QueryPOMDP: POMDP-Based Communication in Multiagent Systems
By Francisco S. Melo, Matthijs T. J. Spaan, Stefan J. Witwicki. 2011
33 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Reinforcement Learning, Game & Decision Theory
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Ali Farhadi
- Mohit Bansal
- Sergey Levine
- Aniruddha Kembhavi
- Trevor Darrell
- Shimon Whiteson
- Yoshua Bengio
- Dacheng Tao
In Reinforcement Learning
- Shimon Whiteson
- Jakob N. Foerster
- Rob Fergus
- Arthur Szlam
- Sainbayar Sukhbaatar
- Ali Farhadi
- Aniruddha Kembhavi
- Sergey Levine
In Game & Decision Theory
- Shimon Whiteson
- Jakob N. Foerster
- Unnat Jain
- Junjiao Tian
- Nathan Glaser
- Ali Farhadi
- Zsolt Kira
- Nando de Freitas
Citations in the corpus
- Learning to Communicate with Deep Multi-Agent Reinforcement Learning (2016, background): “The rapid progress in recent years of machine learning, and deep learning in particular, opens the door to a new perspective on this debate.”
- Learning Multiagent Communication with Backpropagation (2016, methodology): “Others use co unic ti n, but with a p -d rmin d p otoc l [30, 19, 37, 17].”
- Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks (2016, background): “There has been a plethora of work on multi-agent reinforcement learning with communication, e.g., (Tan, 1993; Melo et al., 2011; Panait & Luke, 2005; Zhang & Lesser, 2013; Maravall et al., 2013).”
- When2com: Multi-Agent Perception via Communication Graph Grouping (2020, background): “Early works [35, 27] facilitated information flow and collaboration through pre-defined communication protocols.”
- Who2com: Collaborative Perception via Learnable Handshake Communication (2020, background): “Early attempts utilized a pre-defined communication protocol to propagate the information across agents [37, 39, 27], while dynamics of environments and a varied number of agents gave rise to the development of learnable mulit-agent communication [36, 11, 2, 14, 10, 29, 17, 35, 18, 7, 30, 9, 15, 19]”
- A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks (2020, background): “Similarly, communication and language in the multi-agent setting has been investigated [32,45,10,61,53,27,83,67,7] in maze-based setups, tabular tasks, or Markov games.”