https://github.com/bat67/deep-marl-papers

[WIP✏] Paper list of deep multi-agent reinforcement learning (deep MARL)
https://github.com/bat67/deep-marl-papers

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[WIP✏] Paper list of deep multi-agent reinforcement learning (deep MARL)

• Host: GitHub
• URL: https://github.com/bat67/deep-marl-papers
• Owner: bat67
• Created: 2020-08-13T02:12:16.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-08-16T14:47:49.000Z (almost 6 years ago)
• Last Synced: 2025-03-14T18:37:25.649Z (over 1 year ago)
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  • Readme: README.md

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README

          
# Deep Multi-Agent Reinforcement Learning (MARL) Papers

![overview](overview.png)

## Centralized Training, Decentralized Execution (CTDE)

Value-Decomposition Networks For Cooperative Multi-Agent Learning

https://arxiv.org/abs/1706.05296

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

https://arxiv.org/abs/1803.11485

QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning

https://arxiv.org/abs/1905.05408

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://papers.nips.cc/paper/7217-multi-agent-actor-critic-for-mixed-cooperative-competitive-environments

Counterfactual Multi-Agent Policy Gradients

https://arxiv.org/abs/1705.08926

Actor-Attention-Critic for Multi-Agent Reinforcement Learning

https://arxiv.org/abs/1810.02912

QPLEX: Duplex Dueling Multi-Agent Q-Learning

https://arxiv.org/abs/2008.01062

DOP: Off-Policy Multi-Agent Decomposed Policy Gradients

https://arxiv.org/abs/2007.12322v1

## Coordination

Hysteretic Q-learning : an algorithm for Decentralized Reinforcement Learning in Cooperative Multi-Agent Teams

https://ieeexplore.ieee.org/document/4399095

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability

https://arxiv.org/abs/1703.06182

Lenient Learning in Independent-Learner Stochastic Cooperative Games

https://jmlr.org/papers/v17/15-417.html

Lenient Multi-Agent Deep Reinforcement Learning

https://arxiv.org/abs/1707.04402

Explicitly Coordinated Policy Iteration

https://www.ijcai.org/Proceedings/2019/51

## Learning to Communicate

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

https://arxiv.org/abs/1605.06676

Learning Multiagent Communication with Backpropagation

https://arxiv.org/abs/1605.07736

Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games

https://arxiv.org/abs/1703.10069

ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning

https://arxiv.org/abs/1706.03235

Learning Attentional Communication for Multi-Agent Cooperation

https://arxiv.org/abs/1805.07733

Learning when to Communicate at Scale in Multiagent Cooperative and Competitive Tasks

https://arxiv.org/abs/1812.09755

TarMAC: Targeted Multi-Agent Communication

https://arxiv.org/abs/1810.11187

Learning Nearly Decomposable Value Functions Via Communication Minimization

https://arxiv.org/abs/1910.05366

Learning to Schedule Communication in Multi-agent Reinforcement Learning

https://arxiv.org/abs/1902.01554

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

https://arxiv.org/abs/1810.08647

InfoBot: Transfer and Exploration via the Information Bottleneck

https://arxiv.org/abs/1901.10902

## Neural Network Design

Deep reinforcement learning with relational inductive biases

https://openreview.net/forum?id=HkxaFoC9KQ

Action Semantics Network: Considering the Effects of Actions in Multiagent Systems

https://arxiv.org/abs/1907.11461

Multi-Agent Game Abstraction via Graph Attention Neural Network

https://arxiv.org/abs/1911.10715

From Few to More: Large-scale Dynamic Multiagent Curriculum Learning

https://arxiv.org/abs/1909.02790

## Opponent Exploration

Opponent Modeling in Deep Reinforcement Learning

https://arxiv.org/abs/1609.05559

Efficiently detecting switches against non-stationary opponents

http://ifaamas.org/Proceedings/aamas2017/pdfs/p920.pdf

An exploration strategy for non-stationary opponents

https://link.springer.com/article/10.1007/s10458-016-9347-3?shared-article-renderer

A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents

https://papers.nips.cc/paper/7374-a-deep-bayesian-policy-reuse-approach-against-non-stationary-agents

Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems

https://arxiv.org/abs/1709.08071

A Deep Policy Inference Q-Network for Multi-Agent Systems

https://arxiv.org/abs/1712.07893

Learning with Opponent-Learning Awareness

https://arxiv.org/abs/1709.04326

Bayes-ToMoP: A Fast Detection and Best Response Algorithm Towards Sophisticated Opponents

http://www.ifaamas.org/Proceedings/aamas2019/pdfs/p2282.pdf

Towards Efficient Detection and Optimal Response against Sophisticated Opponents

https://arxiv.org/abs/1809.04240

## Multi-Agent Exploration

Influence-Based Multi-Agent Exploration

https://arxiv.org/abs/1910.05512

Coordinated Exploration in Concurrent Reinforcement Learning

https://arxiv.org/abs/1802.01282

Scalable Coordinated Exploration in Concurrent Reinforcement Learning

https://arxiv.org/abs/1805.08948

Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems

http://proceedings.mlr.press/v80/bargiacchi18a.html

Coordinated Exploration via Intrinsic Rewards for Multi-Agent Reinforcement Learning

https://arxiv.org/abs/1905.12127