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Jonathan Uesato*

2 accepted papers

2020

Toward Evaluating Robustness of Deep Reinforcement Learning with Continuous Control

ICLR 2020poster

Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks. Prior works mostly foc…

Cited by 36SourceScholar
2019

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

ICLR 2019poster

This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. The standar…

Cited by 91SourcePDFScholar