Human-Allied Relational Reinforcement Learning
This work addresses the challenge of handling both structured and unstructured data in RL, with potential applications in domains requiring human-AI collaboration, though it appears incremental in nature.
The paper tackles the problem of reinforcement learning for structured tasks by combining relational RL with object-centric representations and human guidance, resulting in an effective and efficient approach.
Reinforcement learning (RL) has experienced a second wind in the past decade. While incredibly successful in images and videos, these systems still operate within the realm of propositional tasks ignoring the inherent structure that exists in the problem. Consequently, relational extensions (RRL) have been developed for such structured problems that allow for effective generalization to arbitrary number of objects. However, they inherently make strong assumptions about the problem structure. We introduce a novel framework that combines RRL with object-centric representation to handle both structured and unstructured data. We enhance learning by allowing the system to actively query the human expert for guidance by explicitly modeling the uncertainty over the policy. Our empirical evaluation demonstrates the effectiveness and efficiency of our proposed approach.