Papers in Multi-Agent Reinforcement Learning(MARL)

This is my paper lists about Multi-Agent Reinforcement Learning.

What makes this list outstanding?

• There is introduction part(or called comment) based my understanding of the papers(if there is some objective mistakes, thanks a lot if you can tell me!).

• There is score part to help you quickly find papers that may enlight and accelerate your learning.

• PS:

  • "Score" is range from 1 to 5.The higer score is, the more useful the paper is(i.e. 5 means the higest quanlity and useful to study).
  • Note that the point is based on only my personal view.

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Book and Reviews

Title	Introduction	Score
Reinforcement Learning: state of the art	A comprehensive review including POMDP and Bayesian RL	5
POMDP solution methods	A concise and detailed introduction to POMDP	4
A Concise Introduction to Decentralized POMPDs	A newbie-friendly and comprehensive book to dec-POMPDs	4
A Comprehensive Survey of Multi-agent Reinforcement Learning	An top scope to MARL, inconlusive and comprehensive!	5
Markov Decision Process in Artificial Intelligence and CS294-Sequential Decisions: Planning and Reinforcement Learning	Detailed MDP and beyond MDP	4
Multi-agent Systems:Algorithmic, Game-Theoretic, and Logic Foundations	From the view of game theory, not deep reinforcement learning	3

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Deep Dec-POMDPs

Title	Introduction	Score
Multiagent Cooperation and Competition with Deep Reinforcement Learning	The first paper looks at MADRL after dqn?	3
Deep Recurrent Q-Learning for Partially Observable MDPs	Dqn has problem: observation != state	4
Cooperative Multi-Agent Control Using Deep Reinforcement Learning	3 schemes extend DQN、DDPG、TRPO from sing-agent to multi-agent;code avaiable	4
Value-Decomposition Networks for Cooperative Multi-Agent Learning	The first paper apply decomposition in MADRL	4
QMIX: Monotonic Value Function Fatorisation for Deep Multi-agent Reinforcement Learning	Based VDN, more flexible to decomposition global Q	4

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Opponent Modeling

Title	Introduction	Score
Modeling Others using Oneself in Multi-agent Reinforcement Learning	Using opponent goal as addtional input	3
Learning Policy Representations in Multi-agent Systems	Using policy representation to cluser, classify and RL(using opponent's embedding as addtional input)	4

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Communication

Title	Introduction	Score
Emergence of Grounded Compositional Language in Multi-Agent Populations
Learning to Communicate with Deep Multi-Agent Reinforcement Learning	Communicate discrete action	4
Learning Multiagent Communication with Backpropagation	Communicate hidden state	3