Fetching the paper…
Reading the bibliography…
Safe offline RL is a promising way to bypass risky online interactions towards safe policy learning.
Residual algorithms: Reinforcement learning with function approximation
Leemon Baird · 1995
Earlier work this paper cites.
Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford · 2002
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
Earlier work this paper cites.
Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
Earlier work this paper cites.
Hamilton-jacobi reachability: A brief overview and recent advances
Somil Bansal, Mo Chen, Sylvia Herbert, and Claire J Tomlin · 2017
Earlier work this paper cites.
Risk-constrained reinforcement learning with percentile risk criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone · 2017
Earlier work this paper cites.
Jax: composable transformations of python+ numpy programs
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, et al · 2018
Earlier work this paper cites.
Safe exploration in continuous action spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, and Yuval Tassa · 2018
Earlier work this paper cites.
Addressing function approximation error in actor-critic methods
Scott Fujimoto, Herke Hoof, and David Meger · 2018
Earlier work this paper cites.
Reward constrained policy optimization
Chen Tessler, Daniel J Mankowitz, and Shie Mannor · 2018
Earlier work this paper cites.
Control barrier functions: Theory and applications
Aaron D Ames, Samuel Coogan, Magnus Egerstedt, Gennaro Notomista, Koushil Sreenath, and Paulo Tabuada · 2019
Earlier work this paper cites.
An efficient reachability-based framework for provably safe autonomous navigation in unknown environments
Andrea Bajcsy, Somil Bansal, Eli Bronstein, Varun Tolani, and Claire J Tomlin · 2019
Earlier work this paper cites.
Bridging hamilton-jacobi safety analysis and reinforcement learning
Jaime F Fisac, Neil F Lugovoy, Vicenç Rubies-Royo, Shromona Ghosh, and Claire J Tomlin · 2019
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.
Stabilizing off-policy q-learning via bootstrapping error reduction
Aviral Kumar, Justin Fu, Matthew Soh, George Tucker, and Sergey Levine · 2019
Earlier work this paper cites.
Batch policy learning under constraints
Hoang Le, Cameron Voloshin, and Yisong Yue · 2019
Earlier work this paper cites.
Benchmarking Safe Exploration in Deep Reinforcement Learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Reinforcement learning for safety-critical control under model uncertainty, using control lyapunov functions and control barrier functions
Jason Choi, Fernando Castañeda, Claire J Tomlin, and Koushil Sreenath · 2020
Earlier work this paper cites.
Natural policy gradient primal-dual method for constrained markov decision processes
Dongsheng Ding, Kaiqing Zhang, Tamer Basar, and Mihailo Jovanovic · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Earlier work this paper cites.
Conservative q-learning for offline reinforcement learning
Aviral Kumar, Aurick Zhou, George Tucker, and Sergey Levine · 2020
Earlier work this paper cites.
Offline reinforcement learning: Tutorial, review, and perspectives on open problems
Sergey Levine, Aviral Kumar, George Tucker, and Justin Fu · 2020
Earlier work this paper cites.
Learning control barrier functions from expert demonstrations
Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V Dimarogonas, Stephen Tu, and Nikolai Matni · 2020
Earlier work this paper cites.
Responsive safety in reinforcement learning by pid lagrangian methods
Adam Stooke, Joshua Achiam, and Pieter Abbeel · 2020
Cited alongside, same era.
Safe reinforcement learning in constrained markov decision processes
Akifumi Wachi and Yanan Sui · 2020
Cited alongside, same era.
Projection-based constrained policy optimization
Tsung-Yen Yang, Justinian Rosca, Karthik Narasimhan, and Peter J Ramadge · 2020
Cited alongside, same era.
First order constrained optimization in policy space
Yiming Zhang, Quan Vuong, and Keith Ross · 2020
Cited alongside, same era.
Constrained Markov decision processes
Eitan Altman · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Mildly conservative q-learning for offline reinforcement learning
Jiafei Lyu, Xiaoteng Ma, Xiu Li, and Zongqing Lu · 2022
Later among the works it cites.
Joint synthesis of safety certificate and safe control policy using constrained reinforcement learning
Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, and Jianyu Chen · 2022
Later among the works it cites.
When to trust your simulator: Dynamics-aware hybrid offline-and-online reinforcement learning
Haoyi Niu, Yiwen Qiu, Ming Li, Guyue Zhou, Jianming HU, Xianyuan Zhan, et al · 2022
Later among the works it cites.
Sauté rl: Almost surely safe reinforcement learning using state augmentation
Aivar Sootla, Alexander I Cowen-Rivers, Taher Jafferjee, Ziyan Wang, David H Mguni, Jun Wang, and Haitham Ammar · 2022
Later among the works it cites.
