Spiking Neural Networks (SNNs) can achieve competitive performance by converting already existing well-trained Artificial Neural Networks (ANNs), avoiding further costly training. This property is particularly attractive in Reinforcement Learning (RL), where training through environment interaction is expensive and potentially unsafe. However, existing conversion methods perform poorly in continuous control, where suitable baselines are largely absent. We identify error amplification as the key cause: small action approximation errors become temporally correlated across decision steps, inducing cumulative state distribution shift and severe performance degradation. To address this issue, we propose Cross-Step Residual Potential Initialization (CRPI), a lightweight gradient-free mechanism that carries over residual membrane potentials across decision steps to suppress temporally correlated errors. Experiments on continuous control benchmarks with both vector and visual observations demonstrate that CRPI can be integrated into existing conversion pipelines and substantially recovers lost performance. Our results highlight continuous control as a critical and challenging benchmark for ANN-to-SNN conversion, where small errors can be strongly amplified and impact performance. Code is available at https://github.com/xuzijie32/ANN2SNN-CRPI.

翻译：脉冲神经网络（SNN）可通过转换已训练好的人工神经网络（ANN）获得有竞争力的性能，从而避免额外的高成本训练。这一特性在强化学习（RL）中尤为吸引人，因为通过环境交互进行训练既昂贵又存在潜在风险。然而，现有转换方法在连续控制任务中表现不佳，且缺乏合适的基准方法。我们发现误差放大是根本原因：微小的动作近似误差在决策步骤间产生时间相关性，导致累积状态分布偏移和严重的性能退化。为解决该问题，我们提出跨步残差电位初始化（CRPI），这是一种轻量级无梯度机制，通过跨决策步骤传递残差膜电位来抑制时间相关误差。在包含向量和视觉观测的连续控制基准实验表明，CRPI可集成至现有转换流程中并显著恢复损失的性能。我们的结果强调连续控制是ANN到SNN转换的关键且具挑战性的基准场景，其中微小误差可能被强烈放大并影响性能。代码开源地址：https://github.com/xuzijie32/ANN2SNN-CRPI