Exp (StarCraft Multi-Agent Exploration Challenge)

The StarCraft Multi-Agent Challenges+ requires agents to learn completion of multi-stage tasks and usage of environmental factors without precise reward functions. The previous challenges (SMAC) recognized as a standard benchmark of Multi-Agent Reinforcement Learning are mainly concerned with ensuring that all agents cooperatively eliminate approaching adversaries only through fine manipulation with obvious reward functions. This challenge, on the other hand, is interested in the exploration capability of MARL algorithms to efficiently learn implicit multi-stage tasks and environmental factors as well as micro-control. This study covers both offensive and defensive scenarios. In the offensive scenarios, agents must learn to first find opponents and then eliminate them. The defensive scenarios require agents to use topographic features. For example, agents need to position themselves behind protective structures to make it harder for enemies to attack.

11 PAPERS • 13 BENCHMARKS

Hanabi Learning Environment

A new challenge domain with novel problems that arise from its combination of purely cooperative gameplay with two to five players and imperfect information.

8 PAPERS • NO BENCHMARKS YET

CivRealm

Click to add a brief description of the dataset (Markdown and LaTeX enabled).

3 PAPERS • NO BENCHMARKS YET

OG-MARL (Off-the-Grid MARL Datasets)

Diverse datasets for offline multi-agent reinforcement learning research. Includes datasets for popular MARL benchmark environments such as:

3 PAPERS • NO BENCHMARKS YET

ColosseumRL

ColosseumRL is a framework for research in reinforcement learning in n-player games.

1 PAPER • NO BENCHMARKS YET

RoomEnv-v0

RoomEnv-v0 (The Room environment - v0)

The Room environment - v0 There is a newer version, v1

1 PAPER • NO BENCHMARKS YET

pursuitMW (Multi-agent pursuit in matrix world)

Multi-agent pursuit in matrix world (pursuitMW) is a partially observable Markov game (POMG) between a swarm of pursuers and a swarm of evaders. Algorithms can be developed for the pursuers, evaders, or both of them.

1 PAPER • NO BENCHMARKS YET