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…
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…
Strategy Synthesis for Surveillance-Evasion Games with Learning-Enabled Visibility Optimization
Suda Bharadwaj, Louis Ly, Bo Wu +2
This paper studies a two-player game with a quantitative surveillance requirement on an adversarial target moving in a discrete state space and a secondary objective to maximize sh…
The Dirichlet Mechanism for Differential Privacy on the Unit Simplex
Parham Gohari, Bo Wu, Matthew Hale +1
As members of a network share more information with each other and network providers, sensitive data leakage raises privacy concerns. To address this need for a class of problems,…
Controller Synthesis for Multi-Agent Systems With Intermittent Communication: A Metric Temporal Logic Approach
Zhe Xu, Federico M. Zegers, Bo Wu +2
This paper develops a controller synthesis approach for a multi-agent system (MAS) with intermittent communication. We adopt a leader-follower scheme, where a mobile leader with ab…
Joint Inference of Reward Machines and Policies for Reinforcement Learning
Zhe Xu, Ivan Gavran, Yousef Ahmad +4
Incorporating high-level knowledge is an effective way to expedite reinforcement learning (RL), especially for complex tasks with sparse rewards. We investigate an RL problem where…
Control Theory Meets POMDPs: A Hybrid Systems Approach
Mohamadreza Ahmadi, Nils Jansen, Bo Wu +1
Partially observable Markov decision processes (POMDPs) provide a modeling framework for a variety of sequential decision making under uncertainty scenarios in artificial intellige…
Switched Linear Systems Meet Markov Decision Processes: Stability Guaranteed Policy Synthesis
Bo Wu, Murat Cubuktepe, Ufuk Topcu
Switched linear systems are time-varying nonlinear systems whose dynamics switch between different modes, where each mode corresponds to different linear dynamics. They arise natur…
Unpredictable Planning Under Partial Observability
Michael Hibbard, Yagiz Savas, 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…
POMDP Model Learning for Human Robot Collaboration
Wei Zheng, Bo Wu, Hai Lin
Recent years have seen human robot collaboration (HRC) quickly emerged as a hot research area at the intersection of control, robotics, and psychology. While most of the existing w…
Combined Top-Down and Bottom-Up Approaches to Performance-guaranteed Integrated Task and Motion Planning of Cooperative Multi-agent Systems
Rafael Rodrigues da Silva, Bo Wu, Jin Dai +1
We propose a hierarchical design framework to automatically synthesize coordination schemes and control policies for cooperative multi-agent systems to fulfill formal performance r…