Learning to Be Cautious

29 October 2021

Montaser Mohammedalamen

Papers citing "Learning to Be Cautious"

40 / 40 papers shown

Title
Epistemic Neural Networks Ian Osband Zheng Wen M. Asghari Vikranth Dwaracherla M. Ibrahimi Xiyuan Lu Benjamin Van Roy UQCV BDL 63 106 0 19 Jul 2021
Policy Gradient Bayesian Robust Optimization for Imitation Learning Zaynah Javed Daniel S. Brown Satvik Sharma Jerry Zhu Ashwin Balakrishna Marek Petrik Anca Dragan Ken Goldberg 98 16 0 11 Jun 2021
Risk-Averse Bayes-Adaptive Reinforcement Learning Marc Rigter Bruno Lacerda Nick Hawes 57 43 0 10 Feb 2021
Discovering a set of policies for the worst case reward Tom Zahavy André Barreto D. Mankowitz Shaobo Hou Brendan O'Donoghue Iurii Kemaev Satinder Singh OffRL 49 23 0 08 Feb 2021
Hindsight and Sequential Rationality of Correlated Play Dustin Morrill Ryan DÓrazio Reca Sarfati Marc Lanctot James Wright Amy Greenwald Michael Bowling 43 29 0 10 Dec 2020
Learning to be Safe: Deep RL with a Safety Critic K. Srinivasan Benjamin Eysenbach Sehoon Ha Jie Tan Chelsea Finn OffRL 75 144 0 27 Oct 2020
Safe Reinforcement Learning in Constrained Markov Decision Processes Akifumi Wachi Yanan Sui 51 149 0 15 Aug 2020
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings Jesse Zhang Brian Cheung Chelsea Finn Sergey Levine Dinesh Jayaraman 56 60 0 15 Aug 2020
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? Angelos Filos P. Tigas R. McAllister Nicholas Rhinehart Sergey Levine Y. Gal 46 187 0 26 Jun 2020
Safe Reinforcement Learning via Curriculum Induction M. Turchetta Andrey Kolobov S. Shah Andreas Krause Alekh Agarwal 37 92 0 22 Jun 2020
DREAM: Deep Regret minimization with Advantage baselines and Model-free learning Eric Steinberger Adam Lerer Noam Brown 68 54 0 18 Jun 2020
Stochastic Regret Minimization in Extensive-Form Games Gabriele Farina Christian Kroer Tuomas Sandholm 120 30 0 19 Feb 2020
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of $f$ -Regression Counterfactual Regret Minimization Ryan DÓrazio Dustin Morrill J. R. Wright Michael Bowling 45 10 0 06 Dec 2019
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 434 42,393 0 03 Dec 2019
Worst Cases Policy Gradients Yichuan Tang Jian Zhang Ruslan Salakhutdinov 56 75 0 09 Nov 2019
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning L. Zintgraf K. Shiarlis Maximilian Igl Sebastian Schulze Y. Gal Katja Hofmann Shimon Whiteson OffRL 53 277 0 18 Oct 2019
Estimating Risk and Uncertainty in Deep Reinforcement Learning W. Clements B. V. Delft Benoît-Marie Robaglia Reda Bahi Slaoui Sébastien Toth 54 97 0 23 May 2019
On the Convergence of Adam and Beyond Sashank J. Reddi Satyen Kale Surinder Kumar 87 2,498 0 19 Apr 2019
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent Edward Lockhart Marc Lanctot Julien Pérolat Jean-Baptiste Lespiau Dustin Morrill Finbarr Timbers K. Tuyls 116 82 0 13 Mar 2019
Bayesian Neural Network Ensembles Tim Pearce Mohamed H. Zaki A. Neely BDL UQCV 42 5 0 27 Nov 2018
Deep Counterfactual Regret Minimization Noam Brown Adam Lerer Sam Gross Tuomas Sandholm 112 215 0 01 Nov 2018
Out-of-Distribution Detection Using an Ensemble of Self Supervised Leave-out Classifiers Apoorv Vyas Nataraj Jammalamadaka Xia Zhu Dipankar Das Bharat Kaul Theodore L. Willke OODD 69 247 0 04 Sep 2018
A Lyapunov-based Approach to Safe Reinforcement Learning Yinlam Chow Ofir Nachum Edgar A. Duénez-Guzmán Mohammad Ghavamzadeh 158 506 0 20 May 2018
AI Safety Gridworlds Jan Leike Miljan Martic Victoria Krakovna Pedro A. Ortega Tom Everitt Andrew Lefrancq Laurent Orseau Shane Legg 102 253 0 27 Nov 2017
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms Han Xiao Kashif Rasul Roland Vollgraf 266 8,876 0 25 Aug 2017
A Distributional Perspective on Reinforcement Learning Marc G. Bellemare Will Dabney Rémi Munos OffRL 93 1,503 0 21 Jul 2017
Noisy Networks for Exploration Meire Fortunato M. G. Azar Bilal Piot Jacob Menick Ian Osband ... Rémi Munos Demis Hassabis Olivier Pietquin Charles Blundell Shane Legg 79 893 0 30 Jun 2017
Constrained Policy Optimization Joshua Achiam David Held Aviv Tamar Pieter Abbeel 110 1,322 0 30 May 2017
Safe Model-based Reinforcement Learning with Stability Guarantees Felix Berkenkamp M. Turchetta Angela P. Schoellig Andreas Krause 174 851 0 23 May 2017
Ensemble Sampling Xiuyuan Lu Benjamin Van Roy 123 119 0 20 May 2017
Deep Exploration via Randomized Value Functions Ian Osband Benjamin Van Roy Daniel Russo Zheng Wen 89 306 0 22 Mar 2017
Uncertainty-Aware Reinforcement Learning for Collision Avoidance G. Kahn Adam R. Villaflor Vitchyr H. Pong Pieter Abbeel Sergey Levine 93 313 0 03 Feb 2017
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 816 5,806 0 05 Dec 2016
Safe Policy Improvement by Minimizing Robust Baseline Regret Marek Petrik Yinlam Chow Mohammad Ghavamzadeh OffRL 89 134 0 13 Jul 2016
Safe Exploration in Finite Markov Decision Processes with Gaussian Processes M. Turchetta Felix Berkenkamp Andreas Krause 69 189 0 15 Jun 2016
Risk-Constrained Reinforcement Learning with Percentile Risk Criteria Yinlam Chow Mohammad Ghavamzadeh Lucas Janson Marco Pavone 70 512 0 05 Dec 2015
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach Yinlam Chow Aviv Tamar Shie Mannor Marco Pavone 118 317 0 06 Jun 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.7K 150,006 0 22 Dec 2014
Solving Games with Functional Regret Estimation Kevin Waugh Dustin Morrill J. Andrew Bagnell Michael Bowling OffRL 62 58 0 28 Nov 2014
Provably Safe and Robust Learning-Based Model Predictive Control A. Aswani Humberto González S. Shankar Sastry Claire Tomlin 94 524 0 13 Jul 2011