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This paper investigates the multi-agent navigation problem, which requires multiple agents to reach the target goals in a limited time.
Improving the Hungarian assignment algorithm
Roy Jonker and Ton Volgenant. 1986 · 1986
Earlier work this paper cites.
Feudal reinforcement learning
Peter Dayan and Geoffrey E Hinton. 1992 · 1992
Earlier work this paper cites.
Autonomous navigation and exploration in a rescue environment. In IEEE International Safety, Security and Rescue Rototics, Workshop, 2005. IEEE, 54–59
Daniele Calisi, Alessandro Farinelli, Luca Iocchi, and Daniele Nardi. 2005 · 2005
Earlier work this paper cites.
RFID technology-based exploration and SLAM for search and rescue. In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 4054–4059
Alexander Kleiner, Johann Prediger, and Bernhard Nebel. 2006 · 2006
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008 · 2008
Earlier work this paper cites.
Application of beidou navigation satellite system in logistics and transportation
Shaotang Liu and Lixin Hu. 2009 · 2009
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole. 2016 · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum. 2016 · 2016
Earlier work this paper cites.
Simultaneous localization and mapping: A survey of current trends in autonomous driving
Guillaume Bresson, Zayed Alsayed, Li Yu, and Sébastien Glaser. 2017 · 2017
Earlier work this paper cites.
Learning multi-level hierarchies with hindsight
Andrew Levy, George Konidaris, Robert Platt, and Kate Saenko. 2017 · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. 2017a · 2017
Earlier work this paper cites.
Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch. 2017b · 2017
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.
Target-driven visual navigation in indoor scenes using deep reinforcement learning. In 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 3357–3364
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi. 2017 · 2017
Earlier work this paper cites.
Multi-robot multi-target dynamic path planning using artificial bee colony and evolutionary programming in unknown environment
Abdul Qadir Faridi, Sanjeev Sharma, Anupam Shukla, Ritu Tiwari, and Joydip Dhar. 2018 · 2018
Cited alongside, same era.
Multi-mobile robot autonomous navigation system for intelligent logistics. In 2018 Chinese Automation Congress (CAC) . IEEE, 2603–2609
Kaivuan Gao, Jing Xin, Han Cheng, Ding Liu, and Jiang Li. 2018 · 2018
Cited alongside, same era.
Graph convolutional reinforcement learning
Jiechuan Jiang, Chen Dun, Tiejun Huang, and Zongqing Lu. 2018 · 2018
Cited alongside, same era.
Data-efficient hierarchical reinforcement learning
Ofir Nachum, Shixiang Shane Gu, Honglak Lee, and Sergey Levine. 2018 · 2018
Cited alongside, same era.
Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning. In International conference on machine learning . PMLR, 4295–4304
Goal-conditioned reinforcement learning with imagined subgoals. In International Conference on Machine Learning . PMLR, 1430–1440
Elliot Chane-Sane, Cordelia Schmid, and Ivan Laptev. 2021 · 2021
Later among the works it cites.
Latent Goal Allocation for Multi-Agent Goal-Conditioned Self-Supervised Imitation Learning. In Advances in Neural Information Processing Systems
Rui Chen, Peide Huang, and Laixi Shi. 2021 · 2021
Later among the works it cites.
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning
Christopher Hoang, Sungryull Sohn, Jongwook Choi, Wilka Carvalho, and Honglak Lee. 2021 · 2021
Later among the works it cites.
Landmark-guided subgoal generation in hierarchical reinforcement learning
Junsu Kim, Younggyo Seo, and Jinwoo Shin. 2021 · 2021
Later among the works it cites.
Multi-Agent Graph-Attention Communication and Teaming.. In AAMAS . 964–973
Yaru Niu, Rohan R Paleja, and Matthew C Gombolay. 2021 · 2021
Later among the works it cites.
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Tabish Rashid, Mikayel Samvelyan, Christian Schroeder, Gregory Farquhar, Jakob Foerster, and Shimon Whiteson. 2018 · 2018
Cited alongside, same era.
