Using Deep Learning to Automate Feature Modeling in Learning by Observation

Learning by observation allows non-technical experts to transfer their skills to an agent by shifting the knowledge-transfer task to the agent. However, for the agent to learn regardless of expert, domain, or observed behavior, it must learn in a general-purpose manner. Existing learning by observation agents allow for domain-independent learning and reasoning but require human intervention to model the agent’s inputs and outputs. We describe Domain-Independent Deep Feature Learning by Observation (DIDFLO), an agent that uses convolutional neural networks to learn without explicitly defining input features. DIDFLO uses the raw visual inputs at two levels of granularity to automatically learn input features using limited training data. We evaluate DIDFLO in scenarios drawn from a simulated soccer domain and provide a comparison to other learning by observation agents in this domain.