PhD Colloquium: Teaching Robots With Interactive Reinforcement Learning

10 July 2017

We investigate learning approaches, more specifically interactive reinforcement learning to perform a domestic task. We use parent-like advice to explore two set-ups: agent-agent and human-agent interaction.

This thesis contributes to knowledge in terms of studying the interplay of multi-modal interactive feedback and contextual affordances. Overall, we investigate which parameters influence the interactive reinforcement learning process and show that the apprenticeship of reinforcement learning agents can be sped up by means of interactive parent-like advice, multi-modal feedback, and affordances-driven environmental models.

Monday, 10 July 2017, 14:00, Room D-334, Informatikum

Speaker: Francisco Javier Cruz Naranjo, PhD Candidate at group WTM