Ryan W. Gardner
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View article: Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL)
Continuous Mean-Zero Disagreement-Regularized Imitation Learning (CMZ-DRIL) Open
Machine-learning paradigms such as imitation learning and reinforcement learning can generate highly performant agents in a variety of complex environments. However, commonly used methods require large quantities of data and/or a known rew…
View article: Attacking the Diebold Signature Variant -- RSA Signatures with Unverified High-order Padding
Attacking the Diebold Signature Variant -- RSA Signatures with Unverified High-order Padding Open
We examine a natural but improper implementation of RSA signature verification deployed on the widely used Diebold Touch Screen and Optical Scan voting machines. In the implemented scheme, the verifier fails to examine a large number of th…
View article: A Risk-Sensitive Approach to Policy Optimization
A Risk-Sensitive Approach to Policy Optimization Open
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outl…
View article: A Risk-Sensitive Approach to Policy Optimization
A Risk-Sensitive Approach to Policy Optimization Open
Standard deep reinforcement learning (DRL) aims to maximize expected reward, considering collected experiences equally in formulating a policy. This differs from human decision-making, where gains and losses are valued differently and outl…
View article: Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning Open
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many app…
View article: On the Complexity of Reconnaissance Blind Chess
On the Complexity of Reconnaissance Blind Chess Open
This paper provides a complexity analysis for the game of reconnaissance blind chess (RBC), a recently-introduced variant of chess where each player does not know the positions of the opponent's pieces a priori but may reveal a subset of t…
View article: Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning Open
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applicati…
View article: Adaptive Stress Testing: Finding Failure Events with Reinforcement Learning
Adaptive Stress Testing: Finding Failure Events with Reinforcement Learning Open
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars. In many applicati…
View article: Formal verification of ACAS X, an industrial airborne collision avoidance system
Formal verification of ACAS X, an industrial airborne collision avoidance system Open
Formal verification of industrial systems is very challenging, due to reasons ranging from scalability issues to communication difficulties with engineering-focused teams. More importantly, industrial systems are rarely designed for verifi…