Learning from Observation (LfO), also known as Behavioral Cloning, is an
approach for building software agents by recording the behavior of an expert
(human or artificial) and using the recorded data to generate the required
behavior. jLOAF is a platform that uses Case-Based Reasoning to achieve LfO. In
this paper we interface jLOAF with the popular OpenAI Gym environment. Our
experimental results show how our approach can be used to provide a baseline
for comparison in this domain, as well as identify the strengths and weaknesses
when dealing with environmental complexity.