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Current Reinforcement Learning (RL) methods often suffer from sample-inefficiency, resulting from blind exploration strategies that neglect causal relationships among states, actions, and rewards.
Efficient reinforcement learning in factored mdps
Michael Kearns and Daphne Koller · 1999
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Multiagent planning with factored mdps
Carlos Guestrin, Daphne Koller, and Ronald Parr · 2001
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Causation, prediction, and search
Peter Spirtes, Clark Glymour, and Richard Scheines · 2001
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Optimal structure identification with greedy search
David Maxwell Chickering · 2002
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Efficient solution algorithms for factored mdps
Carlos Guestrin, Daphne Koller, Ronald Parr, and Shobha Venkataraman · 2003
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Empowerment: A universal agent-centric measure of control
Alexander S Klyubin, Daniel Polani, and Chrystopher L Nehaniv · 2005
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Defining object types and options using mdp homomorphisms
Alicia Peregrin Wolfe and Andrew G Barto · 2006
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An object-oriented representation for efficient reinforcement learning
Carlos Diuk, Andre Cohen, and Michael L Littman · 2008
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Causality
Judea Pearl · 2009
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Empowerment for continuous agent—environment systems
Tobias Jung, Daniel Polani, and Peter Stone · 2011
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Directlingam: A direct method for learning a linear non-gaussian structural equation model
Shohei Shimizu, Takanori Inazumi, Yasuhiro Sogawa, Aapo Hyvarinen, Yoshinobu Kawahara, Takashi Washio, Patrik O Hoyer, Kenneth Bollen, and Patrik Hoyer · 2011
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Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
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Empowerment–an introduction
Christoph Salge, Cornelius Glackin, and Daniel Polani · 2014
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A physics-based model prior for object-oriented mdps
Jonathan Scholz, Martin Levihn, Charles Isbell, and David Wingate · 2014
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Variational information maximisation for intrinsically motivated reinforcement learning
Shakir Mohamed and Danilo Jimenez Rezende · 2015
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Attend, infer, repeat: Fast scene understanding with generative models
SM Eslami, Nicolas Heess, Theophane Weber, Yuval Tassa, David Szepesvari, Geoffrey E Hinton, et al · 2016
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Diversity is all you need: Learning skills without a reward function
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
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Sequential attend, infer, repeat: Generative modelling of moving objects
Adam Kosiorek, Hyunjik Kim, Yee Whye Teh, and Ingmar Posner · 2018
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Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, and Sergey Levine · 2018
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Reinforcement learning: An introduction
Richard S Sutton · 2018
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Yuval Tassa, Yotam Doron, Alistair Muldal, Tom Erez, Yazhe Li, Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al · 2018
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Scalor: Generative world models with scalable object representations
Jindong Jiang, Sepehr Janghorbani, Gerard De Melo, and Sungjin Ahn · 2019
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Structured object-aware physics prediction for video modeling and planning
Jannik Kossen, Karl Stelzner, Marcel Hussing, Claas Voelcker, and Kristian Kersting · 2019
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A unified bellman optimality principle combining reward maximization and empowerment
Felix Leibfried, Sergio Pascual-Diaz, and Jordi Grau-Moya · 2019
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Habitat: A platform for embodied ai research
Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, et al · 2019
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Multi-object search using object-oriented pomdps
Arthur Wandzel, Yoonseon Oh, Michael Fishman, Nishanth Kumar, Lawson LS Wong, and Stefanie Tellex · 2019
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Nicholas Watters, Loic Matthey, Matko Bosnjak, Christopher P Burgess, and Alexander Lerchner · 2019
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Causal discovery from heterogeneous/nonstationary data
Biwei Huang, Kun Zhang, Jiji Zhang, Joseph Ramsey, Ruben Sanchez-Romero, Clark Glymour, and Bernhard Schölkopf · 2020
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Towards practical multi-object manipulation using relational reinforcement learning
Richard Li, Allan Jabri, Trevor Darrell, and Pulkit Agrawal · 2020
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Improving generative imagination in object-centric world models
Zhixuan Lin, Yi-Fu Wu, Skand Peri, Bofeng Fu, Jindong Jiang, and Sungjin Ahn · 2020
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Enhancing ood generalization in offline reinforcement learning with energy-based policy optimization
Hongye Cao, Shangdong Yang, Jing Huo, Xingguo Chen, and Yang Gao · 2023
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Causal reinforcement learning: A survey
ZH Deng, J Jiang, G Long, and C Zhang · 2023
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Learning dynamic attribute-factored world models for efficient multi-object reinforcement learning
Fan Feng and Sara Magliacane · 2023
