Fetching the paper…
Reading the bibliography…
Deploying Reinforcement Learning (RL) agents in the real-world require that the agents satisfy safety constraints.
Learning to predict by the methods of temporal differences
Richard S Sutton · 1988
Earlier work this paper cites.
Constrained markov decision processes with total cost criteria: Lagrangian approach and dual linear program
Eitan Altman · 1998
Earlier work this paper cites.
Constrained Markov decision processes , volume 7
Eitan Altman · 1999
Earlier work this paper cites.
Optimization of conditional value-at-risk
R Tyrrell Rockafellar, Stanislav Uryasev, et al · 2000
Earlier work this paper cites.
Lyapunov design for safe reinforcement learning
Theodore J Perkins and Andrew G Barto · 2002
Earlier work this paper cites.
Risk-sensitive reinforcement learning applied to control under constraints
Peter Geibel and Fritz Wysotzki · 2005
Earlier work this paper cites.
Risk-constrained markov decision processes
Vivek Borkar and Rahul Jain · 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.
Algorithms for cvar optimization in mdps
Yinlam Chow and Mohammad Ghavamzadeh · 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.
Optimizing the cvar via sampling
Aviv Tamar, Yonatan Glassner, and Shie Mannor · 2014
Earlier work this paper cites.
Trust region policy optimization
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz · 2015
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
Cited alongside, same era.
Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
Cited alongside, same era.
Safe model-based reinforcement learning with stability guarantees
Felix Berkenkamp, Matteo Turchetta, Angela Schoellig, and Andreas Krause · 2017
Cited alongside, same era.
Risk-constrained reinforcement learning with percentile risk criteria
Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, and Marco Pavone · 2017
Cited alongside, same era.
Solving rubik’s cube with a robot hand
Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, et al · 2019
Later among the works it cites.
Lyapunov-based safe policy optimization for continuous control
Yinlam Chow, Ofir Nachum, Aleksandra Faust, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2019
Later among the works it cites.
Being optimistic to be conservative: Quickly learning a cvar policy
Ramtin Keramati, Christoph Dann, Alex Tamkin, and Emma Brunskill · 2019
Later among the works it cites.
Benchmarking Safe Exploration in Deep Reinforcement Learning
Alex Ray, Joshua Achiam, and Dario Amodei · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
Cited alongside, same era.
A lyapunov-based approach to safe reinforcement learning
Yinlam Chow, Ofir Nachum, Edgar Duenez-Guzman, and Mohammad Ghavamzadeh · 2018
Cited alongside, same era.
Safe exploration in continuous action spaces
Gal Dalal, Krishnamurthy Dvijotham, Matej Vecerik, Todd Hester, Cosmin Paduraru, and Yuval Tassa · 2018
Cited alongside, same era.
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, et al · 2018
Cited alongside, same era.
Optlayer-practical constrained optimization for deep reinforcement learning in the real world
Tu-Hoa Pham, Giovanni De Magistris, and Ryuki Tachibana · 2018
Cited alongside, same era.
Yichuan Charlie Tang, Jian Zhang, and Ruslan Salakhutdinov · 2019
Later among the works it cites.
Safe exploration for interactive machine learning
Matteo Turchetta, Felix Berkenkamp, and Andreas Krause · 2019
Later among the works it cites.
Grandmaster level in starcraft ii using multi-agent reinforcement learning
Oriol Vinyals, Igor Babuschkin, Wojciech M Czarnecki, Michaël Mathieu, Andrew Dudzik, Junyoung Chung, David H Choi, Richard Powell, Timo Ewalds, Petko Georgiev, et al · 2019
Later among the works it cites.
Safety augmented value estimation from demonstrations (saved): Safe deep model-based rl for sparse cost robotic tasks
Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E Gonzalez, Sergey Levine, Francesco Borrelli, and Ken Goldberg · 2020
Later among the works it cites.
Safe reinforcement learning in constrained markov decision processes
Akifumi Wachi and Yanan Sui · 2020
Later among the works it cites.
Cautious adaptation for reinforcement learning in safety-critical settings
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, and Dinesh Jayaraman · 2020
Later among the works it cites.