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Radar Anti-jamming Strategy Learning via Domain-knowledge Enhanced Online Convex Optimization
The dynamic competition between radar and jammer systems presents a significant challenge for modern Electronic Warfare (EW), as current active learning approaches still lack sample efficiency and fail to exploit jammer's characteristics.
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games
This work tackles the complexities of multi-player scenarios in \emph{unknown games}, where the primary challenge lies in navigating the uncertainty of the environment through bandit feedback alongside strategic decision-making.
Counterfactual Regret Minimization for Anti-jamming Game of Frequency Agile Radar
Numerical simulations demonstrates the effectiveness of deep CFR algorithm for approximately finding NE and obtaining the best response strategy.
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