Goal Space

Goal space research focuses on defining and learning effective representations of subgoals for hierarchical reinforcement learning (HRL), enabling agents to solve complex, long-horizon tasks. Current research emphasizes automated goal space generation using techniques like vector quantization, large language models (LLMs) for semantic understanding of goals, and reachability analysis to identify meaningful state groupings. This work is significant because it addresses the limitations of manually defined goal spaces, improving the scalability and data efficiency of HRL, and leading to more robust and interpretable agents capable of tackling increasingly complex real-world problems.