2020/02/23 by Chad Peters, Babak Esfandiari, Peters, Chad +5
Computer Science · #AI-based Problem Solving and Planning #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Reinforcement Learning in Robotics #Spreadsheets and End-User Computing #cs.AI #cs.LG
paper · pdf · doi:10.48550/arxiv.2002.11197
arxiv created 2020/02/23 · openalex publication_date 2020/02/23 · arxiv updated 2020/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.