Advice Conformance Verification by Reinforcement Learning agents for Human-in-the-Loop
This addresses the need for transparency in human-in-the-loop reinforcement learning systems, particularly in domains with large action spaces and sparse rewards, though it appears incremental as it builds on existing advice accommodation methods.
The paper tackles the problem of verifying how much human advice reinforcement learning agents conform to, proposing a Tree-based lingua-franca called Preference Tree to communicate this to humans, and shows through experiments in MuJoCo's Humanoid environment and a human-user study with 20 participants that the method provides interpretable assurances.
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.