## 3 Results
### Adaptive Learning Integration with ABMS  
In this subsection, we go into the specifics of the adaptive mechanism we have employed. It is important to recall that our aim with this mechanism is to facilitate agents in learning social distancing behaviors while preserving the normalcy of campus life. We operate under the assumption that individuals within the community are rational and prioritize avoiding infection. This inherent drive motivates them to adapt their behaviors to minimize exposure to potential risks willingly. It is important to note that in our scenario, agents act solely based on their personal experiences and not due to external influences. Our chosen learning approach is grounded in a Q-learning scheme with \(\epsilon\)-greedy action selection. We opted for this strategy due to its adaptability, versatility, and ease of implementation. Among the various tabular methods we explored, Q-learning offers a straightforward method for estimating state-action values and is well-suited for navigating a dynamic environment comprising thousands of knowledgeable agents vying for limited resources. Each agent operates independently, mirroring the concurrent adaptation of a small-sized community within the simulation. Figure 4: Model’s visualization. There are four fundamental components in our proposed design. Firstly, we have the representation, which defines what states, actions and values signify. Secondly, initialization outlines how the scenarios are set up initially. Thirdly, action selection elucidates the process of choosing the next action in the current state. Lastly, value update elucidates how estimations are revised based on the reward signal. Regarding representation, states correspond to specific locations on campus, with each state representing a distinct available location to visit. Actions, on the other hand, symbolize the act of moving from one location to another. The value associated with a state-action pair indicates the desirability of selecting a particular destination while currently situated in another place. Consequently, the value function is modeled as a lookup table comprising \(Q(s,a)\) entries, where's' represents the state and 'a' denotes an available action. Only the following amenities are considered in the learning process: teaching facilities, common areas, and eating places. It is evident that there is a vast amount of \(Q\) values as locations are taken in pairs. On the subject of initialization, preliminary experiments suggested the best setup was leaving all initial figures at zero, thus implying that all places have the same attractiveness factor in the first iteration. The referred situation does not match the classical scheme that recommends a random initialization procedure. An \(\epsilon\)-greedy strategy was implemented in the matter of action selection. The idea behind this procedure is that actions should be picked considering the exploration-exploitation dilemma. To be exact, the best available action, referring to the one with the highest \(Q\) figure, is selected with probability \(1-\epsilon\), while a random one is chosen in the opposite case. By way of explanation, the examined method is sketched in Algorithm 4. Regarding the Q values' update, a reward signal was picked to outcome positive figures for safe places and negative amounts for sites that exceed the ideal social distancing measure (as described in Table 4). The proposed payment for landing in a certain location is described by Equation (7), where the social distancing is measured in meters, and the current density of the place is estimated as the number of people in that location over its superficial area in square meters. As an illustration, if the social distancing policy is set to 2 m, then densities over the 0.5 \(\frac{people}{m^{2}}\) mark will be considered threatening.