Reinforcement Learning-Based Soft–Hard Damage Cooperative Task Allocation and Optimization Method for Multi-Laser Systems Against UAV Swarms

To address the dynamic target allocation and resource scheduling problems of multiple High-Energy Laser Systems (HELSs) in defending critical infrastructure against large-scale heterogeneous UAV swarm penetration, this paper proposes a soft–hard damage cooperative task allocation and optimization method, termed Attention-based Centralized Training and Decentralized Execution proximal policy optimization (Attn-CTDE-PPO), which integrates an attention mechanism with multi-agent reinforcement learning. First, a cooperative multi-agent sequential decision-making model for multi-HELS engagement against UAV swarms is constructed by considering continuous laser irradiation, system energy consumption, thermal accumulation limits, and soft–hard damage mechanisms. Second, a multi-head attention set encoder based on a Transformer is introduced to extract global situational features at the state level. This design enables the policy network to handle a time-varying number of targets and mitigates the explosion of hybrid action spaces. At the action level, a discrete–continuous dual-head network with a masking mechanism is proposed to achieve target allocation and laser-parameter scheduling. Furthermore, a multi-dimensional threat assessment module is developed, which can prune infeasible actions in the action space via physical rule-based masking, thereby accelerating the decision-making speed of the method. Monte Carlo simulation experiments verified the defense reliability, energy management efficiency and real-time responsiveness of the proposed method. The experimental results demonstrate that, in heterogeneous swarm scenarios with different numbers of UAVs, the proposed method effectively suppresses UAV penetration, improves the defense success rate, and reduces total system energy consumption compared with baseline algorithms.