Active Learning in RL with Human Feedback

In the realm of RL, the incorporation of human feedback serves as a framework for enhancing the agent’s learning process through domain-specific insights. While conventional RL algorithms strive to maximize reward by interacting with their environment, defining what precisely constitutes a “reward” is often a complex task. Such definitions demand relevant domain knowledge to appropriately specify “good” and “bad” agent behaviors. Moreover, human feedback often plays a critical role in establishing action constraints to ensure safety and fairness. Research in this domain shows significant promise for enhancing the adaptability of RL algorithms across a wide range of real-world applications. However, there is still a lack of comprehensive scientific investigations in this area. As such, one of my forthcoming research objectives is to contribute substantively to the development of this field.