2 citations · 2 across the 5 of their papers we have counts for
7 papers
Submodular Policy Learning for Distributed Task Allocation in Open Multi-Agent Systems
Jing Liu, Luca Ballotta, Yangyang Yang +3
This paper studies policy learning for distributed task allocation in open multi-agent systems, where agents may join and leave in a time-varying fashion, with submodular stage tea…
Submodular Multi-Agent Policy Learning for Online Distributed Task Allocation in Open Multi-Agent Systems
Jing Liu, Yangyang Yang, Luca Ballotta +3
This paper studies multi-agent reinforcement learning with submodular team utilities for online distributed task allocation. In this setting, each agent selects one action from a l…
Differential Game with Geometric Formulation for Differential Systems Under Bilateral Perception Constraints
Xinyi Zhu, Jiali Wang, Yang Tang +2
This letter employs differential game theory to address the defense problem of a circular target area with perception constraints, involving a single defender and a single attacker…
Reinforcement Learning based Constrained Optimal Control: an Interpretable Reward Design
Jingjie Ni, Fangfei Li, Xin Jin +2
This paper presents an interpretable reward design framework for reinforcement learning based constrained optimal control problems with state and terminal constraints. The problem…
Distributed Task Allocation for Multi-Agent Systems: A Submodular Optimization Approach
Jing Liu, Fangfei Li, Xin Jin +1
This paper addresses dynamic task allocation in resource-constrained multi-agent systems (MASs) with sequentially updated assignments. We develop a submodular maximization framewor…
Computer vision tasks for intelligent aerospace missions: An overview
Huilin Chen, Qiyu Sun, Fangfei Li +1
Computer vision tasks are crucial for aerospace missions as they help spacecraft to understand and interpret the space environment, such as estimating position and orientation, rec…
Reinforcement Learning Based Minimum State-flipped Control for the Reachability of Boolean Control Networks
Jingjie Ni, Yang Tang, Fangfei Li
This paper proposes model-free reinforcement learning methods for minimum-cost state-flipped control in Boolean control networks (BCNs). We tackle two questions: 1) finding the fli…