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Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together.
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Rajeev Agrawal · 1995
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Gaussian processes in machine learning
Carl Edward Rasmussen · 2004
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Risk-sensitive reinforcement learning applied to control under constraints
Peter Geibel and Fritz Wysotzki · 2005
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Posterior consistency of gaussian process prior for nonparametric binary regression
Subhashis Ghosal, Anindya Roy, et al · 2006
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Mars reconnaissance orbiter’s high resolution imaging science experiment (hirise)
Alfred S McEwen, Eric M Eliason, James W Bergstrom, Nathan T Bridges, Candice J Hansen, W Alan Delamere, John A Grant, Virginia C Gulick, Kenneth E Herkenhoff, Laszlo Keszthelyi, et al · 2007
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A learning algorithm for risk-sensitive cost
Arnab Basu, Tirthankar Bhattacharyya, and Vivek S Borkar · 2008
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Gaussian process optimization in the bandit setting: no regret and experimental design
Niranjan Srinivas, Andreas Krause, Sham Kakade, and Matthias Seeger · 2010
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Safe exploration in markov decision processes
Teodor Mihai Moldovan and Pieter Abbeel · 2012
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Efficient gaussian process regression for large datasets
Anjishnu Banerjee, David B Dunson, and Surya T Tokdar · 2012
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An overview of recent progress in the study of distributed multi-agent coordination
Yongcan Cao, Wenwu Yu, Wei Ren, and Guanrong Chen · 2013
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Decentralized control of partially observable markov decision processes
Christopher Amato, Girish Chowdhary, Alborz Geramifard, N Kemal Ure, and Mykel J Kochenderfer · 2013
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Coordinating multi-agent reinforcement learning with limited communication
Chongjie Zhang and Victor Lesser · 2013
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Probabilistic planning for decentralized multi-robot systems
Christopher Amato, George Konidaris, Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Jonathan P How, and Leslie P Kaelbling · 2015
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Planning for decentralized control of multiple robots under uncertainty
Christopher Amato, George Konidaris, Gabriel Cruz, Christopher A Maynor, Jonathan P How, and Leslie P Kaelbling · 2015
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Decentralized control of partially observable markov decision processes using belief space macro-actions
Shayegan Omidshafiei, Ali-Akbar Agha-Mohammadi, Christopher Amato, and Jonathan P How · 2015
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Safe exploration for optimization with gaussian processes
Yanan Sui, Alkis Gotovos, Joel Burdick, and Andreas Krause · 2015
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A comprehensive survey on safe reinforcement learning
Javier García and Fernando Fernández · 2015
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Reach-avoid problems with time-varying dynamics, targets and constraints
Jaime F Fisac, Mo Chen, Claire J Tomlin, and S Shankar Sastry · 2015
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Intention-aware online pomdp planning for autonomous driving in a crowd
Haoyu Bai, Shaojun Cai, Nan Ye, David Hsu, and Wee Sun Lee · 2015
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Human-level control through deep reinforcement learning
Coordinated multi-agent imitation learning
Hoang M Le, Yisong Yue, Peter Carr, and Patrick Lucey · 2017
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Safe decentralized and reconfigurable multi-agent control with guaranteed convergence
Constantinos Vrohidis, Charalampos P Bechlioulis, and Kostas J Kyriakopoulos · 2017
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Cooperative object transport in 3d with multiple quadrotors using no peer communication
Zijian Wang, Sumeet Singh, Marco Pavone, and Mac Schwager · 2018
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Safe exploration and optimization of constrained mdps using gaussian processes
Akifumi Wachi, Yanan Sui, Yisong Yue, and Masahiro Ono · 2018
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A general safety framework for learning-based control in uncertain robotic systems
Jaime F Fisac, Anayo K Akametalu, Melanie N Zeilinger, Shahab Kaynama, Jeremy Gillula, and Claire J Tomlin · 2018
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Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Decentralized multi-agent exploration with online-learning of gaussian processes
Alberto Viseras, Thomas Wiedemann, Christoph Manss, Lukas Magel, Joachim Mueller, Dmitriy Shutin, and Luis Merino · 2016
Cited alongside, same era.
Safe exploration in finite markov decision processes with gaussian processes
Matteo Turchetta, Felix Berkenkamp, and Andreas Krause · 2016
Cited alongside, same era.
Safe controller optimization for quadrotors with gaussian processes
Felix Berkenkamp, Angela P Schoellig, and Andreas Krause · 2016
Cited alongside, same era.
An implemented theory of mind to improve human-robot shared plans execution
Sandra Devin and Rachid Alami · 2016
Cited alongside, same era.
Information gathering actions over human internal state
Dorsa Sadigh, S Shankar Sastry, Sanjit A Seshia, and Anca Dragan · 2016
Cited alongside, same era.
Comparing exploration strategies for q-learning in random stochastic mazes
Arryon D Tijsma, Madalina M Drugan, and Marco A Wiering · 2016
Cited alongside, same era.
Felipe Leno Da Silva, Matthew E Taylor, and Anna Helena Reali Costa · 2018
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Counterfactual multi-agent policy gradients
Jakob N Foerster, Gregory Farquhar, Triantafyllos Afouras, Nantas Nardelli, and Shimon Whiteson · 2018
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Understandable robots-what, why, and how
Thomas Hellström and Suna Bensch · 2018
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Multi-agent generative adversarial imitation learning
Jiaming Song, Hongyu Ren, Dorsa Sadigh, and Stefano Ermon · 2018
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Finite rank deep kernel learning
Sambarta Dasgupta, Kumar Sricharan, and Ashok Srivastava · 2018
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Learning and control using gaussian processes
Achin Jain, Truong Nghiem, Manfred Morari, and Rahul Mangharam · 2018
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Deep neural network compression for aircraft collision avoidance systems
Kyle D Julian, Mykel J Kochenderfer, and Michael P Owen · 2018
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Hierarchical game-theoretic planning for autonomous vehicles
Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, and Anca D. Dragan · 2019
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Efficient and safe exploration in deterministic markov decision processes with unknown transition models
Erdem Bıyık, Jonathan Margoliash, Shahrouz R. Alimo, and Dorsa Sadigh · 2019
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Andrea Bajcsy, Somil Bansal, Eli Bronstein, Varun Tolani, and Claire J Tomlin · 2019
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Safely probabilistically complete real-time planning and exploration in unknown environments
David Fridovich-Keil, Jaime F Fisac, and Claire J Tomlin · 2019
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