@string{ijcai20 = "Proceedings of the Twenty-Nineth International Joint Conference on Artificial Intelligence"}
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@string{ecai12 = "Proceedings of the European Conference on Artificial Intelligence"}
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@string{ecml14 = "Proceedings of the Twenty-Fourth European Conference on Machine Learning"}

@string{icml94 = "Proceedings of the Eleventh International Conference on Machine Learning"}
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@string{icml97 = "Proceedings of the Fourteenth International Conference on Machine Learning"}
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@string{aaai21 = "Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence"}
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@string{aaai15 = "Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence"}
@string{aaai14 = "Proceedings of the Twenty-Eighth  AAAI Conference on Artificial Intelligence"}
@string{aaai13 = "Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence"}
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@string{aaai09 = "DOES NOT EXIST"}
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@string{aaai07 = "Proceedings of the Twenty-Second  AAAI Conference on Artificial Intelligence"}
@string{aaai06 = "Proceedings of the Twenty-First AAAI Conference on Artificial Intelligence"}
@string{aaai05 = "Proceedings of the Twentieth National Conference on Artificial Intelligence"}
@string{aaai04 = "Proceedings of the Nineteenth National Conference on Artificial Intelligence"}
@string{aaai02 = "Proceedings of the Eighteenth National Conference on Artificial Intelligence"}
@string{aaai00 = "Proceedings of the Seventeenth National Conference on Artificial Intelligence"}
@string{aaai99 = "Proceedings of the Sixteenth National Conference on Artificial Intelligence"}
@string{aaai98 = "Proceedings of the Fifteenth National Conference on Artificial Intelligence"}
@string{aaai97 = "Proceedings of the Fourteenth National Conference on Artificial Intelligence"}
@string{aaai96 = "Proceedings of the Thirteenth National Conference on Artificial Intelligence"}
@string{aaai94 = "Proceedings of the Twelfth National Conference on Artificial Intelligence"}
@string{aaai93 = "Proceedings of the Eleventh National Conference on Artificial Intelligence"}
@string{aaai92 = "Proceedings of the Tenth National Conference on Artificial Intelligence"}
@string{aaai91 = "Proceedings of the Ninth National Conference on Artificial Intelligence"}
@string{aaai90 = "Proceedings of the Eighth National Conference on Artificial Intelligence"}
@string{aaai87 = "Proceedings of the Sixth National Conference on Artificial Intelligence"}

@string{colt10 = "Proceedings of the Twenty-Third Annual Conference on Computational Learning Theory"}
@string{colt01 = "Proceedings of the Fourteenth Annual Conference on Computational Learning Theory"}
@string{colt00 = "Proceedings of the Thirteenth Annual Conference on Computational Learning Theory"}
@string{colt99 = "Proceedings of the Twelfth Annual Conference on Computational Learning Theory"}

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@string{nips94 = "Advances in Neural Information Processing Systems 7"}
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@string{nips96 = "Advances in Neural Information Processing Systems 9"}
@string{nips97 = "Advances in Neural Information Processing Systems 10"}
@string{nips98 = "Advances in Neural Information Processing Systems 11"}
@string{nips99 = "Advances in Neural Information Processing Systems 12"}
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@string{nips01 = "Advances in Neural Information Processing Systems 14"}
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@string{nips03 = "Advances in Neural Information Processing Systems 16"}
@string{nips04 = "Advances in Neural Information Processing Systems 17"}
@string{nips05 = "Advances in Neural Information Processing Systems 18"}
@string{nips06 = "Advances in Neural Information Processing Systems 19"}
@string{nips07 = "Advances in Neural Information Processing Systems 20"}
@string{nips08 = "Advances in Neural Information Processing Systems 21"}
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@string{nips10 = "Advances in Neural Information Processing Systems 23"}
@string{nips11 = "Advances in Neural Information Processing Systems 24"}
@string{nips12 = "Advances in Neural Information Processing Systems 25"}
@string{nips13 = "Advances in Neural Information Processing Systems 26"}
@string{nips14 = "Advances in Neural Information Processing Systems 27"}
@string{nips15 = "Advances in Neural Information Processing Systems 28"}
@string{nips16 = "Advances in Neural Information Processing Systems 29"}
@string{nips17 = "Advances in Neural Information Processing Systems 30"}
@string{nips18 = "Advances in Neural Information Processing Systems 31"}
@string{nips19 = "Advances in Neural Information Processing Systems 32"}

