Towards Competence in Autonomous Agents

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 20

Doctoral Consortium

My thesis aims to contribute towards building autonomous agents that are able to develop competency over their environment -- agents that are able to achieve mastery over their domain and are able to solve new problems as they arise using the knowledge and skills they acquired in the past. I propose a number of methods for building competence in autonomous agents using the reinforcement learning framework, a computational approach to learning from interaction. These methods allow an agent to autonomously develop a set of skills (closed-loop policies over lower-level actions) that allows the agent to interact effectively with its environment and flexibly deal with new tasks.