Design of Action Deduction and Decision System Based on Deep Reinforcement Learning
A comprehensive strategy optimization mechanism based on deep reinforcement learning is proposed,in order to address the problem of the inability to quickly obtain the optimal strategy for specific action decisions.By quantitatively evaluating the performance of the effectiveness,cost,risk and timeliness of action plans,expert decision-making fusion is conducted through weight allocation and clustering analysis.The maximum cumulative reward mechanism of reinforcement learning is leveraged to evaluate factors such as the success probability of actions and the battle loss ratio of results,which enables fast matching of feature matrices and optimization of the best plan,significantly improving the speed and accuracy of action decision-making and showing important engineering application value.
deep learningaction deductionintelligent assessmentexpert decision-making
万方数据
维普
