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

Understand

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.