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Multi-agent Reinforcement Learning

34 papers with code · Methodology

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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

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

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

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

23 Jan 2019crowdAI/marLo

Learning in multi-agent scenarios is a fruitful research direction, but current approaches still show scalability problems in multiple games with general reward settings and different opponent types.

MULTI-AGENT REINFORCEMENT 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 incorporate cooperation in reinforcement learning (RL), two typical approaches are proposed to take the influence of other agents into consideration: (1) learning the communications (i. e., the representation of influences between agents) and (2) learning joint actions for agents.

MULTI-AGENT REINFORCEMENT LEARNING

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

ICLR 2019 eugenevinitsky/sequential_social_dilemma_games

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions.

MULTI-AGENT REINFORCEMENT LEARNING

Inequity aversion improves cooperation in intertemporal social dilemmas

NeurIPS 2018 eugenevinitsky/sequential_social_dilemma_games

Groups of humans are often able to find ways to cooperate with one another in complex, temporally extended social dilemmas.

MULTI-AGENT REINFORCEMENT LEARNING