@string{uai15 = "Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence"}
@string{uai14 = "Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence"}
@string{uai13 = "Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence"}
@string{uai12 = "Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence"}
@string{uai11 = "Proceedings of the Twenty-Seventh Conference on Uncertainty in Artificial Intelligence"}
@string{uai10 = "Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence"}
@string{uai09 = "Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence"}
@string{uai08 = "Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence"}
@string{uai07 = "Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence"}
@string{uai06 = "Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence"}
@string{uai05 = "Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence"}
@string{uai04 = "Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence"}
@string{uai03 = "Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai02 = "Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai01 = "Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai00 = "Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai99 = "Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai98 = "Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai97 = "Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai96 = "Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence"}
@string{uai95 = "Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence"}
@string{uai94 = "Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence"}
@string{uai93 = "Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence"}
@string{uai92 = "Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence"}
@string{uai91 = "Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence"}

@string{aamas15 = "Proceedings of the Fourteenth International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas14 = "Proceedings of the Thirteenth International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas13 = "Proceedings of the Twelfth International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas12 = "Proceedings of the Eleventh International  Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas11 = "Proceedings of the Tenth International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas10 = "Proceedings of the Ninth International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas09 = "Proceedings of the Eighth International  Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas08 = "Proceedings of the Seventh International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas07 = "Proceedings of the Sixth International  Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas06 = "Proceedings of the Fifth International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas05 = "Proceedings of the Fourth International  Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas04 = "Proceedings of the Third International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas03 = "Proceedings of the Second International Conference on Autonomous Agents and Multiagent Systems"}
@string{aamas02 = "Proceedings of the First International  Conference on Autonomous Agents and Multiagent Systems"}

@string{ictai21 = "Proceedings of the Thirty-Third International Conference on Tools with Artificial Intelligence"},
@string{ictai12 = "Proceedings of the Twenty-Fourth International Conference on Tools with Artificial Intelligence"},
@string{ictai10 = "Proceedings of the Twenty-Second International Conference on Tools with Artificial Intelligence"},

@string{aiide12 = "Proceedings of the 8th {AAAI} Conference on Artificial Intelligence and Interactive Digital Entertainment"}
@string{aiide18 = "Proceedings of the 14th {AAAI} Conference on Artificial Intelligence and Interactive Digital Entertainment"}
@string{cgc03 = "Advances in Computer Games Conference 10"}
@string{tciaig12 = "IEEE Transactions on Computational Intelligence and AI in Games"}
@string{icra = "Proceedings of the International Conference on Robotics and Automation"}
@string{iros = "Proceedings of the International Conference on Intelligent Robots and Systems"}
@string{rss = "Proceedings of the Robotics: Science and Systems Conference"}
@string{rss08 = "Proceedings of the Robotics: Science and Systems {IV} Conference"}
@string{rss09 = "Proceedings of the Robotics: Science and Systems {V} Conference"}
@string{cdc81 = "20th IEEE Conference on Decision and Control"}
@string{cdc13 = "54th IEEE Conference on Decision and Control"}


@string{ijait = "International Journal of Artiticial Intelligence Tools"}
@string{jaamas = "Journal of Autonomous Agents and Multi-Agent Systems"}
@string{nips = "Advances in Neural Information Processing Systems"}
@string{ijcnn = "International Joint Conference on Neural Network"}
@string{aij = "Artificial Intelligence"}
@string{jair = "Journal of Artificial Intelligence Research"}
@string{aimag = "AI Magazine"}
@string{mlj = "Machine Learning"}
@string{nc = "Neural Computation"}
@string{nn = "Neural Networks"}
@string{or = "Operations Research"}
@string{siamRev = "SIAM Reviews"}
@string{siamJNumAnal = "SIAM Journal on Numerical Analysis"}
@string{jima = "Journal of the Institute of Mathematics and Applications"}
@string{siamJComp = "SIAM Journal on Computing"}
@string{cacm = "Communications of the ACM"}
@string{ic = "Information and Computation"}
@string{bams = "Bulletin of the American Mathematical Society"}
@string{pnas = "Proceedings of the National Academy of Science"}
@string{ria = "Revue d'Intelligence Artificielle"}
@string{corr = "Computing Research Repository (arXiv/CoRR)"}

