The paper formalizes hidden Byzantine action overwrites in cooperative multi-agent RL, proves an exact rectangular robust-MDP reduction, shows linear security regret is unavoidable under uninformative attacks, and gives a stage-tied E2D learner with ~O(H^2 S sqrt(AK)) + E[D_K] regret.
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles (UAVs) control in autonomous mobile access applications. For the purpose, a single neural network is utilized in centralized training for cooperation among multiple agents while maximizing the total quality of service (QoS) in mobile access applications.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2026 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks
The paper formalizes hidden Byzantine action overwrites in cooperative multi-agent RL, proves an exact rectangular robust-MDP reduction, shows linear security regret is unavoidable under uninformative attacks, and gives a stage-tied E2D learner with ~O(H^2 S sqrt(AK)) + E[D_K] regret.