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Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment.
Solving rubik’s cube with a robot hand
Akkaya, I.; Andrychowicz, M.; Chociej, M.; Litwin, M.; McGrew, B.; Petron, A.; Paino, A.; Plappert, M.; Powell, G.; Ribas, R.; et al. 2019 · 1910
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Consideration of risk in reinforcement learning
Heger, M. 1994 · 1994
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A survey of POMDP applications
Cassandra, A. R. 1998 · 1998
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Risk-sensitive and minimax control of discrete-time, finite-state Markov decision processes
Coraluppi, S. P.; and Marcus, S. I. 1999 · 1999
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An empirical investigation of the challenges of real-world reinforcement learning
Dulac-Arnold, G.; Levine, N.; Mankowitz, D. J.; Li, J.; Paduraru, C.; Gowal, S.; and Hester, T. 2020 · 2003
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Evolutionary optimization in uncertain environments-a survey
Jin, Y.; and Branke, J. 2005 · 2005
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Robust reinforcement learning
Morimoto, J.; and Doya, K. 2005 · 2005
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The CMA evolution strategy: a comparing review
Hansen, N. 2006 · 2006
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Double Q-learning
Hasselt, H. 2010 · 2010
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Speedy Q-learning
Azar, M. G.; Munos, R.; Ghavamzadeh, M.; and Kappen, H. 2011 · 2011
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Abandoning objectives: Evolution through the search for novelty alone
Lehman, J.; and Stanley, K. O. 2011 · 2011
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Introduction to robust estimation and hypothesis testing
Wilcox, R. R. 2011 · 2011
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Natural evolution strategies
Wierstra, D.; Schaul, T.; Glasmachers, T.; Sun, Y.; Peters, J.; and Schmidhuber, J. 2014 · 2014
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Robots that can adapt like animals
Cully, A.; Clune, J.; Tarapore, D.; and Mouret, J.-B. 2015 · 2015
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Taming the noise in reinforcement learning via soft updates
Fox, R.; Pakman, A.; and Tishby, N. 2015 · 2015
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A comprehensive survey on safe reinforcement learning
Garcıa, J.; and Fernández, F. 2015 · 2015
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Quality diversity: A new frontier for evolutionary computation
Pugh, J. K.; Soros, L. B.; and Stanley, K. O. 2016 · 2016
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A distributional perspective on reinforcement learning
Bellemare, M. G.; Dabney, W.; and Munos, R. 2017 · 2017
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Reinforcement learning with a corrupted reward channel
Everitt, T.; Krakovna, V.; Orseau, L.; Hutter, M.; and Legg, S. 2017 · 2017
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Noisy evolutionary optimization algorithms–a comprehensive survey
Rakshit, P.; Konar, A.; and Das, S. 2017 · 2017
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Evolution strategies as a scalable alternative to reinforcement learning
Tassa, Y.; Doron, Y.; Muldal, A.; Erez, T.; Li, Y.; Casas, D. d. L.; Budden, D.; Abdolmaleki, A.; Merel, J.; Lefrancq, A.; et al. 2018 · 2018
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Autonomous skill discovery with quality-diversity and unsupervised descriptors
Cully, A. 2019 · 2019
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An overview of robust reinforcement learning
Chen, S.; and Li, Y. 2020 · 2020
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Learning latent plans from play
Lynch, C.; Khansari, M.; Xiao, T.; Kumar, V.; Tompson, J.; Levine, S.; and Sermanet, P. 2020 · 2020
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Effective diversity in population based reinforcement learning
Parker-Holder, J.; Pacchiano, A.; Choromanski, K. M.; and Roberts, S. J. 2020 · 2020
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Reinforcement learning with perturbed rewards
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Salimans, T.; Ho, J.; Chen, X.; Sidor, S.; and Sutskever, I. 2017 · 2017
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The robustness-performance tradeoff in Markov decision processes
Xu, H.; and Mannor, S. 2006 · 2017
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A tutorial on Bayesian optimization
Frazier, P. I. 2018 · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S.; Hoof, H.; and Meger, D. 2018 · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T.; Zhou, A.; Abbeel, P.; and Levine, S. 2018 · 2018
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Deep reinforcement learning that matters
Henderson, P.; Islam, R.; Bachman, P.; Pineau, J.; Precup, D.; and Meger, D. 2018 · 2018
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ES is more than just a traditional finite-difference approximator
Lehman, J.; Chen, J.; Clune, J.; and Stanley, K. O. 2018 · 2018
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Wang, J.; Liu, Y.; and Li, B. 2020 · 2020
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Deep reinforcement learning at the edge of the statistical precipice
Agarwal, R.; Schwarzer, M.; Castro, P. S.; Courville, A. C.; and Bellemare, M. 2021 · 2021
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Quality-Diversity Optimization: a novel branch of stochastic optimization
Chatzilygeroudis, K.; Cully, A.; Vassiliades, V.; and Mouret, J.-B. 2021 · 2021
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Brax–A Differentiable Physics Engine for Large Scale Rigid Body Simulation
Freeman, C. D.; Frey, E.; Raichuk, A.; Girgin, S.; Mordatch, I.; and Bachem, O. 2021 · 2021
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Safe learning in robotics: From learning-based control to safe reinforcement learning
Brunke, L.; Greeff, M.; Hall, A. W.; Yuan, Z.; Zhou, S.; Panerati, J.; and Schoellig, A. P. 2022 · 2022
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A review of safe reinforcement learning: Methods, theory and applications
Gu, S.; Yang, L.; Du, Y.; Chen, G.; Walter, F.; Wang, J.; Yang, Y.; and Knoll, A. 2022 · 2022
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Robust reinforcement learning: A review of foundations and recent advances
Moos, J.; Hansel, K.; Abdulsamad, H.; Stark, S.; Clever, D.; and Peters, J. 2022 · 2022
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Distributional reinforcement learning
Bellemare, M. G.; Dabney, W.; and Rowland, M. 2023 · 2023
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Uncertain Quality-Diversity: Evaluation methodology and new methods for Quality-Diversity in Uncertain Domains
Flageat, M.; and Cully, A. 2023 · 2023
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