We provide a straightforward demonstration of the potential of different types of agent-based learning in the context of the Ultimatum Game. A recurring iteration of the Ultimatum Game is explored through Agent-Based Models (ABM), in which agents—representing the players—engage in repeated interactions following predefined rules. Leveraging the capabilities of Machine Learning we aim to harness the agents’ capacity to acquire strategies for optimizing their earnings within this game. Illustrative simplified examples of Fictitious Play, Reinforcement Learning, and Classifier systems are developed in R. The Classifier systems are based on Decision Trees that enable agents to learn from previous interactions and use background knowledge. The prior knowledge is based on the previous results of Reinforcement Learning in two different ways: on one hand, agent decisions are random (emphasizing exploration), and on the other hand, agent decisions are “epsilon-greedy”, emphasizing exploitation. We compare the gains of the agents in the different setups. We then assume that agents are placed in networks to develop a more sophisticated setup and explore the possibilities of Transfer Learning among agents, where some teach others how to learn.