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In partially observable multi-agent systems, agents typically only have access to local observations.
A Behavioral Model of Rational Choice
Simon, H. A. 1955 · 1955
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A survey of POMDP applications
Cassandra, A. R. 1998 · 1998
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Image inpainting
Bertalmio, M.; Sapiro, G.; Caselles, V.; and Ballester, C. 2000 · 2000
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R-MADDPG for Partially Observable Environments and Limited Communication
Wang, R. E.; Everett, M.; and How, J. P. 2020 · 2002
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Visualizing data using t-SNE
Van der Maaten, L.; and Hinton, G. 2008 · 2008
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The free-energy principle: a unified brain theory?
Friston, K. J. 2010 · 2010
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Partially Observable Markov Decision Processes
Spaan, M. T. J. 2012 · 2012
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Auto-Encoding Variational Bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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Deep Recurrent Q-Learning for Partially Observable MDPs
Hausknecht, M. J.; and Stone, P. 2015 · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Learning to Communicate with Deep Multi-Agent Reinforcement Learning
Foerster, J. N.; Assael, Y. M.; de Freitas, N.; and Whiteson, S. 2016 · 2016
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Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Lowe, R.; Wu, Y.; Tamar, A.; Harb, J.; Abbeel, P.; and Mordatch, I. 2017 · 2017
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Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games
Peng, P.; Yuan, Q.; Wen, Y.; Yang, Y.; Tang, Z.; Long, H.; and Wang, J. 2017 · 2017
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Attention is All you Need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L.; and Polosukhin, I. 2017 · 2017
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Deep Variational Reinforcement Learning for POMDPs
Igl, M.; Zintgraf, L. M.; Le, T. A.; Wood, F.; and Whiteson, S. 2018 · 2018
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Learning Attentional Communication for Multi-Agent Cooperation
Jiang, J.; and Lu, Z. 2018 · 2018
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QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Rashid, T.; Samvelyan, M.; de Witt, C. S.; Farquhar, G.; Foerster, J. N.; and Whiteson, S. 2018 · 2018
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TarMAC: Targeted Multi-Agent Communication
Das, A.; Gervet, T.; Romoff, J.; Batra, D.; Parikh, D.; Rabbat, M.; and Pineau, J. 2019 · 2019
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Efficient Ridesharing Order Dispatching with Mean Field Multi-Agent Reinforcement Learning
Li, M.; Qin, Z. T.; Jiao, Y.; Yang, Y.; Wang, J.; Wang, C.; Wu, G.; and Ye, J. 2019 · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Köpf, A.; Yang, E. Z.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
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The StarCraft Multi-Agent Challenge
Samvelyan, M.; Rashid, T.; de Witt, C. S.; Farquhar, G.; Nardelli, N.; Rudner, T. G. J.; Hung, C.; Torr, P. H. S.; Foerster, J. N.; and Whiteson, S. 2019 · 2019
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A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y.; Si, X.; Hu, C.; and Zhang, J. 2019 · 2019
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A Simple Framework for Contrastive Learning of Visual Representations
Chen, T.; Kornblith, S.; Norouzi, M.; and Hinton, G. E. 2020 · 2020
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Efficient Multi-agent Communication via Self-supervised Information Aggregation
Guan, C.; Chen, F.; Yuan, L.; Wang, C.; Yin, H.; Zhang, Z.; and Yu, Y. 2022 · 2022
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Classifier-Free Diffusion Guidance
Ho, J.; and Salimans, T. 2022 · 2022
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Planning with Diffusion for Flexible Behavior Synthesis
Janner, M.; Du, Y.; Tenenbaum, J. B.; and Levine, S. 2022 · 2022
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SIDE: State Inference for Partially Observable Cooperative Multi-Agent Reinforcement Learning
Xu, Z.; Bai, Y.; Li, D.; Zhang, B.; and Fan, G. 2022a · 2022
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Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning
Xu, Z.; Li, D.; Zhang, B.; Zhan, Y.; Bai, Y.; and Fan, G. 2022b · 2022
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Variational Recurrent Models for Solving Partially Observable Control Tasks
Han, D.; Doya, K.; and Tani, J. 2020 · 2020
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Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
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SVQN: Sequential Variational Soft Q-Learning Networks
Huang, S.; Su, H.; Zhu, J.; and Chen, T. 2020 · 2020
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Graph Convolutional Reinforcement Learning
Jiang, J.; Dun, C.; Huang, T.; and Lu, Z. 2020 · 2020
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Particle Filter Recurrent Neural Networks
Ma, X.; Karkus, P.; Hsu, D.; and Lee, W. S. 2020a · 2020
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Discriminative Particle Filter Reinforcement Learning for Complex Partial observations
Ma, X.; Karkus, P.; Hsu, D.; Lee, W. S.; and Ye, N. 2020b · 2020
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Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Rashid, T.; Farquhar, G.; Peng, B.; and Whiteson, S. 2020 · 2020
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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. M.; and Wu, Y. 2022 · 2022
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EDGI: Equivariant Diffusion for Planning with Embodied Agents
Brehmer, J.; Bose, J.; de Haan, P.; and Cohen, T. S. 2023 · 2023
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GenAug: Retargeting behaviors to unseen situations via Generative Augmentation
Chen, Z. Q.; Kiami, S. C.; Gupta, A.; and Kumar, V. 2023 · 2023
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Diffusion Policy: Visuomotor Policy Learning via Action Diffusion
Chi, C.; Feng, S.; Du, Y.; Xu, Z.; Cousineau, E.; Burchfiel, B.; and Song, S. 2023 · 2023
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Generative Adversarial Networks
Goodfellow, I. J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A. C.; and Bengio, Y. 2022 · 2023
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IDQL: Implicit Q-Learning as an Actor-Critic Method with Diffusion Policies
Hansen-Estruch, P.; Kostrikov, I.; Janner, M.; Kuba, J. G.; and Levine, S. 2023 · 2023
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AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners
Liang, Z.; Mu, Y.; Ding, M.; Ni, F.; Tomizuka, M.; and Luo, P. 2023 · 2023
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Synthetic Experience Replay
Lu, C.; Ball, P. J.; Teh, Y. W.; and Parker-Holder, J. 2023 · 2023
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Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning
Pásztor, B.; Krause, A.; and Bogunovic, I. 2023 · 2023
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Consistency Models
Song, Y.; Dhariwal, P.; Chen, M.; and Sutskever, I. 2023 · 2023
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Learning to Routing in UAV Swarm Network: A Multi-Agent Reinforcement Learning Approach
Wang, Z.; Yao, H.; Mai, T.; Xiong, Z.; Wu, X.; Wu, D.; and Guo, S. 2023 · 2023
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Counterfactual-attention multi-agent reinforcement learning for joint condition-based maintenance and production scheduling
Zhang, N.; Shen, Y.; Du, Y.; Chen, L.; and Zhang, X. 2023 · 2023
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Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning
Ada, S. E.; Öztop, E.; and Ugur, E. 2024 · 2024
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From Explicit Communication to Tacit Cooperation: A Novel Paradigm for Cooperative MARL
Li, D.; Xu, Z.; Zhang, B.; Zhou, G.; Zhang, Z.; and Fan, G. 2024 · 2024
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