7 papers
Distributed Non-Uniform Scaling Control of Multi-Agent Formation with Dynamic Agent Joining
Tao He, Gangshan Jing
Non-uniform scaling control of formation enables multi-agent systems to adjust their shape by scaling with different ratios along different coordinate axes, offering enhanced flexi…
Node bipartition for rigidity and localization of networks with heterogeneous sensing
Yongjie Liu, Gangshan Jing, Long Wang
Graph rigidity theory is an important tool for examining the solvability of sensor network localization (SNL) problems, and ensuring global convergence of localization algorithms.…
Signed Angle Rigid Graphs for Network Localization and Formation Control
Jinpeng Huang, Gangshan Jing
Graph rigidity theory studies the capability of a graph embedded in the Euclidean space to constrain its global geometric shape via local constraints among nodes and edges, and has…
Action Dependency Graphs for Globally Optimal Coordinated Reinforcement Learning
Jianglin Ding, Jingcheng Tang, Gangshan Jing
Action-dependent individual policies, which incorporate both environmental states and the actions of other agents in decision-making, have emerged as a promising paradigm for achie…
Asynchronous Distributed Reinforcement Learning for LQR Control via Zeroth-Order Block Coordinate Descent
Gangshan Jing, He Bai, Jemin George +2
Recently introduced distributed zeroth-order optimization (ZOO) algorithms have shown their utility in distributed reinforcement learning (RL). Unfortunately, in the gradient estim…
Distributed Multi-Agent Reinforcement Learning Based on Graph-Induced Local Value Functions
Gangshan Jing, He Bai, Jemin George +2
Achieving distributed reinforcement learning (RL) for large-scale cooperative multi-agent systems (MASs) is challenging because: (i) each agent has access to only limited informati…