Reinforcement Learning with Spiking Neural Networks for Robotic Applications: A Survey

Abstract

Spiking Neural Networks (SNNs) have long been positioned as a biologically plausible and energy-efficient alternative to conventional deep learning models. Reinforcement Learning (RL), on the other hand, provides a framework for autonomous decision-making based on environmental interaction. While the convergence of SNNs and RL holds significant promise for robotics, this pathway remains in its infancy. This survey examines existing spiking RL approaches, categorizing them into bio-inspired models driven by reward-modulated local plasticity (e.g., R-STDP) and gradient-based models adapted from deep RL techniques. Additionally, we review neuron models, encoding schemes, and learning strategies, with emphasis on robotic applications. We further analyze evaluation metrics, emerging trends, limitations, and proposed future directions to bridge the gap between biological plausibility and scalable robotic intelligence. To the best of our knowledge, this work represents the first comprehensive survey specifically dedicated to RL with SNNs in robotic systems.