49 research outputs found
Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies
Deep learning has become an increasingly common technique for various control
problems, such as robotic arm manipulation, robot navigation, and autonomous
vehicles. However, the downside of using deep neural networks to learn control
policies is their opaque nature and the difficulties of validating their
safety. As the networks used to obtain state-of-the-art results become
increasingly deep and complex, the rules they have learned and how they operate
become more challenging to understand. This presents an issue, since in
safety-critical applications the safety of the control policy must be ensured
to a high confidence level. In this paper, we propose an automated black box
testing framework based on adversarial reinforcement learning. The technique
uses an adversarial agent, whose goal is to degrade the performance of the
target model under test. We test the approach on an autonomous vehicle problem,
by training an adversarial reinforcement learning agent, which aims to cause a
deep neural network-driven autonomous vehicle to collide. Two neural networks
trained for autonomous driving are compared, and the results from the testing
are used to compare the robustness of their learned control policies. We show
that the proposed framework is able to find weaknesses in both control policies
that were not evident during online testing and therefore, demonstrate a
significant benefit over manual testing methods.Comment: 2020 IEEE International Conference on Robotics and Automation (ICRA
Empirical Bounds on Linear Regions of Deep Rectifier Networks
We can compare the expressiveness of neural networks that use rectified
linear units (ReLUs) by the number of linear regions, which reflect the number
of pieces of the piecewise linear functions modeled by such networks. However,
enumerating these regions is prohibitive and the known analytical bounds are
identical for networks with same dimensions. In this work, we approximate the
number of linear regions through empirical bounds based on features of the
trained network and probabilistic inference. Our first contribution is a method
to sample the activation patterns defined by ReLUs using universal hash
functions. This method is based on a Mixed-Integer Linear Programming (MILP)
formulation of the network and an algorithm for probabilistic lower bounds of
MILP solution sets that we call MIPBound, which is considerably faster than
exact counting and reaches values in similar orders of magnitude. Our second
contribution is a tighter activation-based bound for the maximum number of
linear regions, which is particularly stronger in networks with narrow layers.
Combined, these bounds yield a fast proxy for the number of linear regions of a
deep neural network.Comment: AAAI 202