Papers of Multi Agent Reinforcement Learning(MARL)

weixin_34087301發表於2018-08-06

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.

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

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

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

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

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