Hierarchical Imitation Learning (HIL) addresses long-horizon decision-making by breaking tasks into sub-goals, but it faces challenges like limited supervisory labels and the need for extensive expert demonstrations. LLMs, such as GPT-4, offer promising improvements due to their semantic understanding, reasoning, and ability to interpret language instructions. By integrating LLMs, decision-making agents can enhance sub-goal learning. However, existing approaches still need help with dynamic task updates and require high-level plans that depend on low-level policy agents. This raises the question of whether pre-trained LLMs can autonomously define task hierarchies and effectively guide both sub-goal and agent learning.
Imitation Learning (IL) consists of Behavioral Cloning (BC) and Inverse Reinforcement Learning (IRL). BC uses pre-collected expert data for offline learning but faces issues with compounding errors when encountering unseen states. Conversely, IRL involves interacting with the environment to infer the expert’s reward function, but it’s more resource-intensive. HIL enhances IL by decomposing tasks into sub-goals. LLMs are also used to break down complex tasks into high-level plans, assisting both sub-goal identification and low-level action learning, though they still rely on low-level planners for execution.
Researchers from the University of Alberta and a prominent institution in Hong Kong specializing in science and technology have developed SEAL, a new hierarchical imitation learning framework that utilizes LLMs for generating semantically meaningful sub-goals and pre-labeling states without needing prior knowledge of task hierarchies. SEAL features a dual-encoder system, combining LLM-guided supervised learning with unsupervised Vector Quantization (VQ) for robust sub-goal representation. It also includes a transition-augmented low-level planner to manage sub-goal transitions effectively. Experiments show that SEAL surpasses existing HIL methods, particularly in complex tasks with limited expert datasets.
SEAL introduces a method for HIL that uses pre-trained LLMs to generate sub-goal labels, replacing expensive human annotations. SEAL extracts high-level sub-goal plans from task instructions and maps states in expert demonstrations to these sub-goals. A dual-encoder approach combines supervised LLM-generated labels and unsupervised vector quantization (VQ) for robust sub-goal learning. Additionally, the model enhances low-level policy training by emphasizing transitions between sub-goals. The SEAL framework continuously adapts its high-level sub-goal encoders and low-level policies to improve decision-making and overall task performance.
The study evaluates the SEAL model’s effectiveness on two long-horizon compositional tasks, KeyDoor and Grid-World. It compares it against various baseline methods, including non-hierarchical, unsupervised, and supervised hierarchical imitation learning. The KeyDoor task is simpler, featuring a 10×10 grid where the player must obtain a key to unlock a door. In contrast, Grid-World requires the collection of objects in a predetermined order. The findings indicate that SEAL consistently surpasses most baseline models, primarily due to its dual-encoder architecture, which enhances the achievement of sub-goals and smooth transitions, even in complex scenarios involving multiple sub-goals.
In conclusion, SEAL is an innovative HIL framework that utilizes LLMs’ semantic and world knowledge to create meaningful sub-goal representations without needing prior task hierarchy knowledge. SEAL surpasses several baseline methods, including BC, LISA, SDIL, and TC, particularly in complex long-horizon tasks with limited expert demonstrations. Its dual-encoder architecture enhances robustness compared to a standard LLM encoder, and the transition-augmented low-level planner aids in managing sub-goal transitions effectively. While SEAL shows great promise, it still faces challenges with training stability and aims for improved efficiency in partially observed states.
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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.
