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The temporal credit assignment problem is a central challenge in Reinforcement Learning (RL), concerned with attributing the appropriate influence to each actions in a trajectory for their ability to achieve a goal.
Steps toward artificial intelligence
Marvin Minsky · 1961
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Temporal credit assignment in reinforcement learning
Richard S Sutton · 1984
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Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y Ng, Daishi Harada, and Stuart Russell · 1999
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Large scale distributed deep networks
Jeffrey Dean, Greg Corrado, Rajat Monga, Kai Chen, Matthieu Devin, Mark Mao, Marc’aurelio Ranzato, Andrew Senior, Paul Tucker, Ke Yang, et al · 2012
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The arcade learning environment: An evaluation platform for general agents
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling · 2013
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Hierarchical reinforcement learning: a survey
Mostafa Al-Emran · 2015
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Minimalistic gridworld environment for openai gym
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal · 2018
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Using natural language for reward shaping in reinforcement learning
Prasoon Goyal, Scott Niekum, and Raymond J Mooney · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn · 2019
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Keep calm and explore: Language models for action generation in text-based games
Shunyu Yao, Rohan Rao, Matthew Hausknecht, and Karthik Narasimhan · 2020
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Rtfm: Generalising to new environment dynamics via reading
Victor Zhong, Tim Rocktäschel, and Edward Grefenstette · 2020
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The nethack learning environment
Heinrich Küttler, Nantas Nardelli, Alexander Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, and Tim Rocktäschel · 2020
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Reinforcement learning with trajectory feedback
Yonathan Efroni, Nadav Merlis, and Shie Mannor · 2021
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Minihack the planet: A sandbox for open-ended reinforcement learning research
Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Kuttler, Edward Grefenstette, and Tim Rocktäschel · 2021
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Counterfactual credit assignment in model-free reinforcement learning
Thomas Mesnard, Theophane Weber, Fabio Viola, Shantanu Thakoor, Alaa Saade, Anna Harutyunyan, Will Dabney, Thomas S Stepleton, Nicolas Heess, Arthur Guez, et al · 2021
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Multitasking inhibits semantic drift
Athul Paul Jacob, Mike Lewis, and Jacob Andreas · 2021
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Skill induction and planning with latent language
Pratyusha Sharma, Antonio Torralba, and Jacob Andreas · 2021
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Danijar Hafner · 2021
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Unpacking reward shaping: Understanding the benefits of reward engineering on sample complexity
Abhishek Gupta, Aldo Pacchiano, Yuexiang Zhai, Sham Kakade, and Sergey Levine · 2022
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History compression via language models in reinforcement learning
Fabian Paischer, Thomas Adler, Vihang Patil, Angela Bitto-Nemling, Markus Holzleitner, Sebastian Lehner, Hamid Eghbal-Zadeh, and Sepp Hochreiter · 2022
Zihao Wang, Shaofei Cai, Guanzhou Chen, Anji Liu, Xiaojian Ma, and Yitao Liang · 2023
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Progprompt: Generating situated robot task plans using large language models
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg · 2023
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Do as i can, not as i say: Grounding language in robotic affordances
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al · 2023
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Collaborating with language models for embodied reasoning
Ishita Dasgupta, Christine Kaeser-Chen, Kenneth Marino, Arun Ahuja, Sheila Babayan, Felix Hill, and Rob Fergus · 2023
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Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
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Improving policy learning via language dynamics distillation
Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, and Tim Rocktäschel · 2022
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Code as policies: Language model programs for embodied control, 2022
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Improving intrinsic exploration with language abstractions
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Grounded decoding: Guiding text generation with grounded models for robot control
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Reward design with language models, 2023
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Quantile credit assignment
Thomas Mesnard, Wenqi Chen, Alaa Saade, Yunhao Tang, Mark Rowland, Theophane Weber, Clare Lyle, Audrunas Gruslys, Michal Valko, Will Dabney, Georg Ostrovski, Eric Moulines, and Remi Munos · 2023
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Qlora: Efficient finetuning of quantized llms. arxiv 2023
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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Vision-language models as a source of rewards
Kate Baumli, Satinder Baveja, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, et al · 2023
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A survey of temporal credit assignment in deep reinforcement learning
Eduardo Pignatelli, Johan Ferret, Matthieu Geist, Thomas Mesnard, Hado van Hasselt, and Laura Toni · 2024
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Optimizing agent behavior over long time scales by transporting value
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