Structured Representation Learning with Locally Linear Embeddings and Adaptive Feature Fusion

2026-06-16

AI Summary

This work addresses the limitations of existing reinforcement learning methods, which often fail to explicitly model the local geometric structure of the state space and struggle to effectively disentangle and adaptively integrate dynamic information with reward signals. Inspired by neuroscience, the paper proposes a novel framework that leverages Locally Linear Embedding (LLE) to capture the local manifold structure of states, combines it with standard reinforcement learning objectives to extract reward-relevant features, and introduces a cortex-inspired gating attention mechanism to adaptively fuse these two representations based on the current state. By integrating LLE with adaptive feature fusion for the first time, the approach emulates the brain’s mechanisms for information segregation and integration, enabling structured representation learning. Empirical results across multiple benchmark tasks demonstrate significant improvements in both sample efficiency and performance, validating the efficacy of modeling local state geometry and dynamically selecting features.

Abstract

Neuroscientific research has revealed that the brain encodes complex behaviors by leveraging structured, low-dimensional manifolds and dynamically fusing multiple sources of information through adaptive gating mechanisms. Inspired by these principles, we propose a novel reinforcement learning (RL) framework that encourages the disentanglement of dynamics-specific and reward-specific features, drawing direct parallels to how neural circuits separate and integrate information for efficient decision-making. Our approach leverages locally linear embeddings (LLEs) to capture the intrinsic, locally linear structure inherent in many environments, mirroring the local smoothness observed in neural population activity, while concurrently deriving reward-specific features through the standard RL objective. An attention mechanism, analogous to cortical gating, adaptively fuses these complementary representations on a per-state basis. Experimental results on benchmark tasks demonstrate that our method, grounded in neuroscientific principles, improves learning efficiency and overall performance compared to conventional RL approaches, highlighting the benefits of explicitly modeling local state structures and adaptive feature selection as observed in biological systems.

Problem

• structured representation learning
• locally linear embeddings
• adaptive feature fusion
• reinforcement learning
• neuroscientific principles

Innovation

• locally linear embeddings
• adaptive feature fusion
• disentangled representation
• neuroscience-inspired RL
• attention gating