A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning
This work addresses latency and versatility issues in spiking reinforcement learning for applications requiring low-power, event-driven AI, though it appears incremental as it builds on existing SRL methods with adaptive coding.
The paper tackled the problem of high latency and poor versatility in spiking reinforcement learning by introducing a low latency adaptive coding framework using learnable matrix multiplication for encoding and decoding spikes, achieving ultra-low latency as low as 0.8% of other methods and up to 5x energy efficiency compared to DNNs.
In recent years, spiking neural networks (SNNs) have been used in reinforcement learning (RL) due to their low power consumption and event-driven features. However, spiking reinforcement learning (SRL), which suffers from fixed coding methods, still faces the problems of high latency and poor versatility. In this paper, we use learnable matrix multiplication to encode and decode spikes, improving the flexibility of the coders and thus reducing latency. Meanwhile, we train the SNNs using the direct training method and use two different structures for online and offline RL algorithms, which gives our model a wider range of applications. Extensive experiments have revealed that our method achieves optimal performance with ultra-low latency (as low as 0.8% of other SRL methods) and excellent energy efficiency (up to 5X the DNNs) in different algorithms and different environments.