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Foundation models in robotics: Applications, challenges, and the future
Roya Firoozi, Johnathan Tucker, Stephen Tian, Anirudha Majumdar, Jiankai Sun, Weiyu Liu, Yuke Zhu, Shuran Song, Ashish Kapoor, Karol Hausman, et al · 2023
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Entity-centric reinforcement learning for object manipulation from pixels
Dan Haramati, Tal Daniel, and Aviv Tamar · 2023
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Object-centric learning with slot attention
Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf · 2020
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Counterfactual data augmentation using locally factored dynamics
Silviu Pitis, Elliot Creager, and Animesh Garg · 2020
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Plannable approximations to mdp homomorphisms: Equivariance under actions
Elise Van der Pol, Thomas Kipf, Frans A Oliehoek, and Max Welling · 2020
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Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Chelsea Finn, and Sergey Levine · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Rishabh Agarwal, Max Schwarzer, Pablo Samuel Castro, Aaron C Courville, and Marc Bellemare · 2021
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Variational empowerment as representation learning for goal-based reinforcement learning
Jongwook Choi, Archit Sharma, Honglak Lee, Sergey Levine, and Shixiang Shane Gu · 2021
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A review of robot learning for manipulation: Challenges, representations, and algorithms
Oliver Kroemer, Scott Niekum, and George Konidaris · 2021
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Jindong Jiang, Fei Deng, Gautam Singh, and Sungjin Ahn · 2023
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Provably efficient causal model-based reinforcement learning for systematic generalization
Mirco Mutti, Riccardo De Santi, Emanuele Rossi, Juan Felipe Calderon, Michael Bronstein, and Marcello Restelli · 2023
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Slotdiffusion: Object-centric generative modeling with diffusion models
Ziyi Wu, Jingyu Hu, Wuyue Lu, Igor Gilitschenski, and Animesh Garg · 2023
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Object-centric learning for real-world videos by predicting temporal feature similarities
Andrii Zadaianchuk, Maximilian Seitzer, and Georg Martius · 2023
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A survey on causal reinforcement learning
Yan Zeng, Ruichu Cai, Fuchun Sun, Libo Huang, and Zhifeng Hao · 2023
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Towards empowerment gain through causal structure learning in model-based rl
Hongye Cao, Fan Feng, Meng Fang, Shaokang Dong, Jing Huo, and Yang Gao · 2024
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Unsupervised object interaction learning with counterfactual dynamics models
Jongwook Choi, Sungtae Lee, Xinyu Wang, Sungryull Sohn, and Honglak Lee · 2024
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Jindong Jiang, Fei Deng, Gautam Singh, Minseung Lee, and Sungjin Ahn · 2024
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Identifiable object-centric representation learning via probabilistic slot attention
Avinash Kori, Francesco Locatello, Ainkaran Santhirasekaram, Francesca Toni, Ben Glocker, and Fabio De Sousa Ribeiro · 2024
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Additive decoders for latent variables identification and cartesian-product extrapolation
Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, and Simon Lacoste-Julien · 2024
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Manipllm: Embodied multimodal large language model for object-centric robotic manipulation
Xiaoqi Li, Mingxu Zhang, Yiran Geng, Haoran Geng, Yuxing Long, Yan Shen, Renrui Zhang, Jiaming Liu, and Hao Dong · 2024
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Object-aware gaussian splatting for robotic manipulation
Yulong Li and Deepak Pathak · 2024
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Learning world models with identifiable factorization
Yuren Liu, Biwei Huang, Zhengmao Zhu, Honglong Tian, Mingming Gong, Yang Yu, and Kun Zhang · 2024
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Scaling object-centric robotic manipulation with multimodal object identification
Chaitanya Mitash, Mostafa Hussein, Jeroen Vanbaar, Vikedo Terhuja, and Kapil Katyal · 2024
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Robust agents learn causal world models
Jonathan Richens and Tom Everitt · 2024
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Acamda: Improving data efficiency in reinforcement learning through guided counterfactual data augmentation
Yuewen Sun, Erli Wang, Biwei Huang, Chaochao Lu, Lu Feng, Changyin Sun, and Kun Zhang · 2024
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Octo: An open-source generalist robot policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, et al · 2024
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Causal action influence aware counterfactual data augmentation
Núria Armengol Urpí, Marco Bagatella, Marin Vlastelica, and Georg Martius · 2024
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Neural assets: 3d-aware multi-object scene synthesis with image diffusion models
Ziyi Wu, Yulia Rubanova, Rishabh Kabra, Drew A Hudson, Igor Gilitschenski, Yusuf Aytar, Sjoerd van Steenkiste, Kelsey R Allen, and Thomas Kipf · 2024
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H-index: Visual reinforcement learning with hand-informed representations for dexterous manipulation
Yanjie Ze, Yuyao Liu, Ruizhe Shi, Jiaxin Qin, Zhecheng Yuan, Jiashun Wang, and Huazhe Xu · 2024
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Interpretable reward redistribution in reinforcement learning: a causal approach
Yudi Zhang, Yali Du, Biwei Huang, Ziyan Wang, Jun Wang, Meng Fang, and Mykola Pechenizkiy · 2024
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