A Low Latency Adaptive Coding Spiking Framework for Deep Reinforcement Learning

With the help of Deep Neural Networks, Deep Reinforcement Learning (DRL) has achieved great success on many complex tasks during the past few years. Spiking Neural Networks (SNNs) have been used for the implementation of Deep Neural Networks with superb energy efficiency on dedicated neuromorphic hardware, and recent years have witnessed increasing attention on combining SNNs with Reinforcement Learning, whereas most approaches still work with huge energy consumption and high latency. This work proposes the Adaptive Coding Spiking Framework (ACSF) for SNN-based DRL and achieves low latency and great energy efficiency at the same time. Inspired by classical conditioning in biology, we simulate receptors, central interneurons, and effectors with spike encoders, SNNs, and spike decoders, respectively. We use our proposed ACSF to estimate the value function in reinforcement learning and conduct extensive experiments to verify the effectiveness of our proposed framework.