Structure Detection for Contextual Reinforcement Learning

2026-01-13

AI Summary

This work addresses the challenges of negative transfer and high computational cost in contextual reinforcement learning arising from structural heterogeneity across tasks. The authors propose the SD-MBTL framework, which introduces—for the first time—an online structure detection mechanism to dynamically identify the underlying generalization structure (e.g., Mountain structure) of contextual Markov decision processes (CMDPs). Based on this detected structure, the framework adaptively switches between Gaussian process regression and clustering algorithms to enable intelligent source task selection and zero-shot transfer. This approach yields M/GP-MBTL, the first model-based transfer learning framework capable of perceiving contextual structure and dynamically adjusting its strategy accordingly. Evaluated on synthetic data and multiple CRL benchmarks—including continuous control, traffic management, and agricultural management—the method achieves a 12.49% improvement in overall performance over the current state-of-the-art.

Technology Category

• Machine Learning: Structured Learning
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Contextual Reinforcement Learning (CRL) tackles the problem of solving a set of related Contextual Markov Decision Processes (CMDPs) that vary across different context variables. Traditional approaches--independent training and multi-task learning--struggle with either excessive computational costs or negative transfer. A recently proposed multi-policy approach, Model-Based Transfer Learning (MBTL), has demonstrated effectiveness by strategically selecting a few tasks to train and zero-shot transfer. However, CMDPs encompass a wide range of problems, exhibiting structural properties that vary from problem to problem. As such, different task selection strategies are suitable for different CMDPs. In this work, we introduce Structure Detection MBTL (SD-MBTL), a generic framework that dynamically identifies the underlying generalization structure of CMDP and selects an appropriate MBTL algorithm. For instance, we observe Mountain structure in which generalization performance degrades from the training performance of the target task as the context difference increases. We thus propose M/GP-MBTL, which detects the structure and adaptively switches between a Gaussian Process-based approach and a clustering-based approach. Extensive experiments on synthetic data and CRL benchmarks--covering continuous control, traffic control, and agricultural management--show that M/GP-MBTL surpasses the strongest prior method by 12.49% on the aggregated metric. These results highlight the promise of online structure detection for guiding source task selection in complex CRL environments.

Problem

• Contextual Reinforcement Learning
• Contextual Markov Decision Processes
• Structure Detection
• Task Selection
• Transfer Learning

Innovation

• Structure Detection
• Contextual Reinforcement Learning
• Model-Based Transfer Learning
• Gaussian Process
• Task Selection