  
@article{Bernstein02,
  Author	= {D.S. Bernstein and R. Givan and N. Immerman and Shlomo
		  Zilberstein},
  Title		= {The Complexity of Decentralized Control of {Markov}
		  Decision Processes},
  Year		= {2002},
  Journal	= {Math. of Operations Research},
  Volume	= {27},
  Number	= {4},
  comment	= {cf Bernstein00 - pdf}
}

@article{MADANI20035,
	author = {Omid Madani and Steve Hanks and Anne Condon},
	issn = {0004-3702},
	journal = {Artificial Intelligence},
	keywords = {Probabilistic planning, Undecidability, Computability, Markov decision processes, Computational complexity, Infinity-horizon, Partial observability, Unobservability, Stochastic optimization, Discounted},
	note = {Planning with Uncertainty and Incomplete Information},
	number = {1},
	pages = {5-34},
	title = {On the undecidability of probabilistic planning and related stochastic optimization problems},
	volume = {147},
	year = {2003},
}

@book{10.5555/2967142,
author = {Oliehoek, Frans A. and Amato, Christopher},
title = {A Concise Introduction to Decentralized {POMDP}s},
year = {2016},
isbn = {3319289276},
publisher = {Springer Publishing Company, Incorporated},
edition = {1st},
abstract = {This book introduces multiagent planning under uncertainty as formalized by decentralized partially observable Markov decision processes (Dec-POMDPs). The intended audience is researchers and graduate students working in the fields of artificial intelligence related to sequential decision making: reinforcement learning, decision-theoretic planning for single agents, classical multiagent planning, decentralized control, and operations research.}
}

@inproceedings{Wu2013,
author = {Wu, Feng and Zilberstein, Shlomo and Jennings, Nicholas R.},
title = {{Monte-Carlo} Expectation Maximization for Decentralized {POMDP}s},
year = {2013},
isbn = {9781577356332},
publisher = {AAAI Press},
abstract = {We address two significant drawbacks of state-of-the-art solvers of decentralized POMDPs (DECPOMDPs): the reliance on complete knowledge of the model and limited scalability as the complexity of the domain grows. We extend a recently proposed approach for solving DEC-POMDPs via a reduction to the maximum likelihood problem, which in turn can be solved using EM. We introduce a model-free version of this approach that employs Monte-Carlo EM (MCEM). While a na\"{\i}ve implementation of MCEM is inadequate in multiagent settings, we introduce several improvements in sampling that produce high-quality results on a variety of DEC-POMDP benchmarks, including large problems with thousands of agents.},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence},
pages = {397–403},
numpages = {7},
location = {Beijing, China},
series = {IJCAI '13}
}

@inproceedings{Liu2015,
  author =	 {Miao Liu and Christopher Amato and Xuejun Liao and Lawrence Carin and Jonathan P How},
  booktitle = {Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence},
  keywords =	 {Technical Papers — Sequential Decision Making},
  title =	 {Stick-Breaking Policy Learning in {Dec-POMDPs}},
  year =	 2015,
}

@InProceedings{pmlr-v80-dibangoye18a,
  title = 	 {Learning to Act in Decentralized Partially Observable {MDP}s},
  author =       {Dibangoye, Jilles and Buffet, Olivier},
  booktitle = 	 {Proceedings of the 35th International Conference on Machine Learning},
  pages = 	 {1233--1242},
  year = 	 {2018},
  editor = 	 {Dy, Jennifer and Krause, Andreas},
  volume = 	 {80},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {10--15 Jul},
  publisher =    {PMLR},
  abstract = 	 {We address a long-standing open problem of reinforcement learning in decentralized partially observable Markov decision processes. Previous attempts focussed on different forms of generalized policy iteration, which at best led to local optima. In this paper, we restrict attention to plans, which are simpler to store and update than policies. We derive, under certain conditions, the first near-optimal cooperative multi-agent reinforcement learning algorithm. To achieve significant scalability gains, we replace the greedy maximization by mixed-integer linear programming. Experiments show our approach can learn to act near-optimally in many finite domains from the literature.}
}