Efficient multi-agent cooperative navigation in unknown environments with interlaced deep reinforcement learning. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2897–2901
Yue Jin, Yaodong Zhang, Jian Yuan, and Xudong Zhang. 2019 · 2019
Cited alongside, same era.
Inter-level cooperation in hierarchical reinforcement learning
Abdul Rahman Kreidieh, Glen Berseth, Brandon Trabucco, Samyak Parajuli, Sergey Levine, and Alexandre M Bayen. 2019 · 2019
Cited alongside, same era.
Hierarchical foresight: Self-supervised learning of long-horizon tasks via visual subgoal generation
Suraj Nair and Chelsea Finn. 2019 · 2019
Cited alongside, same era.
Qtran: Learning to factorize with transformation for cooperative multi-agent reinforcement learning. In International conference on machine learning . PMLR, 5887–5896
Kyunghwan Son, Daewoo Kim, Wan Ju Kang, David Earl Hostallero, and Yung Yi. 2019 · 2019
Cited alongside, same era.
A survey of deep learning techniques for autonomous driving
Sorin Grigorescu, Bogdan Trasnea, Tiberiu Cocias, and Gigel Macesanu. 2020 · 2020
Cited alongside, same era.
Real-time deep reinforcement learning based vehicle navigation
Songsang Koh, Bo Zhou, Hui Fang, Po Yang, Zaili Yang, Qiang Yang, Lin Guan, and Zhigang Ji. 2020 · 2020
Cited alongside, same era.
Deep implicit coordination graphs for multi-agent reinforcement learning
Sheng Li, Jayesh K Gupta, Peter Morales, Ross Allen, and Mykel J Kochenderfer. 2020 · 2020
Cited alongside, same era.
Learning to fly—a gym environment with pybullet physics for reinforcement learning of multi-agent quadcopter control. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 7512–7519
Jacopo Panerati, Hehui Zheng, SiQi Zhou, James Xu, Amanda Prorok, and Angela P Schoellig. 2021 · 2021
Later among the works it cites.
Visual navigation with multiple goals based on deep reinforcement learning
Zhenhuan Rao, Yuechen Wu, Zifei Yang, Wei Zhang, Shijian Lu, Weizhi Lu, and ZhengJun Zha. 2021 · 2021
Later among the works it cites.
Inference-based Hierarchical Reinforcement Learning for Cooperative Multi-agent Navigation. In 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI) . IEEE, 57–64
Lijun Xia, Chao Yu, and Zifan Wu. 2021 · 2021
Later among the works it cites.
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games
Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, and Yi Wu. 2021 · 2021
Later among the works it cites.
MASER: Multi-Agent Reinforcement Learning with Subgoals Generated from Experience Replay Buffer. In International Conference on Machine Learning . PMLR, 10041–10052
Jeewon Jeon, Woojun Kim, Whiyoung Jung, and Youngchul Sung. 2022 · 2022
Later among the works it cites.
Enhancing deep reinforcement learning approaches for multi-robot navigation via single-robot evolutionary policy search. In 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 5525–5531
Enrico Marchesini and Alessandro Farinelli. 2022 · 2022
Later among the works it cites.
GCS: Graph-Based Coordination Strategy for Multi-Agent Reinforcement Learning
Jingqing Ruan, Yali Du, Xuantang Xiong, Dengpeng Xing, Xiyun Li, Linghui Meng, Haifeng Zhang, Jun Wang, and Bo Xu. 2022 · 2022
Later among the works it cites.
Multi-Agent Reinforcement Learning is a Sequence Modeling Problem
Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, and Yaodong Yang. 2022 · 2022
Later among the works it cites.
SAVE: Spatial-Attention Visual Exploration. In 2022 IEEE International Conference on Image Processing (ICIP) . IEEE, 1356–1360
Xinyi Yang, Chao Yu, Jiaxuan Gao, Yu Wang, and Huazhong Yang. 2022 · 2022
Later among the works it cites.
Learning efficient multi-agent cooperative visual exploration. In European Conference on Computer Vision . Springer, 497–515
Chao Yu, Xinyi Yang, Jiaxuan Gao, Huazhong Yang, Yu Wang, and Yi Wu. 2022 · 2022
Later among the works it cites.