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In this work, we address challenging multi-agent cooperation problems with decentralized control, raw sensory observations, costly communication, and multi-objective tasks instantiated in various embodied environments.
Home: A household multimodal environment
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Ai2-thor: An interactive 3d environment for visual ai
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Multi-agent actor-critic for mixed cooperative-competitive environments
R. Lowe, A. Tamar, J. Harb, O. Pieter Abbeel, and I. Mordatch · 2017
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Visual semantic planning using deep successor representations
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Learning attentional communication for multi-agent cooperation
J. Jiang and Z. Lu · 2018
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Mapping instructions to actions in 3d environments with visual goal prediction
D. Misra, A. Bennett, V. Blukis, E. Niklasson, M. Shatkhin, and Y. Artzi · 2018
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Virtualhome: Simulating household activities via programs
X. Puig, K. Ra, M. Boben, J. Li, T. Wang, S. Fidler, and A. Torralba · 2018
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Pommerman: A multi-agent playground
C. Resnick, W. Eldridge, D. Ha, D. Britz, J. Foerster, J. Togelius, K. Cho, and J. Bruna · 2018
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M 3 rl: Mind-aware multi-agent management reinforcement learning
T. Shu and Y. Tian · 2018
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Gibson env: Real-world perception for embodied agents
F. Xia, A. R. Zamir, Z. He, A. Sax, J. Malik, and S. Savarese · 2018
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Emergent tool use from multi-agent autocurricula
B. Baker, I. Kanitscheider, T. Markov, Y. Wu, G. Powell, B. McGrew, and I. Mordatch · 2019
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On the utility of learning about humans for human-ai coordination
M. Carroll, R. Shah, M. K. Ho, T. Griffiths, S. Seshia, P. Abbeel, and A. Dragan · 2019
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Tarmac: Targeted multi-agent communication
A. Das, T. Gervet, J. Romoff, D. Batra, D. Parikh, M. Rabbat, and J. Pineau · 2019
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
M. Jaderberg, W. M. Czarnecki, I. Dunning, L. Marris, G. Lever, A. G. Castaneda, C. Beattie, N. C. Rabinowitz, A. S. Morcos, A. Ruderman, et al · 2019
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Two body problem: Collaborative visual task completion
U. Jain, L. Weihs, E. Kolve, M. Rastegari, S. Lazebnik, A. Farhadi, A. G. Schwing, and A. Kembhavi · 2019
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Social influence as intrinsic motivation for multi-agent deep reinforcement learning
N. Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. Ortega, D. Strouse, J. Z. Leibo, and N. De Freitas · 2019
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The starcraft multi-agent challenge
M. Samvelyan, T. Rashid, C. Schroeder de Witt, G. Farquhar, N. Nardelli, T. G. Rudner, C.-M. Hung, P. H. Torr, J. Foerster, and S. Whiteson · 2019
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Habitat: A platform for embodied ai research
M. Savva, A. Kadian, O. Maksymets, Y. Zhao, E. Wijmans, B. Jain, J. Straub, J. Liu, V. Koltun, J. Malik, et al · 2019
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Neural mmo: A massively multiagent game environment for training and evaluating intelligent agents
J. Suarez, Y. Du, P. Isola, and I. Mordatch · 2019
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The hanabi challenge: A new frontier for ai research
N. Bard, J. N. Foerster, S. Chandar, N. Burch, M. Lanctot, H. F. Song, E. Parisotto, V. Dumoulin, S. Moitra, E. Hughes, et al · 2020
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Rearrangement: A challenge for embodied ai
D. Batra, A. X. Chang, S. Chernova, A. J. Davison, J. Deng, V. Koltun, S. Levine, J. Malik, I. Mordatch, R. Mottaghi, et al · 2020
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Threedworld: A platform for interactive multi-modal physical simulation
C. Gan, J. Schwartz, S. Alter, D. Mrowca, M. Schrimpf, J. Traer, J. De Freitas, J. Kubilius, A. Bhandwaldar, N. Haber, et al · 2020
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A cordial sync: Going beyond marginal policies for multi-agent embodied tasks
Pre-trained language models for interactive decision-making
S. Li, X. Puig, C. Paxton, Y. Du, C. Wang, L. Fan, T. Chen, D.-A. Huang, E. Akyürek, A. Anandkumar, et al · 2022
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Code as policies: Language model programs for embodied control
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng · 2022
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Unified-io: A unified model for vision, language, and multi-modal tasks
J. Lu, C. Clark, R. Zellers, R. Mottaghi, and A. Kembhavi · 2022
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Teach: Task-driven embodied agents that chat
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Plansformer: Generating symbolic plans using transformers
V. Pallagani, B. Muppasani, K. Murugesan, F. Rossi, L. Horesh, B. Srivastava, F. Fabiano, and A. Loreggia · 2022
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U. Jain, L. Weihs, E. Kolve, A. Farhadi, S. Lazebnik, A. Kembhavi, and A. Schwing · 2020
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks
M. Shridhar, J. Thomason, D. Gordon, Y. Bisk, W. Han, R. Mottaghi, L. Zettlemoyer, and D. Fox · 2020
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Sapien: A simulated part-based interactive environment
F. Xiang, Y. Qin, K. Mo, Y. Xia, H. Zhu, F. Liu, M. Liu, H. Jiang, Y. Yuan, H. Wang, et al · 2020
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Interpretation of emergent communication in heterogeneous collaborative embodied agents
S. Patel, S. Wani, U. Jain, A. G. Schwing, S. Lazebnik, M. Savva, and A. X. Chang · 2021
Cited alongside, same era.