@Article{	  AmaBerZil-jaamas10,
  Author	= {Christopher Amato and Daniel S. Bernstein and Shlomo
		  Zilberstein},
  Title		= {Optimizing fixed-size stochastic controllers for {POMDPs}
		  and decentralized {POMDPs}},
  Journal	= jaamas,
  Volume	= {21},
  Number	= {3},
  Year		= {2010},
  DOI		= {10.1007/s10458-009-9103-z}
}

@Article{	  KumZilTou-jair15,
  Author	= {Akshat Kumar and Shlomo Zilberstein and Marc Toussaint},
  Title		= {Probabilistic Inference Techniques for Scalable Multiagent
		  Decision Making},
  Journal	= jair,
  Volume	= {53},
  Year		= {2015},
  DOI		= {10.1613/jair.4649}
}

@InProceedings{	  PajPel-ijcai11,
  Author	= {Joni Pajarinen and Jaakko Peltonen},
  Title		= {Efficient Planning for Factored Infinite-Horizon
		  {DEC-POMDPs}},
  BookTitle	= ijcai11,
  month_	= {July},
  Year		= 2011
}

@InProceedings{	  PajPel-nips11,
  Author	= {Pajarinen, Joni and Peltonen, Jaakko},
  Title		= {Periodic Finite State Controllers for Efficient {POMDP}
		  and {DEC-POMDP} Planning},
  BookTitle	= nips11,
  volume_	= {24},
  Year		= {2011}
}

@InProceedings{	  MacIsb-nips13,
  Title		= {Point Based Value Iteration with Optimal Belief
		  Compression for {Dec-POMDPs}},
  Author	= {MacDermed, Liam C. and Isbell, Charles},
  BookTitle	= nips13,
  Year		= {2013},
  comment	= {pdf}
}


@InProceedings{	  SzeChaZil-uai05,
  Title		= {{MAA*}: A Heuristic Search Algorithm for Solving
		  Decentralized {POMDPs}},
  Author	= {Szer, Daniel and Charpillet, François and Zilberstein,
		  Shlomo},
  BookTitle	= uai05,
  optbooktitle	= {Proceedings of the 21st Conference on Uncertainty in
		  Artificial Intelligence (UAI-05)},
  Year		= {2005}
}


@Article{DibAmaBufCha-jair16,
  Author	= {Jilles Dibangoye and Chris Amato and Olivier Buffet and
		  Fran\c{c}ois Charpillet},
  Title		= {Optimally Solving {Dec-POMDPs} as Continuous-State
		  {MDPs}},
  Journal	= jair,
  Volume	= {55},
  Year		= {2016},
  Keywords	= {international}
}

@inproceedings{dibangoye2014error,
  title={Error-bounded approximations for infinite-horizon discounted decentralized {POMDP}s},
  author={Dibangoye, Jilles S and Buffet, Olivier and Charpillet, Fran{\c{c}}ois},
  booktitle={Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={338--353},
  year={2014},
  organization={Springer}
}

@inproceedings{InfJESP, 
 author={You, Yang and Thomas, Vincent and Colas, Francis and Buffet, Olivier}, 
  booktitle={Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI)},
  title={Solving infinite-horizon {Dec-POMDPs} using Finite State Controllers within {JESP}},   
  year={2021}
}

@article{kumar2012arxiv,
  title={Anytime planning for decentralized {POMDPs} using expectation maximization},
  author={Kumar, Akshat and Zilberstein, Shlomo},
  journal={arXiv preprint arXiv:1203.3490},
  year={2012}
}

