Transformer Driven Multi-Agent Reinforcement Learning Framework for Integrated Waste Classification Forecasting and Adaptive Routing

The rapid expansion of urban populations has intensified the challenges associated with municipal solid waste management, particularly where conventional static or ad-hoc routing strategies create operational inefficiencies, excessive fuel usage, and repeated bin overflow. Many existing systems still treat waste classification, fill-level forecasting, and routing as separate processes, which restricts coordinated optimization and limits broader sustainability outcomes. To address these shortcomings, TMORL is introduced as a Transformer-enhanced Multi-Agent Reinforcement Learning framework that unifies perception, prediction, and decision-making for intelligent waste management. The framework integrates IoT-enabled sensor measurements with deep learning and MARL-driven optimization to manage waste collection adaptively under real-time uncertainty. A Vision Transformer supports precise waste image classification through global spatial feature extraction, while a Temporal Fusion Transformer generates accurate, uncertainty-aware multi-horizon fill-level forecasts. These model outputs collectively shape the state representation for a multi-objective MARL module that optimizes fuel consumption, travel duration, emission reduction, and overflow mitigation, enabling simultaneous operational and sustainability improvements. TMORL is implemented in PyTorch and evaluated using the Smart Waste Management Dataset containing heterogeneous IoT bin measurements and annotated waste images. The model achieves strong perception accuracy, reporting 97.3% precision, 96.6% recall, and 98.4% mAP@0.5, while the TFT forecasts align closely with real bin-fill patterns to support proactive routing adjustments. When compared with static scheduling and Ant Colony Optimization routing, TMORL reduces fuel usage by 22%, collection time by 25%, and overflow incidents by 95%. Overall, the findings confirm that a transformer-driven, IoT-integrated MARL framework significantly strengthens efficiency, decision responsiveness, and environmental sustainability in next-generation smart waste management systems.

@article{Patel2025,
  title     = {Transformer Driven Multi-Agent Reinforcement Learning Framework for Integrated Waste Classification Forecasting and Adaptive Routing},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Ritesh Patel and Igamberdiyev Asqar Kimsanovich and Vinod Waiker and Elangovan Muniyandy and Swarna Mahesh Naidu and Nurilla Mahamatov and Osama R.Shahin},
  doi       = {10.14569/IJACSA.2025.0161174},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161174}
}