Flight Vehicle Control Law Self-Learning Approach Based on Lightweight Search of Neural Network Architecture
In the context of utilizing soft actor-critic(SAC)reinforcement learning algorithms to realize self-learning in complex flight vehicle control laws,a significant challenge lies in the heavy reliance on manual expertise for hyperparameter tuning,which considerably increases design complexity.A flight vehicle control law self-learning method based on neural network architecture lightweight search strategy is proposed to address this issue.On the basis of transforming the neural network architecture design problem into a graph topology generation problem,this method combines the graph topology generation algorithm of LSTM recurrent neural network,the lightweight training and evaluation mechanism of deep reinforcement learning parameters based on weight sharing,and the strategy gradient based graph topology generator parameter learning algorithm to provide a lightweight automatic search framework for deep reinforcement learning,which automatically optimizes the hyperparameters of the neural network architecture in the SAC training algorithm and completes self-learning of the control law.Taking the three-dimensional space return landing control as an example,the effectiveness and practicality of the proposed method are verified.
Flight vehicleControl law self-learningAutomated machine learningNetwork architecture searchSAC reinforcement learning
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