@inproceedings{kumar2012,
  title={Anytime planning for decentralized {POMDPs} using expectation maximization},
  author={Kumar, Akshat and Zilberstein, Shlomo},
  booktitle = {Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI)},
  month = {July},
  year = {2010},
  pages = {294--301},
  url = {https://dl.acm.org/doi/abs/10.5555/3023549.3023584},
  year={2012}
}


@inproceedings{BerHanZil-ijcai05,
  author =	 {Bernstein, Daniel S. and Hansen, Eric A. and Zilberstein, Shlomo},
  title =	 {Bounded Policy Iteration for Decentralized {POMDPs}},
  year =	 2005,
  publisher =	 {Morgan Kaufmann Publishers Inc.},
  abstract =	 {We present a bounded policy iteration algorithm for infinite-horizon decentralized POMDPs. Policies are represented as joint stochastic finite-state controllers, which consist of a local controller for each agent. We also let a joint controller include a correlation device that
                  allows the agents to correlate their behavior without exchanging information during execution, and show that this leads to improved performance. The algorithm uses a fixed amount of memory, and each iteration is guaranteed to produce a controller with value at least as high as the
                  previous one for all possible initial state distributions. For the case of a single agent, the algorithm reduces to Poupart and Boutilier's bounded policy iteration for POMDPs.},
  booktitle =	 ijcai05,
  pages =	 {1287--1292},
  location =	 {Edinburgh, Scotland},
}

@InProceedings{	  MeuKimKaeCas-uai99,
  Author	= {N. Meuleau and K.-E. Kim and L.P. Kaelbling and A.R.
		  Cassandra},
  Title		= {Solving {POMDP}s by searching the space of finite policies},
  BookTitle	= uai99,
  Year		= {1999},
  comment	= {ps.gz - resume}
}

@InProceedings{	  Hansen-nips97,
  Author	= {Hansen, Eric},
  BookTitle	= nips97,
  Title		= {An Improved Policy Iteration Algorithm for Partially
		  Observable {MDPs}},
  volume_	= {10},
  Year		= {1998}
}


@inproceedings{NIPS2010_edfbe1af,
  author =	 {Silver, David and Veness, Joel},
  booktitle =	 {Advances in Neural Information Processing Systems},
  publisher =	 {Curran Associates, Inc.},
  title =	 {{Monte-Carlo} Planning in Large {POMDPs}},
  volume =	 23,
  year =	 2010
}

@inproceedings{JESP,
  title =	 {Taming decentralized {POMDPs}: Towards efficient policy computation for multiagent settings},
  author =	 {Nair, Ranjit and Tambe, Milind and Yokoo, Makoto and Pynadath, David and Marsella, Stacy},
  booktitle =	 ijcai03,
  volume =	 3,
  pages =	 {705--711},
  year =	 2003,
  organization = {Citeseer}
}

@inproceedings{sarsop,
  author =	 {Hanna Kurniawati and David Hsu and Wee Sun Lee},
  title =	 {{SARSOP}: Efficient point-based {POMDP} planning by approximating optimally reachable belief spaces},
  booktitle =	 {Proc. of Robotics: Science and Systems (RSS)},
  year =	 2008
}


@incollection{bai2010monte,
  title={{Monte Carlo} value iteration for continuous-state {POMDPs}},
  author={Bai, Haoyu and Hsu, David and Lee, Wee Sun and Ngo, Vien A},
  booktitle={Algorithmic foundations of robotics IX},
  pages={175--191},
  year={2010},
  publisher={Springer}
}

@article{Pynadath-jair02,
  Title =	 {The Communicative Multiagent Team Decision Problem: Analyzing Teamwork Theories and Models},
  Volume =	 16,
  ISSN =	 {1076-9757},
  DOI =		 {10.1613/jair.1024},
  Journal =	 jair,
  Author =	 {Pynadath, David V. and Tambe, Milind},
  Year =	 2002,
}

@inproceedings{Amato2007UAI,
  author =	 {Amato, Christopher and Bernstein, Daniel and Zilberstein, Shlomo},
  year =	 2012,
  title =	 {Optimizing Memory-Bounded Controllers for Decentralized {POMDPs}},
  booktitle =	 uai07
}

