The actions in the Q-learning context refer to the selection of specific training data points. Each action influences the subsequent state and the agent's understanding of the environment. In the described methodology, the actions correspond to choosing data points for training the GP model, thereby refining its predictions. The action as performed by the agent is done through an exploration-exploitation tradeoff, which is a fundamental concept in RL and, specifically, Qlearning. It involves a delicate balance between exploring new actions and exploiting known highreward actions. In this study, exploration entails selecting data points that have not been queried much, allowing the algorithm to gather more information about the data space. Exploitation, on the other hand, involves choosing actions based on the current knowledge to maximize shortterm rewards. The tradeoff is managed through an epsilon-greedy strategy, where the agent occasionally explores new actions (with probability ε) and mostly exploits known high-reward actions.