# Reinforcement Learning based Embodied Agents Modelling Human Users Through Interaction and Multi-Sensory Perception

## TL;DR

This paper investigates how combining human control and feedback signals enhances reinforcement learning agents' ability to model human users through multi-sensory interaction, improving performance in movement control tasks.

## Contribution

It demonstrates that integrating control and feedback signals, along with temporal feedback decay, significantly improves RL-based embodied agent performance in human-interactive tasks.

## Key findings

- Smearing human feedback over time improves task performance.
- Varying feedback correctness influences agent learning.
- Temporal decay of feedback impacts learning effectiveness.

## Abstract

This paper extends recent work in interactive machine learning (IML) focused on effectively incorporating human feedback. We show how control and feedback signals complement each other in systems which model human reward. We demonstrate that simultaneously incorporating human control and feedback signals can improve interactive robotic systems' performance on a self-mirrored movement control task where an RL-agent controlled right arm attempts to match the preprogrammed movement pattern of the left arm. We illustrate the impact of varying human feedback parameters on task performance by investigating the probability of giving feedback on each time step and the likelihood of given feedback being correct. We further illustrate that varying the temporal decay with which the agent incorporates human feedback has a significant impact on task performance. We found that smearing human feedback over time steps improves performance and we show varying the probability of feedback at each time step, and an increased likelihood of those feedbacks being 'correct' can impact agent performance. We conclude that understanding latent variables in human feedback is crucial for learning algorithms acting in human-machine interaction domains.