A Hybrid Framework Integrating Supervised and Reinforcement Learning for Adaptive Decision-Making in Dynamic Environments

Abstract

In intelligent systems, especially in various types of autonomous systems, robotics, intelligent health care, industrial control and real-time resource management, adaptive decision-making in dynamic environments has become a key challenge. The traditional supervised learning models are effective in dealing with static datasets, but suffer from poor performance in dynamic environments because they lack adaptability. Reinforcement learning is another class of algorithms that learn optimal actions based on feedback from contextual rewards generated by the environment, which can be very difficult to explore and may take a long time to train the agent. This means that an efficient Hybrid learning framework of predictive Intelligence and Adaptive optimization is needed. This research attempts to develop a hybrid solution combining supervised learning and reinforcement learning for adaptive decision-making in dynamic environment. The model first applies supervised learning on past data to discover predictive knowledge and then makes initial decisions. A reinforcement learning agent then optimizes the decisions by tuning the policy based on the rewards received. Dynamic simulation evaluation was performed with the main metrics: prediction accuracy, reward convergence, adaptation efficiency and computational latency. The proposed framework is assessed and the results show that the accuracy of the adaptive decisions, the stability of the convergence and the efficiency of the responses is greatly enhanced over the traditional approaches.