Multi-agent Reinforcement Learning

380 papers with code • 3 benchmarks • 9 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


Most implemented papers

Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents

cts198859/deeprl_network ICML 2018

To this end, we propose two decentralized actor-critic algorithms with function approximation, which are applicable to large-scale MARL problems where both the number of states and the number of agents are massively large.

QPLEX: Duplex Dueling Multi-Agent Q-Learning

wjh720/QPLEX ICLR 2021

This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

eugenevinitsky/sequential_social_dilemma_games 10 Feb 2017

We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions.

CoLight: Learning Network-level Cooperation for Traffic Signal Control

wingsweihua/colight 11 May 2019

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

Learning Transferable Cooperative Behavior in Multi-Agent Teams

sumitsk/matrl 4 Jun 2019

While multi-agent interactions can be naturally modeled as a graph, the environment has traditionally been considered as a black box.

MAVEN: Multi-Agent Variational Exploration

AnujMahajanOxf/MAVEN NeurIPS 2019

We specifically focus on QMIX [40], the current state-of-the-art in this domain.

Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning

facebookresearch/Hanabi_SAD ICLR 2020

Learning to be informative when observed by others is an interesting challenge for Reinforcement Learning (RL): Fundamentally, RL requires agents to explore in order to discover good policies.

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

oxwhirl/wqmix NeurIPS 2020

We show in particular that this projection can fail to recover the optimal policy even with access to $Q^*$, which primarily stems from the equal weighting placed on each joint action.

Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning

alex-petrenko/sample-factory ICML 2020

In this work we aim to solve this problem by optimizing the efficiency and resource utilization of reinforcement learning algorithms instead of relying on distributed computation.