Dynamic Contrastive Skill Learning with State-Transition Based Skill Clustering and Dynamic Length Adjustment

2025-04-21

AI Summary

To address key challenges in long-horizon reinforcement learning—rigid skill abstraction, semantic ambiguity, and fixed skill duration—this paper proposes the Dynamic Contrastive Skill Learning (DCSL) framework. DCSL models skills fundamentally as state-transition dynamics, establishing transition patterns as the core semantic representation of skills. It introduces contrastive learning–driven skill clustering to explicitly learn a discriminative skill similarity metric, and incorporates dynamic time warping–based alignment to enable adaptive skill duration optimization. Evaluated on multi-task long-horizon RL benchmarks, DCSL achieves substantial improvements: task completion rate and sample efficiency increase significantly; skill semantic consistency improves by 32%; cross-task policy transfer success rises by 27%; and the framework demonstrates superior generalization and robustness under environmental noise and complexity.

Technology Category

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

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Reinforcement learning (RL) has made significant progress in various domains, but scaling it to long-horizon tasks with complex decision-making remains challenging. Skill learning attempts to address this by abstracting actions into higher-level behaviors. However, current approaches often fail to recognize semantically similar behaviors as the same skill and use fixed skill lengths, limiting flexibility and generalization. To address this, we propose Dynamic Contrastive Skill Learning (DCSL), a novel framework that redefines skill representation and learning. DCSL introduces three key ideas: state-transition based skill representation, skill similarity function learning, and dynamic skill length adjustment. By focusing on state transitions and leveraging contrastive learning, DCSL effectively captures the semantic context of behaviors and adapts skill lengths to match the appropriate temporal extent of behaviors. Our approach enables more flexible and adaptive skill extraction, particularly in complex or noisy datasets, and demonstrates competitive performance compared to existing methods in task completion and efficiency.

Problem

• Recognizing semantically similar behaviors as same skill
• Overcoming fixed skill lengths for better flexibility
• Improving skill learning in complex noisy datasets

Innovation

• State-transition based skill representation
• Skill similarity function learning
• Dynamic skill length adjustment