Action Control and Decision Analysis Based on Reinforcement Learning

Motion control and decision analysis play an important role in intelligent robot technology, but there is the problem of inaccurate analysis and positioning. Traditional genetic algorithms cannot solve the problem of control positioning in intelligent robot technology, and the effect is not ideal. Therefore, this paper proposes the positioning and early warning of action control and decision analysis based on reinforcement learning, and analyzes the factor localization and quality early warning. Firstly, Markov's decision theory is used to locate the influencing factors, and the indicators is divided according to the requirements of action control and decision analysis, so as to reduce the interference factors in action control and decision analysis. Then, Markov's decision theory is used to form a reinforcement learning action control and decision analysis scheme, and the results of action control and decision analysis is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, reinforcement learning is superior to traditional genetic algorithms in terms of accuracy of action control and decision analysis, and time of influencing factors of action control and decision analysis.