A Concise Introduction to Decentralized POMDPs
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Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Feriani, A., & Hossain, E. (2021) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2021.3063822 · Cited by 410 · see citing works
MARNet: Backdoor Attacks Against Cooperative Multi-Agent Reinforcement Learning
Chen, Y., Zheng, Z., & Gong, X. (2023) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2022.3207429 · Cited by 32 · see citing works
Memory-Limited Partially Observable Stochastic Control and Its Mean-Field Control Approach
Tottori, T., & Kobayashi, T. J. (2022) · Entropy · doi:10.3390/e24111599 · Cited by 11 · see citing works
Decentralized Stochastic Control with Finite-Dimensional Memories: A Memory Limitation Approach
Tottori, T., & Kobayashi, T. J. (2023) · Entropy · doi:10.3390/e25050791 · Cited by 7 · see citing works
$τ^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
Barres, V., Dong, H., Ray, S., Si, X., & Narasimhan, K. (2025) · arXiv · doi:10.48550/arxiv.2506.07982 · Cited by 1 · see citing works
“This is formalized using a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) [15].”
NavFormer: A Transformer Architecture for Robot Target-Driven Navigation in Unknown and Dynamic Environments
Wang, H., Tan, A. H., & Nejat, G. (2024) · arXiv · Cited by 40
“GC-POMDP is described as a tuple ( 𝒮 , ℊ , 𝒜 , 𝒫 , ℛ , Ω , 𝒪 ) , where 𝒮 denotes the state space, and ℊ is the set of target RGB images.”
- Authors
- Oliehoek, F. A., & Amato, C.
- Affiliations
- University of Liverpool (GB)
- Massachusetts Institute of Technology (US)
- Venue
- SpringerBriefs in Intelligent Systems
- Publisher
- Springer International Publishing
- Published
- 2016
- ISSN
- 2196-548X (ISSN-L)
- 2196-5498
- OpenAlex
- W2747213132
- Cited by
- 968
- References
- 0
- Citations by year
- 2026: 115 · 2025: 178 · 2024: 154 · 2023: 173 · 2022: 106 · 2021: 119 · 2020: 56 · 2019: 29 · 2018: 23 · 2017: 14 · 2016: 1
- FWCI
- 5.29
- Type
- book
- Language
- en
- Open access
- closed
- Locations
- SpringerBriefs in intelligent systems (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- DIAL (Catholic University of Leuven) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Multi-Agent Systems and Negotiation
- Keywords
- decentralized POMDPs, partially observable Markov decision processes, sequential decision making, multi-agent planning, decentralized control, decision-theoretic planning, reinforcement learning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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