# Sample-efficient Deep Reinforcement Learning with Imaginary Rollouts for Human-Robot Interaction

## TL;DR

This paper introduces a method that uses learned environment models to generate synthetic data, significantly speeding up deep reinforcement learning for human-robot interaction tasks involving stochastic environments.

## Contribution

It presents an architecture for online environment model learning and synthetic data generation to enhance sample efficiency in deep reinforcement learning for robots.

## Key findings

- Faster learning with less environment interaction
- Effective use of synthetic transitions for policy improvement
- Successful application to a human-gesture-based robotic task

## Abstract

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 policy. In this paper, we present an architecture that allows agents to learn models of stochastic environments and use them to accelerate learning. We descirbe how an environment model can be learned online and used to generate synthetic transitions, as well as how an agent can leverage these synthetic data to accelerate learning. We validate our approach using an experiment in which a robotic arm has to complete a task composed of a series of actions based on human gestures. Results show that our approach leads to significantly faster learning, requiring much less interaction with the environment. Furthermore, we demonstrate how learned models can be used by a robot to produce optimal plans in real world applications.