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LAËTITIA MATIGNON
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Open Visualization
Name
Affiliation
Papers
LAËTITIA MATIGNON
Université de Franche-Comté FEMTO-ST/UFC-ENSMM-UTBM-CNRS Besançon France
19
Collaborators
Citations
PageRank
20
88
9.43
Referers
Referees
References
262
256
186
Search Limit
100
262
Publications (19 rows)
Collaborators (20 rows)
Referers (100 rows)
Referees (100 rows)
Title
Citations
PageRank
Year
Teaching Agents how to Map: Spatial Reasoning for Multi-Object Navigation.
0
0.34
2022
Solving Multi-Agent Routing Problems Using Deep Attention Mechanisms
1
0.34
2021
Heart: Using Abstract Plans As A Guarantee Of Downward Refinement In Decompositional Planning
0
0.34
2019
Multi-Robot Simultaneous Coverage And Mapping Of Complex Scene
0
0.34
2018
Multi-Robot Simultaneous Coverage And Mapping Of Complex Scene - Comparison Of Different Strategies
0
0.34
2018
Incremental And Adaptive Multi-Robot Mapping For Human Scene Observation
0
0.34
2016
Modeling Biological Agents Beyond the Reinforcement Learning Paradigm
0
0.34
2015
Distributed value functions for multi-robot exploration
12
0.67
2012
Coordinated Multi-Robot Exploration Under Communication Constraints Using Decentralized Markov Decision Processes.
16
0.71
2012
Distributed value functions for the coordination of decentralized decision makers
0
0.34
2012
Distributed value functions for the coordination of decentralized decision makers
1
0.36
2012
Independent reinforcement learners in cooperative Markov games: a survey regarding coordination problems.
5
0.38
2012
The world of independent learners is not markovian
15
1.11
2011
A Model for Verbal and Non-Verbal Human-Robot Collaboration.
4
0.58
2010
Designing Decentralized Controllers for Distributed-Air-Jet MEMS-Based Micromanipulators by Reinforcement Learning
9
0.78
2010
Design of semi-decentralized control laws for distributed-air-jet micromanipulators by reinforcement learning
1
0.35
2009
Hysteretic q-learning : an algorithm for decentralized reinforcement learning in cooperative multi-agent teams
10
0.55
2007
Improving Reinforcement Learning Speed For Robot Control
3
0.43
2006
Reward function and initial values: better choices for accelerated goal-directed reinforcement learning
11
0.79
2006
1