@inproceedings{amato2009incremental,
  title={Incremental policy generation for finite-horizon {DEC-POMDP}s},
  author={Amato, Christopher and Dibangoye, Jilles Steeve and Zilberstein, Shlomo},
  booktitle=icaps09,
  year={2009}
}

@article{Seuken2007ImprovedMD,
  title={Improved Memory-Bounded Dynamic Programming for Decentralized {POMDP}s},
  author={Sven Seuken and Shlomo Zilberstein},
  journal={ArXiv},
  year={2007},
  volume={abs/1206.5295}
}

@inproceedings{amato2009achieving,
  title={Achieving goals in decentralized {POMDP}s},
  author={Amato, Christopher and Zilberstein, Shlomo},
  booktitle={Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems},
  pages={593--600},
  year={2009}
}

@InProceedings{	  AmaBonZil-aaai10,
  Author =	 {Christopher Amato and Blai Bonet and Shlomo Zilberstein},
  Title =	 {Finite-State Controllers Based on {Mealy} Machines for Centralized and Decentralized {POMDPs}},
  BookTitle =	 aaai10,
  Year =	 2010
}


@inproceedings{lee2019improved,
  title={Improved cooperative multi-agent reinforcement learning algorithm augmented by mixing demonstrations from centralized policy},
  author={Lee, Hyun-Rok and Lee, Taesik},
  booktitle={Proceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems},
  pages={1089--1098},
  year={2019}
}

@article{KRAEMER201682,
title = {Multi-agent reinforcement learning as a rehearsal for decentralized planning},
journal = {Neurocomputing},
volume = {190},
pages = {82-94},
year = {2016},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2016.01.031},
url = {https://www.sciencedirect.com/science/article/pii/S0925231216000783},
author = {Landon Kraemer and Bikramjit Banerjee},
keywords = {Multi-agent reinforcement learning, Decentralized planning},
abstract = {Decentralized partially observable Markov decision processes (Dec-POMDPs) are a powerful tool for modeling multi-agent planning and decision-making under uncertainty. Prevalent Dec-POMDP solution techniques require centralized computation given full knowledge of the underlying model. Multi-agent reinforcement learning (MARL) based approaches have been recently proposed for distributed solution of Dec-POMDPs without full prior knowledge of the model, but these methods assume that conditions during learning and policy execution are identical. In some practical scenarios this may not be the case. We propose a novel MARL approach in which agents are allowed to rehearse with information that will not be available during policy execution. The key is for the agents to learn policies that do not explicitly rely on these rehearsal features. We also establish a weak convergence result for our algorithm, RLaR, demonstrating that RLaR converges in probability when certain conditions are met. We show experimentally that incorporating rehearsal features can enhance the learning rate compared to non-rehearsal-based learners, and demonstrate fast, (near) optimal performance on many existing benchmark Dec-POMDP problems. We also compare RLaR against an existing approximate Dec-POMDP solver which, like RLaR, does not assume a priori knowledge of the model. While RLaR׳s policy representation is not as scalable, we show that RLaR produces higher quality policies for most problems and horizons studied.}
}

@inproceedings{KumMosZil-icaps16,
  author    = {Akshat Kumar and
               Hala Mostafa and
               Shlomo Zilberstein},
  editor_    = {Amanda Jane Coles and
               Andrew Coles and
               Stefan Edelkamp and
               Daniele Magazzeni and
               Scott Sanner},
  title     = {Dual Formulations for Optimizing {Dec-POMDP} Controllers},
  booktitle = {Proceedings of the Twenty-Sixth International Conference on Automated
               Planning and Scheduling ({ICAPS})},
 bak = {2016, London, UK, June 12-17, 2016},
  pages     = {202--210},
  publisher = {{AAAI} Press},
  year      = {2016},
  url       = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13124},
  timestamp = {Wed, 08 Jun 2016 12:49:09 +0200},
  biburl    = {https://dblp.org/rec/conf/aips/KumarMZ16.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{VDN2017,
  title={Value-decomposition networks for cooperative multi-agent learning},
  author={Sunehag, Peter and Lever, Guy and Gruslys, Audrunas and Czarnecki, Wojciech Marian and Zambaldi, Vinicius and Jaderberg, Max and Lanctot, Marc and Sonnerat, Nicolas and Leibo, Joel Z and Tuyls, Karl and others},
  journal={arXiv preprint arXiv:1706.05296},
  year={2017}
}

