2019
Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction
IROS 2019poster
Deep reinforcement learning has proven to be a great success in allowing agents to learn complex tasks. However, its application to actual robots can be prohibitively expensive. Furthermore, the unpredictability of human behavior in human-robot interaction tasks can hinder convergence to a good poli…