Key result
Human-in-the-loop reinforcement learning improves system stability versus traditional Q-learning while maintaining similar average scores.
Why the study?
Augmented intelligence and human-in-the-loop reinforcement learning are still in their infancy, prompting the exploration of concept designs that integrate human intelligence into reinforcement learning decision making.
Answers are grounded in this paper and cite their sources.
Integrating human intelligence into reinforcement learning algorithms can improve system stability while maintaining convergence and performance in simulated environments.
Augmented intelligence (AuI) is a concept that combines human intelligence (HI) and artificial intelligence (AI) to leverage their respective strengths. While AI typically aims to replace humans, AuI integrates humans into machines, recognizing their irreplaceable role. Meanwhile, human-in-the-loop reinforcement learning (HITL-RL) is a semisupervised algorithm that integrates humans into the traditional reinforcement learning (RL) algorithm, enabling autonomous agents to gather inputs from both humans and environments, learn, and select optimal actions across various environments. Both AuI and HITL-RL are still in their infancy. Based on AuI, we propose and investigate three separate concept designs for HITL-RL: HI-AI, AI-HI, and parallel-HI-and-AI approaches, each differing in the order of HI and AI involvement in decision making. The literature on AuI and HITL-RL offers insights into integrating HI into existing concept designs. A preliminary study in an Atari game offers insights for future research directions. Simulation results show that human involvement maintains RL convergence and improves system stability, while achieving approximately similar average scores to traditional <tex-math notation="LaTeX">Q</tex-math>-learning in the game. Future research directions are proposed to encourage further investigation in this area.
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Yau et al. (2024) reported a review. Human-in-the-loop reinforcement learning (HITL-RL) vs. Traditional Q-learning was evaluated on RL convergence, system stability, and average scores. Human involvement in reinforcement learning maintained convergence and improved system stability while achieving approximately similar average scores to traditional Q-learning in an Atari game.
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