Fetching the paper…
Reading the bibliography…
Recent advances in learning decision-making policies can largely be attributed to training expressive policy models, largely via imitation learning.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
Reinforcement learning by reward-weighted regression for operational space control
J. Peters and S. Schaal · 2007
Earlier work this paper cites.
Relative entropy policy search
Jan Peters, Katharina Mülling, and Yasemin Altün · 2010
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Earlier work this paper cites.
Maximum a posteriori policy optimisation
A. Abdolmaleki, J. T. Springenberg, Y. Tassa, R. Munos, N. Heess, and M. Riedmiller · 2018
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke van Hoof, and David Meger · 2018
Earlier work this paper cites.
Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, and Sergey Levine · 2018
Earlier work this paper cites.
Self-imitation learning
Junhyuk Oh, Yijie Guo, Satinder Singh, and Honglak Lee · 2018
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S Sutton and Andrew G Barto · 2018
Earlier work this paper cites.
Off-policy deep reinforcement learning without exploration
Scott Fujimoto, David Meger, and Doina Precup · 2019
Earlier work this paper cites.
Reward-conditioned policies
A. Kumar, X.B. Peng, and S. Levine · 2019
Earlier work this paper cites.
Advantage-weighted regression: Simple and scalable off-policy reinforcement learning
Xue Bin Peng, Aviral Kumar, Grace Zhang, and Sergey Levine · 2019
Earlier work this paper cites.
Q-learning for continuous actions with cross-entropy guided policies
Riley Simmons-Edler, Ben Eisner, Eric Mitchell, Sebastian Seung, and Daniel Lee · 2019
Earlier work this paper cites.
D4rl: Datasets for deep data-driven reinforcement learning
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 2020
Earlier work this paper cites.
Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Cited alongside, same era.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Cited alongside, same era.
Leveraging exploration in off-policy algorithms via normalizing flows
Bogdan Mazoure, Thang Doan, Audrey Durand, Joelle Pineau, and R Devon Hjelm · 2020
Cited alongside, same era.
Diffusion policy: Visuomotor policy learning via action diffusion
Cheng Chi, Zhenjia Xu, Siyuan Feng, Eric Cousineau, Yilun Du, Benjamin Burchfiel, Russ Tedrake, and Shuran Song · 2023
Later among the works it cites.
Idql: Implicit q-learning as an actor-critic method with diffusion policies
Philippe Hansen-Estruch, Ilya Kostrikov, Michael Janner, Jakub Grudzien Kuba, and Sergey Levine · 2023
Later among the works it cites.
Pre-training for robots: Offline rl enables learning new tasks from a handful of trials
Aviral Kumar, Anikait Singh, Frederik Ebert, Yanlai Yang, Chelsea Finn, and Sergey Levine · 2023
Later among the works it cites.
Skill-based model-based reinforcement learning
Lucy Xiaoyang Shi, Joseph J Lim, and Youngwoon Lee · 2023
Later among the works it cites.
Hybrid RL: Using both offline and online data can make RL efficient
Yuda Song, Yifei Zhou, Ayush Sekhari, Drew Bagnell, Akshay Krishnamurthy, and Wen Sun · 2023
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ashvin Nair, Murtaza Dalal, Abhishek Gupta, and Sergey Levine · 2020
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
Cited alongside, same era.
Emaq: Expected-max q-learning operator for simple yet effective offline and online rl
Seyed Kamyar Seyed Ghasemipour, Dale Schuurmans, and Shixiang Shane Gu · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Offline reinforcement learning as one big sequence modeling problem
Michael Janner, Qiyang Li, and Sergey Levine · 2021
Cited alongside, same era.
Bridge data: Boosting generalization of robotic skills with cross-domain datasets
Frederik Ebert, Yanlai Yang, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, and Sergey Levine · 2022
Cited alongside, same era.
Calvin: A benchmark for language-conditioned policy learning for long-horizon robot manipulation tasks
Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard · 2022
Cited alongside, same era.
Later among the works it cites.
Bridgedata v2: A dataset for robot learning at scale
Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, and Sergey Levine · 2023
Later among the works it cites.
Q-learning decision transformer: Leveraging dynamic programming for conditional sequence modelling in offline rl
Taku Yamagata, Ahmed Khalil, and Raul Santos-Rodriguez · 2023
Later among the works it cites.
Policy representation via diffusion probability model for reinforcement learning
Long Yang, Zhixiong Huang, Fenghao Lei, Yucun Zhong, Yiming Yang, Cong Fang, Shiting Wen, Binbin Zhou, and Zhouchen Lin · 2023
Later among the works it cites.
Rt-2: Vision-language-action models transfer web knowledge to robotic control
Brianna Zitkovich, Tianhe Yu, Sichun Xu, Peng Xu, Ted Xiao, Fei Xia, Jialin Wu, Paul Wohlhart, Stefan Welker, Ayzaan Wahid, et al · 2023
Later among the works it cites.
Digirl: Training in-the-wild device-control agents with autonomous reinforcement learning
Hao Bai, Yifei Zhou, Mert Cemri, Jiayi Pan, Alane Suhr, Sergey Levine, and Aviral Kumar · 2024
Closest in time.
Openvla: An open-source vision-language-action model
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, et al · 2024
Closest in time.
Learning multimodal behaviors from scratch with diffusion policy gradient
Zechu Li, Rickmer Krohn, Tao Chen, Anurag Ajay, Pulkit Agrawal, and Georgia Chalvatzaki · 2024
Closest in time.
Is value learning really the main bottleneck in offline rl?
Seohong Park, Kevin Frans, Sergey Levine, and Aviral Kumar · 2024
Closest in time.
Diffusion policy policy optimization
Allen Z Ren, Justin Lidard, Lars L Ankile, Anthony Simeonov, Pulkit Agrawal, Anirudha Majumdar, Benjamin Burchfiel, Hongkai Dai, and Max Simchowitz · 2024
Closest in time.
V-former: Offline RL with temporally-extended actions, 2024
Jeffrey Wu, Seohong Park, Zipeng Lin, Jianlan Luo, and Sergey Levine · 2024
Closest in time.