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Despite the success in specific scenarios, existing foundation agents still struggle to generalize across various virtual scenarios, mainly due to the dramatically different encapsulations of environments with manually designed observation and action spaces.
Template matching techniques in computer vision: theory and practice
Roberto Brunelli · 2009
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The Arcade learning environment: An evaluation platform for general agents
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The Malmo platform for artificial intelligence experimentation
Matthew Johnson, Katja Hofmann, Tim Hutton, and David Bignell · 2016
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Universe, 2016
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Super Mario Bros for OpenAI Gym
Christian Kauten · 2018
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Reinforcement learning on web interfaces using workflow-guided exploration
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Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler, and Antonio Torralba · 2018
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Dota 2 with large scale deep reinforcement learning
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Xiaofeng Gao, Ran Gong, Tianmin Shu, Xu Xie, Shu Wang, and Song-Chun Zhu · 2019
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Minerl: A large-scale dataset of Minecraft demonstrations
William H Guss, Brandon Houghton, Nicholay Topin, Phillip Wang, Cayden Codel, Manuela Veloso, and Ruslan Salakhutdinov · 2019
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Human-level performance in 3D multiplayer games with population-based reinforcement learning
Max Jaderberg, Wojciech M Czarnecki, Iain Dunning, Luke Marris, Guy Lever, Antonio Garcia Castaneda, Charles Beattie, Neil C Rabinowitz, Ari S Morcos, Avraham Ruderman, et al · 2019
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Mikayel Samvelyan, Tabish Rashid, Christian Schroeder De Witt, Gregory Farquhar, Nantas Nardelli, Tim GJ Rudner, Chia-Man Hung, Philip HS Torr, Jakob Foerster, and Shimon Whiteson · 2019
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Scalable evaluation of multi-agent reinforcement learning with melting pot
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igibson 2.0: Object-centric simulation for robot learning of everyday household tasks
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igibson 1.0: a simulation environment for interactive tasks in large realistic scenes
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Habitat 2.0: Training home assistants to rearrange their habitat
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao, John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier, Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik, Manolis Savva, and Dhruv Batra · 2021
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Too many cooks: Coordinating multi-agent collaboration through inverse planning
Sarah A. Wu, Rose E. Wang, James A. Evans, Joshua B. Tenenbaum, David C. Parkes, and Max Kleiman-Weiner · 2021
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Video pretraining (VPT): Learning to act by watching unlabeled online videos
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Human-level play in the game of diplomacy by combining language models with strategic reasoning
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Procthor: Large-scale embodied ai using procedural generation
Matt Deitke, Eli VanderBilt, Alvaro Herrasti, Luca Weihs, Kiana Ehsani, Jordi Salvador, Winson Han, Eric Kolve, Aniruddha Kembhavi, and Roozbeh Mottaghi · 2022
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Minedojo: Building open-ended embodied agents with internet-scale knowledge
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang, Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, and Anima Anandkumar · 2022
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New and improved embedding model, 2022
OpenAI · 2022
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Outracing champion Gran Turismo drivers with deep reinforcement learning
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Weihao Tan, Wentao Zhang, Shanqi Liu, Longtao Zheng, Xinrun Wang, and Bo An · 2023
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Palm-e: An embodied multimodal language model
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Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v, 2023
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AppAgent: Multimodal agents as smartphone users
Zhao Yang, Jiaxuan Liu, Yucheng Han, Xin Chen, Zebiao Huang, Bin Fu, and Gang Yu · 2023
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ReAct: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao · 2023
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WebArena: A realistic web environment for building autonomous agents
Shuyan Zhou, Frank F. Xu, Hao Zhu, Xuhui Zhou, Robert Lo, Abishek Sridhar, Xianyi Cheng, Yonatan Bisk, Daniel Fried, Uri Alon, et al · 2023
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The claude 3 model family: Opus, sonnet, haiku, 2024
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