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.

通过 Adaptive Coding Spiking Framework（ACSF）结合 Deep Reinforcement Learning 和 Spiking Neural Networks，实现了低延迟和高能效，并在强化学习中使用 ACSF 来估计值函数，并进行广泛的实验证明了该框架的有效性。