Fetching the paper…
Reading the bibliography…
The goal of multi-objective reinforcement learning (MORL) is to learn policies that simultaneously optimize multiple competing objectives.
A test for normality based on sample entropy
Oldrich Vasicek · 1976
Earlier work this paper cites.
Vector-valued markov decision processes and the systems of linear inequalities
Kazuyoshi Wakuta · 1995
Earlier work this paper cites.
On min-norm and min-max methods of multi-objective optimization
JiGuan G Lin · 2005
Earlier work this paper cites.
Multi-task reinforcement learning: a hierarchical bayesian approach
Aaron Wilson, Alan Fern, Soumya Ray, and Prasad Tadepalli · 2007
Earlier work this paper cites.
Moea/d: A multiobjective evolutionary algorithm based on decomposition
Qingfu Zhang and Hui Li · 2007
Earlier work this paper cites.
Bayesian multi-task reinforcement learning
Alessandro Lazaric and Mohammad Ghavamzadeh · 2010
Earlier work this paper cites.
Linear support for multi-objective coordination graphs
Diederik M Roijers, Shimon Whiteson, Frans A Oliehoek, et al · 2014
Earlier work this paper cites.
Multi-objective deep reinforcement learning
Hossam Mossalam, Yannis M Assael, Diederik M Roijers, and Shimon Whiteson · 2016
Earlier work this paper cites.
Multi-objective reinforcement learning through continuous pareto manifold approximation
Simone Parisi, Matteo Pirotta, and Marcello Restelli · 2016
Earlier work this paper cites.
Distral: Robust multitask reinforcement learning
Yee Teh, Victor Bapst, Wojciech M Czarnecki, John Quan, James Kirkpatrick, Raia Hadsell, Nicolas Heess, and Razvan Pascanu · 2017
Earlier work this paper cites.
Meta-learning for multi-objective reinforcement learning
Xi Chen, Ali Ghadirzadeh, Mårten Björkman, and Patric Jensfelt · 2019
Earlier work this paper cites.
Safe policy improvement with baseline bootstrapping
Romain Laroche, Paul Trichelair, and Remi Tachet Des Combes · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
A generalized algorithm for multi-objective reinforcement learning and policy adaptation
Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan · 2019
Cited alongside, same era.
Huixin Zhan and Yongcan Cao · 2019
Cited alongside, same era.
A distributional view on multi-objective policy optimization
Abbas Abdolmaleki, Sandy Huang, Leonard Hasenclever, Michael Neunert, Francis Song, Martina Zambelli, Murilo Martins, Nicolas Heess, Raia Hadsell, and Martin Riedmiller · 2020
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
Later among the works it cites.
Rvs: What is essential for offline rl via supervised learning?
Scott Emmons, Benjamin Eysenbach, Ilya Kostrikov, and Sergey Levine · 2021
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.
Multi-objective spibb: Seldonian offline policy improvement with safety constraints in finite mdps
Philip S Thomas, Joelle Pineau, Romain Laroche, et al · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D4rl: Datasets for deep data-driven reinforcement learning, 2020
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine · 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.
Scipy 1.0: fundamental algorithms for scientific computing in python
Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al · 2020
Cited alongside, same era.
Prediction-guided multi-objective reinforcement learning for continuous robot control
Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, and Wojciech Matusik · 2020
Cited alongside, same era.
Qinqing Zheng, Amy Zhang, and Aditya Grover · 2020
Cited alongside, same era.
Offline constrained multi-objective reinforcement learning via pessimistic dual value iteration
Runzhe Wu, Yufeng Zhang, Zhuoran Yang, and Zhaoran Wang · 2021
Later among the works it cites.
A practical guide to multi-objective reinforcement learning and planning
Conor F Hayes, Roxana Rădulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, et al · 2022
Later among the works it cites.
A constrained multi-objective reinforcement learning framework
Sandy Huang, Abbas Abdolmaleki, Giulia Vezzani, Philemon Brakel, Daniel J Mankowitz, Michael Neunert, Steven Bohez, Yuval Tassa, Nicolas Heess, Martin Riedmiller, et al · 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.
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.