Fetching the paper…
Reading the bibliography…
Recently, sequence learning methods have been applied to the problem of off-policy Reinforcement Learning, including the seminal work on Decision Transformers, which employs transformers for this task.
Behavior regularized offline reinforcement learning
Yifan Wu, George Tucker, and Ofir Nachum · 1911
Earlier work this paper cites.
Discrete dynamic programming
David Blackwell · 1962
Earlier work this paper cites.
Pracniques: further remarks on reducing truncation errors
William Kahan · 1965
Earlier work this paper cites.
Quasi double-precision in floating point addition
Ole Møller · 1965
Earlier work this paper cites.
On finding optimal policies in discrete dynamic programming with no discounting
Arthur F Veinott · 1966
Earlier work this paper cites.
Relative error propagation in the recursive solution of linear recurrence relations
J Oliver · 1967
Earlier work this paper cites.
What every computer scientist should know about floating-point arithmetic
David Goldberg · 1991
Earlier work this paper cites.
A reinforcement learning method for maximizing undiscounted rewards
Anton Schwartz · 1993
Earlier work this paper cites.
On-line Q-learning using connectionist systems , volume 37
Gavin A Rummery and Mahesan Niranjan · 1994
Earlier work this paper cites.
Reinforcement learning with long short-term memory
Bram Bakker · 2001
Earlier work this paper cites.
Accuracy and stability of numerical algorithms
Nicholas J Higham · 2002
Earlier work this paper cites.
Reinforcement learning by backpropagation through an lstm model/critic
Bram Bakker · 2007
Earlier work this paper cites.
Reinforcement learning of motor skills with policy gradients
Jan Peters and Stefan Schaal · 2008
Earlier work this paper cites.
Reinforcement learning with recurrent neural networks
Anton Maximilian Schäfer · 2008
Earlier work this paper cites.
Algorithms for variance reduction in a policy-gradient based actor-critic framework
Yogesh P Awate · 2009
Earlier work this paper cites.
Convergent temporal-difference learning with arbitrary smooth function approximation
Hamid Maei, Csaba Szepesvari, Shalabh Bhatnagar, Doina Precup, David Silver, and Richard S Sutton · 2009
Earlier work this paper cites.
Double q-learning
Hado Hasselt · 2010
Earlier work this paper cites.
Mujoco: A physics engine for model-based control
Emanuel Todorov, Tom Erez, and Yuval Tassa · 2012
Earlier work this paper cites.
Reinforcement learning in robotics: A survey
Jens Kober, J Andrew Bagnell, and Jan Peters · 2013
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Deterministic policy gradient algorithms
David Silver, Guy Lever, Nicolas Heess, Thomas Degris, Daan Wierstra, and Martin Riedmiller · 2014
Earlier work this paper cites.
Recurrent reinforcement learning: a hybrid approach
Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, and Ji He · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
Timothy P Lillicrap, Jonathan J Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra · 2015
Cited alongside, same era.
Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Cited alongside, same era.
Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, et al · 2016
Is long horizon reinforcement learning more difficult than short horizon reinforcement learning?
Ruosong Wang, Simon S Du, Lin F Yang, and Sham M Kakade · 2020
Later among the works it cites.
Deep reinforcement learning for long-term pavement maintenance planning
Linyi Yao, Qiao Dong, Jiwang Jiang, and Fujian Ni · 2020
Later among the works it cites.
Decision transformer: Reinforcement learning via sequence modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch · 2021
Later among the works it cites.
Generalized decision transformer for offline hindsight information matching
Hiroki Furuta, Yutaka Matsuo, and Shixiang Shane Gu · 2021
Later among the works it cites.
How to train your robot with deep reinforcement learning: lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, and Sergey Levine · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Sample efficient actor-critic with experience replay
Ziyu Wang, Victor Bapst, Nicolas Heess, Volodymyr Mnih, Remi Munos, Koray Kavukcuoglu, and Nando de Freitas · 2016
Cited alongside, same era.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Shixiang Gu, Ethan Holly, Timothy Lillicrap, and Sergey Levine · 2017
Cited alongside, same era.
Bridging the gap between value and policy based reinforcement learning
Ofir Nachum, Mohammad Norouzi, Kelvin Xu, and Dale Schuurmans · 2017
Cited alongside, same era.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Cited alongside, same era.
Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, and Sergey Levine · 2018
Cited alongside, same era.
Handbook of floating-point arithmetic
Jean-Michel Muller, Nicolas Brisebarre, Florent De Dinechin, Claude-Pierre Jeannerod, Vincent Lefevre, Guillaume Melquiond, Nathalie Revol, Damien Stehlé, Serge Torres, et al · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Later among the works it cites.
Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
Later among the works it cites.
Offline reinforcement learning with implicit q-learning
Ilya Kostrikov, Ashvin Nair, and Sergey Levine · 2021
Later among the works it cites.
Linghui Meng, Muning Wen, Yaodong Yang, Chenyang Le, Xiyun Li, Weinan Zhang, Ying Wen, Haifeng Zhang, Jun Wang, and Bo Xu · 2021
Later among the works it cites.
Amp: Adversarial motion priors for stylized physics-based character control
Xue Bin Peng, Ze Ma, Pieter Abbeel, Sergey Levine, and Angjoo Kanazawa · 2021
Later among the works it cites.
Synthetic returns for long-term credit assignment
David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, and Francis Song · 2021
Later among the works it cites.
Blind bipedal stair traversal via sim-to-real reinforcement learning
Jonah Siekmann, Kevin Green, John Warila, Alan Fern, and Jonathan Hurst · 2021
Later among the works it cites.
It’s raw! audio generation with state-space models
Karan Goel, Albert Gu, Chris Donahue, and Christopher Ré · 2022
Later among the works it cites.
Diagonal state spaces are as effective as structured state spaces
Ankit Gupta · 2022
Later among the works it cites.
Long movie clip classification with state-space video models
Md Mohaiminul Islam and Gedas Bertasius · 2022
Later among the works it cites.
Multi-game decision transformers
Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, et al · 2022
Later among the works it cites.
Long range language modeling via gated state spaces
Harsh Mehta, Ankit Gupta, Ashok Cutkosky, and Behnam Neyshabur · 2022
Later among the works it cites.
Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters
Xue Bin Peng, Yunrong Guo, Lina Halper, Sergey Levine, and Sanja Fidler · 2022
Later among the works it cites.
Scott Reed, Konrad Zolna, Emilio Parisotto, Sergio Gomez Colmenarejo, Alexander Novikov, Gabriel Barth-Maron, Mai Gimenez, Yury Sulsky, Jackie Kay, Jost Tobias Springenberg, et al · 2022
Later among the works it cites.
Can wikipedia help offline reinforcement learning?
Machel Reid, Yutaro Yamada, and Shixiang Shane Gu · 2022
Later among the works it cites.
Multi-agent reinforcement learning is a sequence modeling problem
Muning Wen, Jakub Grudzien Kuba, Runji Lin, Weinan Zhang, Ying Wen, Jun Wang, and Yaodong Yang · 2022
Later among the works it cites.
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2022
Later among the works it cites.