50 citations · 125 across the 14 of their papers we have counts for
19 papers
Entropy Maximization for Partially Observable Markov Decision Processes
Yagiz Savas, Michael Hibbard, Bo Wu +2
We study the problem of synthesizing a controller that maximizes the entropy of a partially observable Markov decision process (POMDP) subject to a constraint on the expected total…
Controller Synthesis for Multi-Agent Systems with Intermittent Communication and Metric Temporal Logic Specifications
Zhe Xu, Federico M. Zegers, Bo Wu +3
This paper investigates the controller synthesis problem for a multi-agent system (MAS) with intermittent communication. We adopt a relay-explorer scheme, where a mobile relay agen…
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners
Parham Gohari, Bo Chen, Bo Wu +2
Kickstarting deep reinforcement learning algorithms facilitate a teacher-student relationship among the agents and allow for a well-performing teacher to share demonstrations with…
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…