Application of Reinforcement Learning in Complex Environmental Decision-making Problems

This paper provides an in-depth discussion on the application of reinforcement learning in decision-making problems in complex environments, focusing on the challenges it faces such as data sparsity and the balance between exploration and utilization, and proposes corresponding solutions. The study realizes an integrated process from data collection to decision support by developing a decision support system for sustainable enterprise management based on big data. Meanwhile, the exploration strategies and optimization algorithms in reinforcement learning are used to improve decision-making efficiency and accuracy. An application case in an intelligent transportation system demonstrates the potential of the multi-intelligence reinforcement learning framework in vehicle collaboration and optimization of traffic flow, effectively alleviating traffic congestion. Experiments show that the system significantly improves decision-making efficiency and accuracy, verifying the effectiveness of reinforcement learning in decision-making in complex environments.