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Sparse but Strong: Crafting Adversarially Robust Graph Lottery Tickets
By iteratively applying ARGS to prune both the perturbed graph adjacency matrix and the GNN model weights, we can find adversarially robust graph lottery tickets that are highly sparse yet achieve competitive performance under different untargeted training-time structure attacks.
Regret Analysis of the Posterior Sampling-based Learning Algorithm for Episodic POMDPs
We propose a Posterior Sampling-based reinforcement learning algorithm for POMDPs (PS4POMDPs), which is much simpler and more implementable compared to state-of-the-art optimism-based online learning algorithms for POMDPs.
Optimal Control of Logically Constrained Partially Observable and Multi-Agent Markov Decision Processes
In this paper, we first introduce an optimal control theory for partially observable Markov decision processes (POMDPs) with finite linear temporal logic constraints.
Exact and Cost-Effective Automated Transformation of Neural Network Controllers to Decision Tree Controllers
Over the past decade, neural network (NN)-based controllers have demonstrated remarkable efficacy in a variety of decision-making tasks.
Co-Design of Topology, Scheduling, and Path Planning in Automated Warehouses
Given a list of products, the WSP amounts to finding a plan for a team of agents which brings every product on the list to a station within a given timeframe.
Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation
Constrained Markov decision processes (CMDPs) model scenarios of sequential decision making with multiple objectives that are increasingly important in many applications.
Control Barrier Function Contracts for Vehicular Mission Planning Under Signal Temporal Logic Specifications
The STL predicates are then mapped to an aggregation of contracts associated with continuously differentiable time-varying control barrier functions.
Correct-By-Construction Design of Adaptive Cruise Control with Control Barrier Functions Under Safety and Regulatory Constraints
The safety-critical nature of adaptive cruise control (ACC) systems calls for systematic design procedures, e. g., based on formal methods or control barrier functions (CBFs), to provide strong guarantees of safety and performance under all driving conditions.
Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic Constraints
Autonomous agents often operate in scenarios where the state is partially observed.
ReIGNN: State Register Identification Using Graph Neural Networks for Circuit Reverse Engineering
Reverse engineering an integrated circuit netlist is a powerful tool to help detect malicious logic and counteract design piracy.
Model-Free Reinforcement Learning for Optimal Control of MarkovDecision Processes Under Signal Temporal Logic Specifications
We present a model-free reinforcement learning algorithm to find an optimal policy for a finite-horizon Markov decision process while guaranteeing a desired lower bound on the probability of satisfying a signal temporal logic (STL) specification.
Synthesis of Discounted-Reward Optimal Policies for Markov Decision Processes Under Linear Temporal Logic Specifications
We present a method to find an optimal policy with respect to a reward function for a discounted Markov decision process under general linear temporal logic (LTL) specifications.
Designing Interpretable Approximations to Deep Reinforcement Learning
In an ever expanding set of research and application areas, deep neural networks (DNNs) set the bar for algorithm performance.
A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints
Constrained Markov Decision Processes (CMDPs) formalize sequential decision-making problems whose objective is to minimize a cost function while satisfying constraints on various cost functions.
