Constraint learning is a research area covering different methodologies for an inductive construction of constraint theories from data. In many contexts, a system can benefit from constraint learning to optimize the process in terms of time and performance. In this thesis, trajectories data of a trained reinforcement learning agent are used as examples to define constraints characterizing the environment. The methodology involves three main steps. In the first phase an agent is trained in a two-dimensional space containing unsafe areas. Then, the agent is tested and its trajectories are stored and used as data for learning a formula that classifies the region of the training examples as positive, thereby identifying safe and unsafe areas.