New algorithms to train robots for army

Researchers at the US Army Research Laboratory and the University of Texas at Austin have developed new algorithms for robots to learn to perform tasks effectively by interacting with a human instructor. The findings of the study were presented at the Association for the Advancement of Artificial Intelligence Conference in New Orleans, Louisiana. The researchers considered a specific case where a human provides real-time feedback in the form of critique. First introduced by collaborator Peter Stone from the University of Texas at Austin as TAMER, or Training an Agent Manually via Evaluative Reinforcement, the team developed a new algorithm called Deep TAMER. The research is an extension of TAMER that uses deep learning — a class of machine learning algorithms that are loosely inspired by the brain to provide a robot the ability to learn how to perform tasks by viewing video streams in a short amount of time with a human trainer. According to army researcher Garrett Warnell, the team considered situations where a human teaches an agent how to behave by observing it and providing critique.