Rewards in RL encapsulate the desirability or utility associated with the outcomes of an agent's actions. They act as a form of reinforcement, providing feedback to the agent and influencing its decision-making processes. In the Q-learning approach tailored for MOFs, the purpose of rewards is to encourage the agent to learn a policy that maximizes cumulative rewards over time.