In this paper, the real-time cooperative guidance strategy for the aircraft trying to evade an incoming attacking missile was developed. The aircraft performs evasive maneuvers, launching a defender missile to divert the missile. Instead of classical strategies, which are based on optimal control or differential game, the engagement was formulated as a multi-agent game. The problem was solved by using the deep reinforcement learning method. To address the sparse reward problem, a general reward design method was presented utilizing the shaping technique. Guidance law, reward function, and training approach were demonstrated through the learning process and simulations. The application of the non-sparse reward function accelerated the model convergence. Considering a standard optimal guidance law as a benchmark, the effectiveness and advantages (guarantees of the aircraft's escape and win rates in multi-agent game) of the proposed guidance strategy were validated by the simulation results. Compared to the standard optimal guidance law, the proposed guidance strategy performed better with less prior knowledge.
Real-time Guidance Strategy for Active Defense Aircraft via Deep Reinforcement Learning
2021-08-16
1231536 byte
Conference paper
Electronic Resource
English
High-dynamic intelligent maneuvering guidance strategy via deep reinforcement learning
SAGE Publications | 2023
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