Interactively shaping agents via human reinforcement: the TAMER framework

As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without numerous high-cost learning trials. One promising approach to reducing sample complexity of learning a task is knowledge transfer from humans to agents. Ideally, methods of transfer should be accessible to anyone with task knowledge, regardless of that person’s expertise in programming and AI. This paper focuses on allowing a human trainer to interactively shape an agent’s policy via reinforcement signals. Specifically, the paper introduces “Training an Agent Manually via Evaluative Reinforcement,” or tamer, a framework that enables such shaping. Differing from previous approaches to interactive shaping, a tamer agent models the human’s reinforcement and exploits its model by choosing actions expected to be most highly reinforced. Results from two domains demonstrate that lay users can train tamer ...

• Type: Conference
• Year: 2009
• Where: KCAP
• Authors: W. Bradley Knox, Peter Stone
• Keywords: Computational Learning Agents, Information Retrieval, KCAP 2009, Sample Complexity, Tamer Agents