“One-shot learning" traditionally refers to classifying a single instance using a machine learning model pre-trained on extensive datasets. In contrast, Inductive Logic Programming (ILP) approaches such as Meta-Interpretive Learning (MIL) and Meta Inverse Entailment (MIE), can generate complex logic programs from just a single positive example and minimal background knowledge without prior extensive training. This approach offers a human-centred form of machine learning that is more controllable, reliable, and comprehensible due to its small training data size and the inherent interpretability of logic programs. We use PyGol, a Python-based implementation of Meta Inverse Entailment, and compare its performance with ExpGen-PPO in learning autonomous behaviour. ExpGen-PPO is a state-of-the-art reinforcement learning framework designed to address the challenge of generalisation across diverse tasks through experience diversification and robust policy optimisation. Our experiments focus on two domains: maze-solving and obstacle avoidance for mobile robotics. In both domains, we first train the systems in simplified environments without obstacles and then test their ability to generalise to more complex environments with obstacles. Our results show that PyGol effectively learns generalisable solutions from a single example in both domains, whereas ExpGen-PPO requires more training and significantly more exploration to achieve similar performance.