Human-Machine Coadaptation Based on Reinforcement Learning with Policy Gradients

Abstract

The problem of adaptive human-machine interaction is investigated. It is sought that not only the human learns how to perform a task with a novel machine, but the machine itself co-adapts to the human style in the interaction. This requires solving the problem of two agents co-adapting or co-learning at the same time. Due to the lack of human learning and performance models, it is hypothesized that reinforcement learning with policy gradient algorithms are good candidates for addressing this problem with robustness and fast convergence.