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The use of Reinforcement Learning (RL) agents in practical applications requires the consideration of suboptimal outcomes, depending on the familiarity of the agent with its environment.
Optimization of conditional value-at-risk
R. T. Rockafellar and S. Uryasev · 2000
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Properties of distortion risk measures
A. Balbás, J. Garrido, and S. Mayoral · 2009
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Algorithms for CVaR optimization in MDPs
Y. Chow and M. Ghavamzadeh · 2014
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A comprehensive survey on safe reinforcement learning
J. García and F. Fernández · 2015
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Safe exploration for optimization with gaussian processes
Y. Sui, A. Gotovos, J. W. Burdick, and A. Krause · 2015
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Robust adversarial reinforcement learning
L. Pinto, J. Davidson, R. Sukthankar, and A. Gupta · 2017
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A distributional perspective on reinforcement learning
M. G. Bellemare, W. Dabney, and R. Munos · 2017
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Minimalistic gridworld environment for openai gym
M. Chevalier-Boisvert, L. Willems, and S. Pal · 2018
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Risk-aware active inverse reinforcement learning
D. S. Brown, Y. Cui, and S. Niekum · 2019
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Exploration by random network distillation
Y. Burda, H. Edwards, A. J. Storkey, and O. Klimov · 2019
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Quantifying generalization in reinforcement learning
K. Cobbe, O. Klimov, C. Hesse, T. Kim, and J. Schulman · 2019
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Pybullet gymperium
B. Ellenberger · 2019
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Sim-to-real transfer in deep reinforcement learning for robotics: a survey
W. Zhao, J. P. Queralta, and T. Westerlund · 2020
Cited alongside, same era.
Safe reinforcement learning using probabilistic shields (invited paper)
N. Jansen, B. Könighofer, S.n Junges, A. Serban, and R. Bloem · 2020
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DSAC: Distributional soft actor critic for risk-sensitive reinforcement learning, 2020
X. Ma, L. Xia, Z. Zhou, J. Yang, and Q. Zhao · 2020
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Being optimistic to be conservative: Quickly learning a CVaR policy
R. Keramati, C. Dann, A. Tamkin, and E. Brunskill · 2020
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Likelihood quantile networks for coordinating multi-agent reinforcement learning
X. Lyu and C. Amato · 2020
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Improving robustness via risk averse distributional reinforcement learning
R. Singh, Q. Zhang, and Y. Chen · 2020
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Risk-conditioned distributional soft actor-critic for risk-sensitive navigation
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Safe reinforcement learning via curriculum induction
M. Turchetta, A. Kolobov, S. Shah, A. Krause, and A. Agarwal · 2020
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Implicit quantile networks for distributional reinforcement learning
W. Dabney, G. Ostrovski, D. Silver, and R. Munos
Cited in the paper.
Distributional reinforcement learning with quantile regression
W. Dabney, M. Rowland, M. G. Bellemare, and R. Munos
Cited in the paper.
J. Choi, C. R. Dance, J.-E. Kim, S. Hwang, and K. Park · 2021
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Risk-aware model-based control
C. Yu and A. Rosendo · 2021
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