Watch-and-help: A challenge for social perception and human-ai collaboration
X. Puig, T. Shu, S. Li, Z. Wang, Y.-H. Liao, J. B. Tenenbaum, S. Fidler, and A. Torralba · 2021
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Skill induction and planning with latent language
P. Sharma, A. Torralba, and J. Andreas · 2021
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Tom2c: Target-oriented multi-agent communication and cooperation with theory of mind
Y. Wang, J. Xu, Y. Wang, et al · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
M. Ahn, A. Brohan, N. Brown, Y. Chebotar, O. Cortes, B. David, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, et al · 2022
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Planning with large language models via corrective re-prompting
S. S. Raman, V. Cohen, E. Rosen, I. Idrees, D. Paulius, and S. Tellex · 2022
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Llm-planner: Few-shot grounded planning for embodied agents with large language models
C. H. Song, J. Wu, C. Washington, B. M. Sadler, W.-L. Chao, and Y. Su · 2022
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Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou · 2022
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Sparks of artificial general intelligence: Early experiments with gpt-4, 2023
S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y. T. Lee, Y. Li, S. Lundberg, H. Nori, H. Palangi, M. T. Ribeiro, and Y. Zhang · 2023
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Palm-e: An embodied multimodal language model
D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, W. Huang, Y. Chebotar, P. Sermanet, D. Duckworth, S. Levine, V. Vanhoucke, K. Hausman, M. Toussaint, K. Greff, A. Zeng, I. Mordatch, and P. Florence · 2023
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Language is not all you need: Aligning perception with language models
S. Huang, L. Dong, W. Wang, Y. Hao, S. Singhal, S. Ma, T. Lv, L. Cui, O. K. Mohammed, Q. Liu, et al · 2023
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Behavior-1k: A benchmark for embodied ai with 1,000 everyday activities and realistic simulation
C. Li, R. Zhang, J. Wong, C. Gokmen, S. Srivastava, R. Martín-Martín, C. Wang, G. Levine, M. Lingelbach, J. Sun, et al · 2023
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Generative agents: Interactive simulacra of human behavior
J. S. Park, J. C. O’Brien, C. J. Cai, M. R. Morris, P. Liang, and M. S. Bernstein · 2023
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X. Puig, T. Shu, J. B. Tenenbaum, and A. Torralba · 2023
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Describe, explain, plan and select: Interactive planning with large language models enables open-world multi-task agents, 2023
Z. Wang, S. Cai, A. Liu, X. Ma, and Y. Liang · 2023
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Plan, eliminate, and track–language models are good teachers for embodied agents
Y. Wu, S. Y. Min, Y. Bisk, R. Salakhutdinov, A. Azaria, Y. Li, T. Mitchell, and S. Prabhumoye · 2023
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Foundation models for decision making: Problems, methods, and opportunities
S. Yang, O. Nachum, Y. Du, J. Wei, P. Abbeel, and D. Schuurmans · 2023
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Distilling script knowledge from large language models for constrained language planning
S. Yuan, J. Chen, Z. Fu, X. Ge, S. Shah, C. R. Jankowski, D. Yang, and Y. Xiao · 2023
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