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Found 25 papers, 5 papers with code
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning
This is the first theoretical result for randomized exploration in cooperative MARL.
On the Uniqueness of Solution for the Bellman Equation of LTL Objectives
In a widely-adopted surrogate reward approach, two discount factors are used to ensure that the expected return approximates the satisfaction probability of the LTL objective.
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment
Traditional commercial DBS devices are only able to deliver fixed-frequency periodic pulses to the basal ganglia (BG) regions of the brain, i. e., continuous DBS (cDBS).
Spectral Statistics of the Sample Covariance Matrix for High Dimensional Linear Gaussians
Performance of ordinary least squares(OLS) method for the \emph{estimation of high dimensional stable state transition matrix} $A$(i. e., spectral radius $\rho(A)<1$) from a single noisy observed trajectory of the linear time invariant(LTI)\footnote{Linear Gaussian (LG) in Markov chain literature} system $X_{-}:(x_0, x_1, \ldots, x_{N-1})$ satisfying \begin{equation} x_{t+1}=Ax_{t}+w_{t}, \hspace{10pt} \text{ where } w_{t} \thicksim N(0, I_{n}), \end{equation} heavily rely on negative moments of the sample covariance matrix: $(X_{-}X_{-}^{*})=\sum_{i=0}^{N-1}x_{i}x_{i}^{*}$ and singular values of $EX_{-}^{*}$, where $E$ is a rectangular Gaussian ensemble $E=[w_0, \ldots, w_{N-1}]$.
MadRadar: A Black-Box Physical Layer Attack Framework on mmWave Automotive FMCW Radars
Frequency modulated continuous wave (FMCW) millimeter-wave (mmWave) radars play a critical role in many of the advanced driver assistance systems (ADAS) featured on today's vehicles.
From Spectral Theorem to Statistical Independence with Application to System Identification
In the light of these findings we set the stage for non-asymptotic error analysis in estimation of state transition matrix $A$ via least squares regression on observed trajectory by showing that element-wise error is essentially a variant of well-know Littlewood-Offord problem.
Vulnerability Analysis of Nonlinear Control Systems to Stealthy False Data Injection Attacks
When the attacker has complete knowledge about the system, we show that if the closed loop system is incrementally exponentially stable while the open loop plant is incrementally unstable, then the system is vulnerable to stealthy yet impactful attacks on sensors.
Individual Treatment Effects in Extreme Regimes
Understanding individual treatment effects in extreme regimes is important for characterizing risks associated with different interventions.
Robust Reinforcement Learning through Efficient Adversarial Herding
In this work, we extend the two-player game by introducing an adversarial herd, which involves a group of adversaries, in order to address ($\textit{i}$) the difficulty of the inner optimization problem, and ($\textit{ii}$) the potential over pessimism caused by the selection of a candidate adversary set that may include unlikely scenarios.
A Modular Platform For Collaborative, Distributed Sensor Fusion
Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source.
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment
In this work, we introduce an offline reinforcement learning (RL) framework, allowing the use of past clinical data to train an RL policy to adjust the stimulation amplitude in real time, with the goal of reducing energy use while maintaining the same level of treatment (i. e., control) efficacy as cDBS.
Variational Latent Branching Model for Off-Policy Evaluation
In this work, we propose the variational latent branching model (VLBM) to learn the transition function of MDPs by formulating the environmental dynamics as a compact latent space, from which the next states and rewards are then sampled.
Concentration Phenomenon for Random Dynamical Systems: An Operator Theoretic Approach
Via operator theoretic methods, we formalize the concentration phenomenon for a given observable `$r$' of a discrete time Markov chain with `$\mu_{\pi}$' as invariant ergodic measure, possibly having support on an unbounded state space.
Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits
Specifically, we consider a general setup with a nonlinear affine physical plant controlled with a perception-based controller that maps both the physical (e. g., IMUs) and perceptual (e. g., camera) sensing to the control input; the system is also equipped with a statistical or learning-based anomaly detector (AD).
Transportation-Inequalities, Lyapunov Stability and Sampling for Dynamical Systems on Continuous State Space
We study the concentration phenomenon for discrete-time random dynamical systems with an unbounded state space.
Gradient Importance Learning for Incomplete Observations
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assumptions that may not align with real-world applications and could suffer from poor performance in subsequent tasks such as classification.
Security Analysis of Camera-LiDAR Fusion Against Black-Box Attacks on Autonomous Vehicles
Thus, in this work, we perform an analysis of camera-LiDAR fusion, in the AV context, under LiDAR spoofing attacks.
Model-Free Learning of Safe yet Effective Controllers
We study the problem of learning safe control policies that are also effective; i. e., maximizing the probability of satisfying a linear temporal logic (LTL) specification of a task, and the discounted reward capturing the (classic) control performance.
Learning-Based Vulnerability Analysis of Cyber-Physical Systems
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS).
Formal Verification of Stochastic Systems with ReLU Neural Network Controllers
Using this abstraction, we propose a method to compute tight bounds on the safety probabilities of nodes in this graph, despite possible over-approximations of the transition probabilities between these nodes.
Learning Optimal Strategies for Temporal Tasks in Stochastic Games
By deriving distinct rewards and discount factors from the acceptance condition of the DPA, we reduce the maximization of the worst-case probability of satisfying the LTL specification into the maximization of a discounted reward objective in the product game; this enables the use of model-free RL algorithms to learn an optimal controller strategy.
Probabilistic Conformance for Cyber-Physical Systems
In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatically transfer to the other, or one system can safely replace the other in practice.
Learning Expected Reward for Switched Linear Control Systems: A Non-Asymptotic View
In this work, we show existence of invariant ergodic measure for switched linear dynamical systems (SLDSs) under a norm-stability assumption of system dynamics in some unbounded subset of $\mathbb{R}^{n}$.
Learning Monotone Dynamics by Neural Networks
For monotonicity constraints, we propose to use nonnegative neural networks and batch normalization.
Control Synthesis from Linear Temporal Logic Specifications using Model-Free Reinforcement Learning
We present a reinforcement learning (RL) framework to synthesize a control policy from a given linear temporal logic (LTL) specification in an unknown stochastic environment that can be modeled as a Markov Decision Process (MDP).
