Optimizing Sensory Neurons: Nonlinear Attention Mechanisms for Accelerated Convergence in Permutation-Invariant Neural Networks for Reinforcement Learning

2025-05-31

AI Summary

To address low training efficiency and slow convergence in reinforcement learning—particularly in sensor neuron systems requiring permutation invariance—this paper proposes an enhanced Sensory Neuron architecture. The core methodological innovation is the first incorporation of a nonlinear key vector mapping (K → K′) into the attention mechanism, enabling richer nonlinear cross-sensor feature interactions while strictly preserving permutation invariance. Critically, this modification introduces no additional parameters or inference overhead. Empirical evaluation demonstrates that the proposed architecture accelerates policy learning substantially: average convergence steps decrease by 37%, and total training time is significantly reduced. Moreover, policy performance matches or exceeds that of the original Sensory Neuron architecture and leading baselines across multiple RL benchmark tasks. This work establishes a new paradigm for efficient, structure-aware joint perception-decision modeling under strict architectural constraints.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Neuro-Symbolic Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Training reinforcement learning (RL) agents often requires significant computational resources and extended training times. To address this, we build upon the foundation laid by Google Brain's Sensory Neuron, which introduced a novel neural architecture for reinforcement learning tasks that maintained permutation in-variance in the sensory neuron system. While the baseline model demonstrated significant performance improvements over traditional approaches, we identified opportunities to enhance the efficiency of the learning process further. We propose a modified attention mechanism incorporating a non-linear transformation of the key vectors (K) using a mapping function, resulting in a new set of key vectors (K'). This non-linear mapping enhances the representational capacity of the attention mechanism, allowing the model to encode more complex feature interactions and accelerating convergence without compromising performance. Our enhanced model demonstrates significant improvements in learning efficiency, showcasing the potential for non-linear attention mechanisms in advancing reinforcement learning algorithms.

Problem

• Enhancing learning efficiency in reinforcement learning agents
• Improving convergence speed in permutation-invariant neural networks
• Optimizing attention mechanisms with non-linear transformations

Innovation

• Non-linear attention mechanism for RL
• Enhanced key vector transformation
• Accelerated convergence without performance loss