Multi-agent Reinforcement Learning

149 papers with code • 1 benchmarks • 4 datasets

The target of Multi-agent Reinforcement Learning is to solve complex problems by integrating multiple agents that focus on different sub-tasks. In general, there are two types of multi-agent systems: independent and cooperative systems.

Source: Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports

Subtasks


Greatest papers with code

Patch AutoAugment

kornia/kornia 20 Mar 2021

At each step, PAA samples the most effective operation for each patch based on its content and the semantics of the whole image.

Data Augmentation Fine-Grained Image Recognition +2

DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning

kwai/DouZero 11 Jun 2021

Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.

Game of Poker Multi-agent Reinforcement Learning

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

geek-ai/MAgent 2 Dec 2017

Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents.

Multi-agent Reinforcement Learning Platform

RLCard: A Toolkit for Reinforcement Learning in Card Games

datamllab/rlcard 10 Oct 2019

The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.

Board Games Game of Poker +1

Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences

openai/multi-agent-emergence-environments NeurIPS 2020

Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments.

Multi-agent Reinforcement Learning

Deep Multi-agent Reinforcement Learning for Highway On-Ramp Merging in Mixed Traffic

eleurent/highway-env 12 May 2021

On-ramp merging is a challenging task for autonomous vehicles (AVs), especially in mixed traffic where AVs coexist with human-driven vehicles (HDVs).

Autonomous Vehicles Curriculum Learning +1

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

oxwhirl/pymarl 19 Mar 2020

At the same time, it is often possible to train the agents in a centralised fashion where global state information is available and communication constraints are lifted.

SMAC Starcraft

The StarCraft Multi-Agent Challenge

oxwhirl/pymarl 11 Feb 2019

In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.

SMAC Starcraft +1

QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

oxwhirl/pymarl ICML 2018

At the same time, it is often possible to train the agents in a centralised fashion in a simulated or laboratory setting, where global state information is available and communication constraints are lifted.

Multi-agent Reinforcement Learning Starcraft +1