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Air combat autonomous maneuver decision for one-on-one within visual range engagement base on robust multi-agent reinforcement learning | IEEE Conference Publication | IEEE Xplore

Air combat autonomous maneuver decision for one-on-one within visual range engagement base on robust multi-agent reinforcement learning


Abstract:

Based on a robust multi-agent reinforcement learning (MARL) algorithm framework, an autonomous maneuver decision-making algorithm for UCAV air combat in one-on-one combat...Show More

Abstract:

Based on a robust multi-agent reinforcement learning (MARL) algorithm framework, an autonomous maneuver decision-making algorithm for UCAV air combat in one-on-one combat in the visible range is designed and implemented. This algorithm can solve the problem that the single agent reinforcement learning algorithm cannot converge during the training process due to the unstable environment. At the same time, considering the shortcomings of the MADDPG algorithm in a strong competitive environment, it is easy to obtain a very fragile strategy, which is only targeted at a specific equilibrium strategy. In this paper, a minimax module is introduced to obtain the expected perturbation, which can locally approach the worst-case perturbation through the gradient. Through simulation tests of algorithm convergence and policy quality, the algorithm is found to be effective.
Date of Conference: 09-11 October 2020
Date Added to IEEE Xplore: 30 November 2020
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Conference Location: Singapore

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