将神经科学中的时间尺度不变记忆引入深度强化学习，提升模型跨时序场景的适应能力。
Deep reinforcement learning with time-scale invariant memory
- 引入受神经科学启发的时间尺度不变记忆机制
- 在多种时序任务上显著优于LSTM等传统循环架构
- 适合需要长期依赖建模的复杂时序决策问题
准确估计时间关系对动物和人工智能体都至关重要。认知科学与神经科学揭示了行为与神经层面的时间信用分配规律，其中学习动态的时间尺度不变性是核心原则之一：时间关系的成比例缩放不会改变整体学习效率。本文将一种计算神经科学中的时间尺度不变记忆模型整合进深度强化学习代理中。通过理论分析和实验验证，这类代理能在广泛的时间尺度下稳健学习，而使用常见循环记忆结构（如LSTM）的代理则表现不佳。结果表明，将神经科学与认知科学中的计算原理融入深度神经网络，可增强其对复杂时序动态的适应性，模仿人类学习的核心特性。
原文摘要 · Abstract (English)
The ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale invariance of learning dynamics, observed in behavior and supported by neural data, is one of the key principles that governs animal perception: proportional rescaling of temporal relationships does not alter the overall learning efficiency. Here we integrate a computational neuroscience model of scale invariant memory into deep reinforcement learning (RL) agents. We first provide a theoretical analysis and then demonstrate through experiments that such agents can learn robustly across a wide range of temporal scales, unlike agents built with commonly used recurrent memory architectures such as LSTM. This result illustrates that incorporating computational principles from neuroscience and cognitive science into deep neural networks can enhance adaptability to complex temporal dynamics, mirroring some of the core properties of human learning.
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