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A key objective of embodied intelligence is enabling agents to perform long-horizon tasks in dynamic environments while maintaining robust decision-making and adaptability.
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The human hippocampus and spatial and episodic memory
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Q-decomposition for reinforcement learning agents
Stuart J Russell and Andrew Zimdars · 2003
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Embodied artificial intelligence: Trends and challenges
Rolf Pfeifer and Fumiya Iida · 2004
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Malik Ghallab, Dana Nau, and Paolo Traverso · 2004
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Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Neural map: Structured memory for deep reinforcement learning
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Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards
Mel Vecerik, Todd Hester, Jonathan Scholz, Fumin Wang, Olivier Pietquin, Bilal Piot, Nicolas Heess, Thomas Rothörl, Thomas Lampe, and Martin Riedmiller · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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A survey of robotic motion planning in dynamic environments
MG Mohanan and Ambuja Salgoankar · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Distributed prioritized experience replay
Dan Horgan, John Quan, David Budden, Gabriel Barth-Maron, Matteo Hessel, Hado Van Hasselt, and David Silver · 2018
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Scene memory transformer for embodied agents in long-horizon tasks
Kuan Fang, Alexander Toshev, Li Fei-Fei, and Silvio Savarese · 2019
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Textworld: A learning environment for text-based games
Marc-Alexandre Côté, Akos Kádár, Xingdi Yuan, Ben Kybartas, Tavian Barnes, Emery Fine, James Moore, Matthew Hausknecht, Layla El Asri, Mahmoud Adada, et al · 2019
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Model-based reinforcement learning for atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, et al · 2019
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Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, and Shimon Whiteson · 2023
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Chateval: Towards better llm-based evaluators through multi-agent debate
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Graph neural networks: A review of methods and applications
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Expel: Llm agents are experiential learners
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Swiftsage: A generative agent with fast and slow thinking for complex interactive tasks
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