Reinforcement Learning-Based Equipment Combination Selection Optimization for Multi-Layer Kill Webs

Chao Fang

doi:10.1109/tcc.2026.3683729

Reinforcement Learning-Based Equipment Combination Selection Optimization for Multi-Layer Kill Webs

2026-04-14

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DOI：10.1109/tcc.2026.3683729

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Modern network-centric operations increasingly rely on multi-layer Kill Webs (KWs), enabling redundant and non-linear sensing-to-strike pathways while introducing a combinatorial equipment selection problem under uncertainty and resource constraints. This paper formulates a multi-layer KW equipment combination selection as a sequential decision-making problem by explicitly modeling heterogeneous equipment capabilities, resource constraints, and the network topology. To address this problem, we developed an RL learning-based optimization framework, where a multi-objective reward function integrates normalized relevance, operational risk, and timeliness, with a penalty mechanism for infeasible or incomplete kill-chain closure. Based on the jointly captured state information (e.g., network structure, equipment attributes, target characteristics, and resource availability), an Actor-Critic (AC) algorithm is developed to learn adaptive equipment combination selection across different operational stages using temporal-difference advantage estimation and entropy regularization. Simulation results under diverse battlefield scenarios demonstrate that the proposed framework consistently outperforms Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Particle Swarm Optimization (PSO), achieving at least a 19.6% improvement in overall operational effectiveness while maintaining low decision latency.

Keywords:

Kill web (KW)

kill-chain

multi-layer network

equipment combination selection

reinforcement learning

Reinforcement Learning-Based Equipment Combination Selection Optimization for Multi-Layer Kill Webs

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