Toward Energy-Efficient Spike-Based Deep Reinforcement Learning With Temporal Coding

Deep reinforcement learning (DRL) facilitates efficient interaction with complex environments by enabling continuous optimization strategies and providing agents with autonomous learning abilities. However, traditional DRL methods often require large-scale neural networks and extensive computational resources, which limits their applicability in power-sensitive and resource-constrained edge environments, such as mobile robots and drones. To overcome these limitations, we leverage the energy-efficient properties of brain-inspired spiking neural networks (SNNs) to develop a novel spike-based DRL framework, referred to as Spike-DRL. Unlike traditional SNN-based reinforcement learning methods, Spike-DRL incorporates the energy-efficient time-to-first-spike (TTFS) encoding scheme, where information is encoded through the precise timing of a single spike. This TTFS-based method allows Spike-DRL to work in a sparse, event-driven manner, significantly reducing energy consumption. In addition, to improve the deployment capability of Spike-DRL in resource-constrained environments, a lightweight strategy for quantizing synaptic weights into low-bit representations is introduced, significantly reducing memory usage and computational complexity. Extensive experiments have been conducted to evaluate the performance of the proposed Spike-DRL, and the results show that our method achieves competitive performance with higher energy efficiency and lower memory requirements. This work presents a biologically inspired model that is well suited for real-time decision-making and autonomous learning in power-sensitive and resource-limited edge environments.