Estimating Risk and Uncertainty in Deep Reinforcement Learning

Risk-sensitive markov decision processes

Howard, R. A. and Matheson, J. E · 1972

Earlier work this paper cites.

A possibility for implementing curiosity and boredom in model-building neural controllers

Schmidhuber, J · 1991

Earlier work this paper cites.

Bayesian learning for neural networks , volume 118

Neal, R. M · 1995

Earlier work this paper cites.

Quantile regression

Koenker, R. and Hallock, K. F · 2001

Earlier work this paper cites.

Information theory, inference and learning algorithms

MacKay, D. J · 2003

Earlier work this paper cites.

Planning to be surprised: Optimal bayesian exploration in dynamic environments

Sun, Y., Gomez, F., and Schmidhuber, J · 2011

Earlier work this paper cites.

The arcade learning environment: an evaluation platform for general agents

Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M · 2013

Earlier work this paper cites.

Learning to optimize via information-directed sampling

Russo, D. and Van Roy, B · 2014

Earlier work this paper cites.

Weight uncertainty in neural networks

Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015

Earlier work this paper cites.

A comprehensive survey on safe reinforcement learning

Garcıa, J. and Fernández, F · 2015

Earlier work this paper cites.

Adam: A method for stochastic optimization

Kingma, D. P. and Ba, J · 2015

Earlier work this paper cites.

Human-level control through deep reinforcement learning

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al · 2015

Cited alongside, same era.

Incentivizing exploration in reinforcement learning with deep predictive models

Stadie, B. C., Levine, S., and Abbeel, P · 2015

Cited alongside, same era.

Dropout as a Bayesian approximation: representing model uncertainty in deep learning

Gal, Y. and Ghahramani, Z · 2016

Cited alongside, same era.

Risk versus uncertainty in deep learning: Bayes, bootstrap and the dangers of dropout

Osband, I · 2016

Cited alongside, same era.

Deep exploration via bootstrapped DQN

Osband, I., Blundell, C., Pritzel, A., and Van Roy, B · 2016

Cited alongside, same era.

Learning the variance of the reward-to-go

Tamar, A., Di Castro, D., and Mannor, S · 2016

Deep reinforcement learning in a handful of trials using probabilistic dynamics models

Chua, K., Calandra, R., McAllister, R., and Levine, S · 2018

Later among the works it cites.

Decomposition of uncertainty in bayesian deep learning for efficient and risk-sensitive learning

Depeweg, S., Hernandez-Lobato, J.-M., Doshi-Velez, F., and Udluft, S · 2018

Later among the works it cites.

Exploration by distributional reinforcement learning

Tang, Y. and Agrawal, S · 2018

Later among the works it cites.

Exploration by random network distillation

Burda, Y., Edwards, H., Storkey, A., and Klimov, O · 2019

Closest in time.

Model-predictive policy learning with uncertainty regularization for driving in dense traffic

Henaff, M., Canziani, A., and LeCun, Y · 2019

Closest in time.

Information-directed exploration for deep reinforcement learning

Nikolov, N., Kirschner, J., Berkenkamp, F., and Krause, A · 2019