Decentralization of Multiagent Policies by Learning What to Communicate
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Vijay Kumar | Daigo Shishika | Steven W. Chen | James Paulos | Steven W. Chen | Vijay R. Kumar | James Paulos | Daigo Shishika
[1] Yoshua Bengio,et al. BinaryConnect: Training Deep Neural Networks with binary weights during propagations , 2015, NIPS.
[2] Vijay Kumar,et al. Local-game Decomposition for Multiplayer Perimeter-defense Problem , 2018, 2018 IEEE Conference on Decision and Control (CDC).
[3] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Raffaello D'Andrea,et al. Guest editorial: A revolution in the warehouse: a retrospective on Kiva Systems and the grand challenges ahead , 2012, IEEE Trans Autom. Sci. Eng..
[5] Jonathan P. How,et al. Decentralized control of partially observable Markov decision processes , 2015, 52nd IEEE Conference on Decision and Control.
[6] Bikramjit Banerjee,et al. Multi-agent reinforcement learning as a rehearsal for decentralized planning , 2016, Neurocomputing.
[7] Andrew R. Barron,et al. Minimum complexity density estimation , 1991, IEEE Trans. Inf. Theory.
[8] Craig Boutilier,et al. Planning, Learning and Coordination in Multiagent Decision Processes , 1996, TARK.
[9] Risto Miikkulainen,et al. Multiagent Learning through Neuroevolution , 2012, WCCI.
[10] Shimon Whiteson,et al. Counterfactual Multi-Agent Policy Gradients , 2017, AAAI.
[11] Yi Wu,et al. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments , 2017, NIPS.
[12] Morten Bisgaard,et al. Adaptive Surveying and Early Treatment of Crops with a Team of Autonomous Vehicles , 2011, ECMR.
[13] Antonio G. Marques,et al. Convolutional Neural Network Architectures for Signals Supported on Graphs , 2018, IEEE Transactions on Signal Processing.
[14] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[15] Nikos A. Vlassis,et al. Optimal and Approximate Q-value Functions for Decentralized POMDPs , 2008, J. Artif. Intell. Res..
[16] Vijay Kumar,et al. Distributed Search and Rescue with Robot and Sensor Teams , 2003, FSR.
[17] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[18] Andrew Howard,et al. Multi-robot Simultaneous Localization and Mapping using Particle Filters , 2005, Int. J. Robotics Res..
[19] Pritish Narayanan,et al. Deep Learning with Limited Numerical Precision , 2015, ICML.
[20] Michael Trentini,et al. Multiple‐Robot Simultaneous Localization and Mapping: A Review , 2016, J. Field Robotics.
[21] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[22] Goldie Nejat,et al. Multirobot Cooperative Learning for Semiautonomous Control in Urban Search and Rescue Applications , 2016, J. Field Robotics.
[23] Barnabás Póczos,et al. Deep Learning with Sets and Point Clouds , 2016, ICLR.
[24] David Fridovich-Keil,et al. Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach , 2017, NIPS.
[25] Bart De Schutter,et al. A Comprehensive Survey of Multiagent Reinforcement Learning , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
[26] Shimon Whiteson,et al. Learning to Communicate to Solve Riddles with Deep Distributed Recurrent Q-Networks , 2016, ArXiv.
[27] Rob Fergus,et al. Learning Multiagent Communication with Backpropagation , 2016, NIPS.
[28] Antonio Barrientos,et al. Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots , 2011, J. Field Robotics.
[29] Barnabás Póczos,et al. Equivariance Through Parameter-Sharing , 2017, ICML.