### Adaptive Learning in ABMS  
There is a growing body of literature on adaptive learning in ABMS that holds that habits and past experiences heavily influence people's social behavior [9; 11; 12]. Recent developments in ABMS have demonstrated the potential of adaptive learning mechanisms, such as Reinforcement Learning, to significantly enhance the modeling and simulation of epidemic scenarios [11]. For example, [PERSON] et al. [51] developed a psychology-based framework to model human emotions during disaster evacuation. Their study provides new insights into mapping emotions to membership functions so that agents act according to a probabilistic algorithm that combines personal and emotional data. In another study, [PERSON] et al. [9] integrated Reinforcement Learning methods with a multi-agent system designed to simulate city fires. This research sought to create an adaptive mechanism that allows a single firefighter to learn strategies to keep people safe. Precisely, their study employed various well-known RL algorithms, including Temporal Difference, SARSA, and Q-learning. In the context of epidemic modeling, [PERSON] et al. [52] developed a framework called Pandemic Control decision-making via large-scale ABMS and deep Reinforcement learning (PaCAR). It utilizes large-scale agent-based simulation and reinforcement learning to find optimal control policies that minimize infection spread and government restrictions simultaneously. It includes a realistic simulator for cities or states with vaccine settings and a reinforcement learning architecture with a reward system based on economic benefit. This framework outperforms existing methods and is adaptable to different pandemic variants like Alpha and Delta in COVID-19. Similarly, [PERSON] and [PERSON] [53] presented a reinforcement learning framework for COVID-19 resource allocation. The approach involves creating an agent-based epidemic environment to simulate transmission dynamics across multiple states. A multi-agent reinforcement learning algorithm is then developed, taking into account the time-varying characteristics of the environment. The study applies this framework to determine optimal lockdown resource allocation strategies considering factors such as population age distribution and economic conditions. Results demonstrated that this approach enables more flexible resource allocation strategies, aiding decision-makers in optimizing the deployment of limited resources for infection prevention during the COVID-19 pandemic. [PERSON] et al. [54] proposed a novel agent-based pandemic simulator that, unlike traditional models, is able to model fine-grained interactions among people at specific locations in a community. Unlike traditional models, they utilized an RL-based methodology for optimizing fine-grained mitigation policies within this simulator. Furthermore, [PERSON] et al. [55] employed machine learning in an agent-based model to propose a response and recovery approach for contamination events in water distribution systems. [PERSON] and [PERSON] [56] also proposed an agent-based model coupled to a hydraulic simulation where the decision-making of the individual agents is based on a fuzzy logic system reacting to a contamination event in a water network. In a different context, [PERSON] et al. [57] used a conceptual agent-based model to simulate interactions between a group of agents and a governing agent. They included six Temporal Difference Reinforcement Learning algorithms used by the governing agent to influence the group of agents to perform an action that benefits the governing agent. Their research investigates the emergence of new social norms within an agent framework, using recognition and good reputation as incentives for agent cooperation, even without penalties. They employ ABMS to explore norm development. This demonstrates the benefits of using incentives for agent behavior and integrating adaptive learning techniques into ABMS. In addition, in the context of ABMS for epidemic scenarios, the study by [PERSON] et al.