Hierarchical Policy

Hierarchical policies in reinforcement learning aim to improve efficiency and generalization by decomposing complex tasks into simpler subtasks managed by separate policies. Current research focuses on developing effective architectures for this decomposition, including those using large language models, graph neural networks, and flow-based representations for low-level policies, often incorporating techniques like multi-objective optimization and adversarial inverse reinforcement learning. This approach shows promise in enhancing performance on challenging problems, particularly in domains requiring long-horizon planning and adaptation to new tasks, with applications ranging from robotics and resource management to natural language processing.