@inproceedings{rashid2018qmix,
  title={{Qmix}: Monotonic value function factorisation for deep multi-agent reinforcement learning},
  author={Rashid, Tabish and Samvelyan, Mikayel and Schroeder, Christian and Farquhar, Gregory and Foerster, Jakob and Whiteson, Shimon},
  booktitle={International conference on machine learning},
  pages={4295--4304},
  year={2018},
  organization={PMLR}
}

@article{rashid2020weighted,
  title={Weighted {Qmix}: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning},
  author={Rashid, Tabish and Farquhar, Gregory and Peng, Bei and Whiteson, Shimon},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={10199--10210},
  year={2020}
}

@inproceedings{son2019qtran,
  title={Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning},
  author={Son, Kyunghwan and Kim, Daewoo and Kang, Wan Ju and Hostallero, David Earl and Yi, Yung},
  booktitle={International conference on machine learning},
  pages={5887--5896},
  year={2019},
  organization={PMLR}
}

@inproceedings{PBVI,
  ABSTRACT = {This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points and then tracking the value and its derivative for those points only. By using stochastic trajectories to choose belief points, and by maintaining only one value hyper-plane per point, PBVI successfully solves large problems: we present results on a robotic laser tag problem as well as three test domains from the literature.},
  AUTHOR = {Pineau, Joelle and Gordon, Geoff and Thrun, Sebastian},
  LOCATION = {Acapulco, Mexico},
  PUBLISHER = {Morgan Kaufmann Publishers Inc.},
  BOOKTITLE = ijcai03,
  year = {2003},
  PAGES = {1025--1030},
  TITLE = {Point-Based Value Iteration: An Anytime Algorithm for {POMDPs}},
}

@inproceedings{Smith_2004_HSVI,
  ABSTRACT = {We present a novel POMDP planning algorithm called heuristic search value iteration (HSVI). HSVI is an anytime algorithm that returns a policy and a provable bound on its regret with respect to the optimal policy. HSVI gets its power by combining two well-known techniques: attention-focusing search heuristics and piecewise linear convex representations of the value function. HSVI's soundness and convergence have been proven. On some bench-mark problems from the literature, HSVI displays speedups of greater than 100 with respect to other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to a new rover exploration problem 10 times larger than most POMDP problems in the literature.},
  AUTHOR = {Smith, Trey and Simmons, Reid},
  LOCATION = {Banff, Canada},
  PUBLISHER = {AUAI Press},
  BOOKTITLE = uai04,
  year = {2004},
  ISBN = {0974903906},
  PAGES = {520--527},
  TITLE = {Heuristic Search Value Iteration for POMDPs},
}

@inproceedings{beygelzimer2006cover,
  title={Cover Trees for Nearest Neighbor},
  author={Beygelzimer, Alina and Kakade, Sham and Langford, John},
  booktitle_={Proceedings of the 23rd international conference on Machine learning},
  booktitle = icml06,
  pages={97--104},
  year={2006}
}

@article{10.1145/361002.361007,
author = {Bentley, Jon Louis},
title = {Multidimensional Binary Search Trees Used for Associative Searching},
year = {1975},
issue_date_ = {Sept. 1975},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {18},
number = {9},
issn = {0001-0782},
url = {https://doi.org/10.1145/361002.361007},
doi = {10.1145/361002.361007},
journal = {Communications of the ACM},
month = {sep},
pages = {509--517},
numpages = {9},
keywords = {nearest neighbor queries, key, attribute, binary tree insertion, partial match queries, binary search trees, associative retrieval, information retrieval system, intersection queries}
}

  

@inproceedings{sunberg2018online,
  title={Online Algorithms for {POMDPs} with Continuous State, Action, and Observation Spaces},
  author={Sunberg, Zachary N. and Kochenderfer, Mykel J.},
  booktitle=icaps18,
  year={2018}
}
