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Multi-agent systems (MAS) need to adaptively cope with dynamic environments, changing agent populations, and diverse tasks.
Learning Transferable Cooperative Behavior in Multi-Agent Teams
Agarwal, A.; Kumar, S.; and Sycara, K. 2019 · 1906
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Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI; :; Berner, C.; Brockman, G.; Chan, B.; Cheung, V.; Dębiak, P.; Dennison, C.; Farhi, D.; Fischer, Q.; Hashme, S.; Hesse, C.; Józefowicz, R.; Gray, S.; Olsson, C.; Pachocki, J.; Petrov, M.; d. O. Pinto, H. P.; Raiman, J.; Salimans, T.; Schlatter, J.; Schneider, J.; Sidor, S.; Sutskever, I.; Tang, J.; Wolski, F.; and Zhang, S. 2019 · 1912
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Global path planning using artificial potential fields
Warren, C. W. 1989 · 1989
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Sliding mode control for gradient tracking and robot navigation using artificial potential fields
Guldner, J.; and Utkin, V. I. 1995 · 1995
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Social force model for pedestrian dynamics
Helbing, D.; and Molnár, P. 1995 · 1995
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A gradient method for realtime robot control
Konolige, K. 2000 · 2000
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Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning
Long, Q.; Zhou, Z.; Gupta, A.; Fang, F.; Wu, Y.; and Wang, X. 2020a · 2003
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Evolutionary population curriculum for scaling multi-agent reinforcement learning
Long, Q.; Zhou, Z.; Gupta, A.; Fang, F.; Wu, Y.; and Wang, X. 2020b · 2003
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Multi-robot dynamic role assignment and coordination through shared potential fields
Vail, D.; and Veloso, M. 2003 · 2003
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Score-based generative modeling through stochastic differential equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2020 · 2011
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Score-Based Generative Modeling through Stochastic Differential Equations
Song, Y.; Sohl-Dickstein, J.; Kingma, D. P.; Kumar, A.; Ermon, S.; and Poole, B. 2021 · 2011
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A connection between score matching and denoising autoencoders
Vincent, P. 2011 · 2011
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Research on evacuation in the subway station in China based on the Combined Social Force Model
Wan, J.; Sui, J.; and Yu, H. 2014 · 2014
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A multi-agent path planning algorithm based on hierarchical reinforcement learning and artificial potential field
Zheng, Y.; Li, B.; An, D.; and Li, N. 2015 · 2015
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Rusu, A. A.; Colmenarejo, S. G.; Gulcehre, C.; Desjardins, G.; Kirkpatrick, J.; Pascanu, R.; Mnih, V.; Kavukcuoglu, K.; and Hadsell, R. 2016 · 2016
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Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving
Shalev-Shwartz, S.; Shammah, S.; and Shashua, A. 2016 · 2016
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Omidshafiei, S.; Pazis, J.; Amato, C.; How, J. P.; and Vian, J. 2017 · 2017
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Unrealcv: Virtual worlds for computer vision
Qiu, W.; Zhong, F.; Zhang, Y.; Qiao, S.; Xiao, Z.; Kim, T. S.; and Wang, Y. 2017 · 2017
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Social force models for pedestrian traffic–state of the art
Chen, X.; Treiber, M.; Kanagaraj, V.; and Li, H. 2018 · 2018
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Learning policy representations in multiagent systems
Grover, A.; Al-Shedivat, M.; Gupta, J.; Burda, Y.; and Edwards, H. 2018 · 2018
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Social force model-based group behavior simulation in virtual geographic environments
Huang, L.; Gong, J.; Li, W.; Xu, T.; Shen, S.; Liang, J.; Feng, Q.; Zhang, D.; and Sun, J. 2018 · 2018
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Learning attentional communication for multi-agent cooperation
UPDeT: Universal Multi-agent Reinforcement Learning via Policy Decoupling with Transformers
Hu, S.; Zhu, F.; Chang, X.; and Liang, X. 2021 · 2021
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An integration of enhanced social force and crowd control models for high-density crowd simulation
Kolivand, H.; Rahim, M. S.; Sunar, M. S.; Fata, A. Z. A.; and Wren, C. 2021 · 2021
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The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
Yu, C.; Velu, A.; Vinitsky, E.; Gao, J.; Wang, Y.; Bayen, A.; and Wu, Y. 2021 · 2021
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Towards distraction-robust active visual tracking
Zhong, F.; Sun, P.; Luo, W.; Yan, T.; and Wang, Y. 2021 · 2021
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Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment
Zhou, T.; Zhang, F.; Shao, K.; Li, K.; Huang, W.; Luo, J.; Wang, W.; Yang, Y.; Mao, H.; Wang, B.; Li, D.; Liu, W.; and Hao, J. 2021 · 2021
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Jiang, J.; and Lu, Z. 2018 · 2018
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Emergence of grounded compositional language in multi-agent populations
Mordatch, I.; and Abbeel, P. 2018 · 2018
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Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns
Liu, Y.; Hu, Y.; Gao, Y.; Chen, Y.; and Fan, C. 2019 · 2019
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Distributed path planning of a multi-robot system based on the neighborhood artificial potential field approach
Matoui, F.; Boussaid, B.; and Abdelkrim, M. N. 2019 · 2019
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Learning to teach in cooperative multiagent reinforcement learning
Omidshafiei, S.; Kim, D.-K.; Liu, M.; Tesauro, G.; Riemer, M.; Amato, C.; Campbell, M.; and How, J. P. 2019 · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; et al. 2019 · 2019
Cited alongside, same era.
Learning multi-agent coordination for enhancing target coverage in directional sensor networks
Xu, J.; Zhong, F.; and Wang, Y. 2020 · 2020
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GFPose: Learning 3D Human Pose Prior with Gradient Fields
Ci, H.; Wu, M.; Zhu, W.; Ma, X.; Dong, H.; Zhong, F.; and Wang, Y. 2022 · 2022
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Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search
Klančar, G.; Zdešar, A.; and Krishnan, M. 2022 · 2022
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Vinitsky, E.; Köster, R.; Agapiou, J. P.; Duéñez-Guzmán, E.; Vezhnevets, A. S.; and Leibo, J. Z. 2022 · 2022
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ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind
Wang, Y.; Zhong, F.; Xu, J.; and Wang, Y. 2022 · 2022
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Multi-agent reinforcement learning is a sequence modeling problem
Wen, M.; Kuba, J.; Lin, R.; Zhang, W.; Wen, Y.; Wang, J.; and Yang, Y. 2022 · 2022
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TarGF: Learning Target Gradient Field for Object Rearrangement
Wu, M.; Zhong, F.; Xia, Y.; and Dong, H. 2022 · 2022
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The emergence of division of labor through decentralized social sanctioning
Yaman, A.; Leibo, J. Z.; Iacca, G.; and Lee, S. W. 2022 · 2022
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Hybrid gradient vector fields for path-following guidance
Zhao, Y.-y.; Yang, Z.; Kong, W.-r.; Piao, H.-y.; Huang, J.-c.; Lv, X.-f.; and Zhou, D.-y. 2022 · 2022
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Learning Gradient Fields for Scalable and Generalizable Irregular Packing
Xue, T.; Wu, M.; Lu, L.; Wang, H.; Dong, H.; and Chen, B. 2023 · 2023
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Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
Wu, T.; Wu, M.; Zhang, J.; Gan, Y.; and Dong, H. 2024 · 2024
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