Language-guided Skill Learning with Temporal Variational Inference (2402.16354v2)

Abstract: We present an algorithm for skill discovery from expert demonstrations. The algorithm first utilizes LLMs to propose an initial segmentation of the trajectories. Following that, a hierarchical variational inference framework incorporates the LLM-generated segmentation information to discover reusable skills by merging trajectory segments. To further control the trade-off between compression and reusability, we introduce a novel auxiliary objective based on the Minimum Description Length principle that helps guide this skill discovery process. Our results demonstrate that agents equipped with our method are able to discover skills that help accelerate learning and outperform baseline skill learning approaches on new long-horizon tasks in BabyAI, a grid world navigation environment, as well as ALFRED, a household simulation environment.

• The paper introduces a framework that uses LLMs to generate fine-grained trajectory segments and hierarchical temporal variational inference to convert them into reusable skills.
• It demonstrates enhanced learning efficiency and superior task performance compared to baseline methods in environments like BabyAI and ALFRED.
• By incorporating the MDL principle, the approach balances data compression and skill adaptability, offering scalable insights for AI-driven tasks.

An Expert Analysis of "Language-guided Skill Learning with Temporal Variational Inference"

The paper "Language-guided Skill Learning with Temporal Variational Inference" presents a novel algorithm aimed at skill discovery using expert demonstrations. This paper's primary focus revolves around leveraging LLMs for initial trajectory segmentation, subsequently incorporating a hierarchical variational inference framework to refine and merge these segments into reusable skills. Below, I provide an in-depth analysis of the methodology, results, and potential implications of this research within the field of AI and skill learning.

Overview of the Methodology

The authors introduce a unique approach that initially employs LLMs to propose a preliminary segmentation of trajectories from expert demonstrations. This initial step is crucial as it reduces the complexities inherent in trajectory segmentation, which traditionally involves a vast search space that grows exponentially with an increased horizon length. The significance of using LLMs lies in their ability to generate fine-grained, semantically meaningful segments that are subsequently enhanced through a temporal variational inference framework.

The core of the proposed framework lies in its ability to merge these initial granular segments into coherent skills by employing a hierarchical variational inference strategy. It uses the Minimum Description Length (MDL) principle as an auxiliary objective to effectively guide the balance between compression and skill reusability. This framework is designed to discover semantically meaningful skills from given trajectories, considering the inherent trade-off between describing trajectories concisely and maintaining skill adaptability.

Key Findings and Results

The paper presents empirical results demonstrating that the proposed method outperforms existing baseline approaches across diverse domains, notably in BabyAI—a grid world navigation environment—and ALFRED—a challenging household simulation environment. These results are backed by extensive simulations showing improved learning efficiency and superior task performance in long-horizon tasks compared to baseline methods.

In comparison, methods like LOVE and LISA, which either lack language assistance or do not employ a strong variational inference framework, were less effective in discovering and leveraging reusable skills for complex task execution. Moreover, distinct numerical results illustrated that the introduction of the MDL-based auxiliary objective aided in achieving a more optimal balance between skill generalization and data compression, further boosting task performance and learning efficiency.

Implications and Speculative Future Directions

The theoretical and practical contributions of this work have several implications for the field of reinforcement learning and artificial intelligence at large. By optimally utilizing LLMs to generate initial segments, the proposed method capitalizes on the rich semantic embedding capabilities of these models, pointing towards a more informative and robust approach in skill learning paradigms. The incorporation of the MDL principle in skill learning can transform how researchers approach the balance between expressiveness and succinctness in skill definitions.

Future directions may explore how LLMs can be utilized not just in segmentation but as integral components in more complex decision-making workflows, where semantics and contextual understanding are pivotal. Further studies could explore scaling this approach to more dynamic environments and seeking integration with other forms of learning, such as unsupervised or self-supervised learning paradigms.

In conclusion, this paper's methodological novelty and empirical validation demonstrate its contributions toward advancing skill learning techniques. Its ability to foster more efficient learning and task performance opens new avenues for research both in academic explorations and practical applications in AI-driven tasks. The proposed method stands as a testament to the potential embedded in integrating language-based insights with sophisticated variational approaches to skill learning.