Rorl: Robust offline reinforcement learning via conservative smoothing
Rui Yang, Chenjia Bai, Xiaoteng Ma, Zhaoran Wang, Chongjie Zhang, and Lei Han · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Robust control barrier–value functions for safety-critical control
Jason J Choi, Donggun Lee, Koushil Sreenath, Claire J Tomlin, and Sylvia L Herbert · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Cited alongside, same era.
A minimalist approach to offline reinforcement learning
Scott Fujimoto and Shixiang Shane Gu · 2021
Cited alongside, same era.
Classifier-free diffusion guidance
Jonathan Ho and Tim Salimans · 2021
Cited alongside, same era.
Deep reinforcement learning for autonomous driving: A survey
B Ravi Kiran, Ibrahim Sobh, Victor Talpaert, Patrick Mannion, Ahmad A Al Sallab, Senthil Yogamani, and Patrick Pérez · 2021
Cited alongside, same era.
Offline reinforcement learning with fisher divergence critic regularization
Ilya Kostrikov, Rob Fergus, Jonathan Tompson, and Ofir Nachum · 2021
Cited alongside, same era.
Deepthermal: Combustion optimization for thermal power generating units using offline reinforcement learning
Xianyuan Zhan, Haoran Xu, Yue Zhang, Xiangyu Zhu, Honglei Yin, and Yu Zheng · 2022
Later among the works it cites.
Is conditional generative modeling all you need for decision making?
Anurag Ajay, Yilun Du, Abhi Gupta, Joshua B Tenenbaum, Tommi S Jaakkola, and Pulkit Agrawal · 2023
Later among the works it cites.
Offline reinforcement learning via high-fidelity generative behavior modeling
Huayu Chen, Cheng Lu, Chengyang Ying, Hang Su, and Jun Zhu · 2023
Later among the works it cites.
Look beneath the surface: Exploiting fundamental symmetry for sample-efficient offline rl
Peng Cheng, Xianyuan Zhan, Zhihao Wu, Wenjia Zhang, Shoucheng Song, Han Wang, Youfang Lin, and Li Jiang · 2023
Later among the works it cites.
Extreme q-learning: Maxent rl without entropy
Divyansh Garg, Joey Hejna, Matthieu Geist, and Stefano Ermon · 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.
Omnisafe: An infrastructure for accelerating safe reinforcement learning research
Jiaming Ji, Jiayi Zhou, Borong Zhang, Juntao Dai, Xuehai Pan, Ruiyang Sun, Weidong Huang, Yiran Geng, Mickel Liu, and Yaodong Yang · 2023
Later among the works it cites.
Efficient diffusion policies for offline reinforcement learning
Bingyi Kang, Xiao Ma, Chao Du, Tianyu Pang, and Shuicheng Yan · 2023
Later among the works it cites.
Jeeseop Kim, Jaemin Lee, and Aaron D Ames · 2023
Later among the works it cites.
Jaemin Lee, Jeeseop Kim, and Aaron D Ames · 2023
Later among the works it cites.
Reinforcement learning for sequential decision and optimal control
Shengbo Eben Li · 2023
Later among the works it cites.
Safe offline reinforcement learning with real-time budget constraints
Qian Lin, Bo Tang, Zifan Wu, Chao Yu, Shangqin Mao, Qianlong Xie, Xingxing Wang, and Dong Wang · 2023
Later among the works it cites.
Contrastive energy prediction for exact energy-guided diffusion sampling in offline reinforcement learning
Cheng Lu, Huayu Chen, Jianfei Chen, Hang Su, Chongxuan Li, and Jun Zhu · 2023
Later among the works it cites.
Revealing the mystery of distribution correction estimation via orthogonal-gradient update
Liyuan Mao, Haoran Xu, Weinan Zhang, and Xianyuan Zhan · 2023
Later among the works it cites.
Data-driven safety filters: Hamilton-jacobi reachability, control barrier functions, and predictive methods for uncertain systems
Kim P Wabersich, Andrew J Taylor, Jason J Choi, Koushil Sreenath, Claire J Tomlin, Aaron D Ames, and Melanie N Zeilinger · 2023
Later among the works it cites.
The in-sample softmax for offline reinforcement learning
Chenjun Xiao, Han Wang, Yangchen Pan, Adam White, and Martha White · 2023
Later among the works it cites.
Offline RL with no OOD actions: In-sample learning via implicit value regularization
Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, and Xianyuan Zhan · 2023
Later among the works it cites.
State-wise safe reinforcement learning: A survey
Weiye Zhao, Tairan He, Rui Chen, Tianhao Wei, and Changliu Liu · 2023
Later among the works it cites.