50 citations · 125 across the 14 of their papers we have counts for
7 papers · 1 filter
Control Strategies for COVID-19 Epidemic with Vaccination, Shield Immunity and Quarantine: A Metric Temporal Logic Approach
Zhe Xu, Bo Wu, Ufuk Topcu
Ever since the outbreak of the COVID-19 epidemic, various public health control strategies have been proposed and tested against the coronavirus SARS-CoV-2. We study three specific…
Constrained Active Classification Using Partially Observable Markov Decision Processes
Bo Wu, Niklas Lauffer, Mohamadreza Ahmadi +3
In this work, we study the problem of actively classifying the attributes of dynamical systems characterized as a finite set of Markov decision process (MDP) models. We are interes…
Reward Machines for Cooperative Multi-Agent Reinforcement Learning
Cyrus Neary, Zhe Xu, Bo Wu +1
In cooperative multi-agent reinforcement learning, a collection of agents learns to interact in a shared environment to achieve a common goal. We propose the use of reward machines…
Temporal-Logic-Based Reward Shaping for Continuing Reinforcement Learning Tasks
Yuqian Jiang, Sudarshanan Bharadwaj, Bo Wu +3
In continuing tasks, average-reward reinforcement learning may be a more appropriate problem formulation than the more common discounted reward formulation. As usual, learning an o…
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples
Zhe Xu, Bo Wu, Aditya Ojha +2
Despite the fact that deep reinforcement learning (RL) has surpassed human-level performances in various tasks, it still has several fundamental challenges. First, most RL methods…
Policy Synthesis for Switched Linear Systems with Markov Decision Process Switching
Bo Wu, Murat Cubuktepe, Franck Djeumou +2
We study the synthesis of mode switching protocols for a class of discrete-time switched linear systems in which the mode jumps are governed by Markov decision processes (MDPs). We…
Active Task-Inference-Guided Deep Inverse Reinforcement Learning
Farzan Memarian, Zhe Xu, Bo Wu +2
We consider the problem of reward learning for temporally extended tasks. For reward learning, inverse reinforcement learning (IRL) is a widely used paradigm. Given a Markov decisi…