We present a reactive agent that successfully learns action models in a continuous and dynamic environment. The TRAIL agent uses teleo-reactive trees to integrate planning, reactive execution, and performance improvement through action model learning. This paper discusses the diiculties of action model learning in the face of irrelevant features, durative actions, and stochastic action eeects, ...