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Multi-agent Reinforcement Learning

70 papers with code · Methodology

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.

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

2 Dec 2017geek-ai/MAgent

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

RLCard: A Toolkit for Reinforcement Learning in Card Games

10 Oct 2019datamllab/rlcard

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 MULTI-AGENT REINFORCEMENT LEARNING

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

NeurIPS 2019 TorchCraft/TorchCraftAI

While centralized reinforcement learning methods can optimally solve small MAC instances, they do not scale to large problems and they fail to generalize to scenarios different from those seen during training.

MULTI-AGENT REINFORCEMENT LEARNING STARCRAFT STRUCTURED PREDICTION

Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning

19 Mar 2020oxwhirl/pymarl

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.

MULTI-AGENT REINFORCEMENT LEARNING STARCRAFT

The StarCraft Multi-Agent Challenge

11 Feb 2019oxwhirl/pymarl

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

MULTI-AGENT REINFORCEMENT LEARNING STARCRAFT STARCRAFT II

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

ICML 2018 oxwhirl/pymarl

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 STARCRAFT II

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

NeurIPS 2016 iassael/learning-to-communicate

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility.

MULTI-AGENT REINFORCEMENT LEARNING Q-LEARNING

CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario

13 May 2019cityflow-project/CityFlow

The most commonly used open-source traffic simulator SUMO is, however, not scalable to large road network and large traffic flow, which hinders the study of reinforcement learning on traffic scenarios.

MULTI-AGENT REINFORCEMENT LEARNING

CoLight: Learning Network-level Cooperation for Traffic Signal Control

11 May 2019cityflow-project/CityFlow

To enable cooperation of traffic signals, in this paper, we propose a model, CoLight, which uses graph attentional networks to facilitate communication.

MULTI-AGENT REINFORCEMENT LEARNING