IoT-Enabled Deep Reinforcement Learning for Adaptive Waste Management in Hospital Environments

Abstract

Healthcare industry produces considerable amount of biomedical waste, which needs efficient and safe ways of handling. The research paper puts forward a scheme of IoT-enabled DRL for hospital waste management. Unlike previously known schemes, the suggested scheme incorporates IoT-based sensing capabilities with a Proximal Policy Optimization agent. The performance of the framework is analyzed by developing a custom simulation environment based on the OpenAI Gym library, wherein the process of waste production is modeled as stochastic. According to the experimental results, the suggested scheme of PPO surpasses its competitors in all key criteria. Statistical validation using ANOVA and t-tests confirms that the improvements are significant. The findings highlight the potential of IoT-DRL integration for intelligent, adaptive, and efficient hospital waste management systems.