Reinforcement learning and multi-agents systems for sustainable urban waste management

Urban waste management in developing countries poses significant challenges and has a wide range of environmental and health impacts. The study proposes an integrated approach that combines multi-agent systems (MAS) and reinforcement learning to optimise urban waste management in these contexts. The main objective is to improve operational efficiency by utilising advanced technologies. To achieve this objective, a multi-agent framework is proposed in which agents interact to monitor the real-time waste collection level. The optimisation focus is on the waste collection time of each truck achieved by implementing the Q-learning algorithm, a reinforcement learning technique. To assess the effectiveness of this approach, a case study was proposed in which two alternative algorithms, a heuristic and a meta-heuristic, were developed and compared with the reinforcement learning algorithm proposed using standardised test datasets. The findings of this study provide valuable insights for decision-makers and stakeholders involved in waste management in developing countries. The integration of reinforcement learning and multi-agent systems offers a 46% improvement rate that significantly improves waste collection and management processes. With the help of advanced technologies, decision makers can adopt innovative and effective strategies to address complex challenges relating to urban waste.