Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop

TL;DR

This paper introduces a method for reinforcement learning agents to verify and communicate how well they conform to human advice, enhancing transparency and trust in human-in-the-loop systems.

Contribution

It proposes a novel Advice-Conformance Verification framework using a Preference Tree to interpret and communicate advice adherence in RL agents.

Findings

The method effectively indicates advice conformity in MuJoCo's Humanoid environment.

It provides interpretable explanations of advice usage.

A human study validates the approach's usefulness.

Abstract

Human-in-the-loop (HiL) reinforcement learning is gaining traction in domains with large action and state spaces, and sparse rewards by allowing the agent to take advice from HiL. Beyond advice accommodation, a sequential decision-making agent must be able to express the extent to which it was able to utilize the human advice. Subsequently, the agent should provide a means for the HiL to inspect parts of advice that it had to reject in favor of the overall environment objective. We introduce the problem of Advice-Conformance Verification which requires reinforcement learning (RL) agents to provide assurances to the human in the loop regarding how much of their advice is being conformed to. We then propose a Tree-based lingua-franca to support this communication, called a Preference Tree. We study two cases of good and bad advice scenarios in MuJoCo's Humanoid environment. Through our experiments, we show that our method can provide an interpretable means of solving the Advice-Conformance Verification problem by conveying whether or not the agent is using the human's advice. Finally, we present a human-user study with 20 participants that validates our method.