Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
This work addresses energy efficiency for neuromorphic control in mobile devices, though it is incremental as it builds on existing SNN and policy gradient methods.
The paper tackled the problem of using Spiking Neural Networks (SNNs) as stochastic policies in reinforcement learning to reduce energy consumption, demonstrating that online-trained SNNs can trade off energy (measured by spike count) and control performance, with significant gains over converting offline-trained ANNs to SNNs.
Artificial Neural Networks (ANNs) are currently being used as function approximators in many state-of-the-art Reinforcement Learning (RL) algorithms. Spiking Neural Networks (SNNs) have been shown to drastically reduce the energy consumption of ANNs by encoding information in sparse temporal binary spike streams, hence emulating the communication mechanism of biological neurons. Due to their low energy consumption, SNNs are considered to be important candidates as co-processors to be implemented in mobile devices. In this work, the use of SNNs as stochastic policies is explored under an energy-efficient first-to-spike action rule, whereby the action taken by the RL agent is determined by the occurrence of the first spike among the output neurons. A policy gradient-based algorithm is derived considering a Generalized Linear Model (GLM) for spiking neurons. Experimental results demonstrate the capability of online trained SNNs as stochastic policies to gracefully trade energy consumption, as measured by the number of spikes, and control performance. Significant gains are shown as compared to the standard approach of converting an offline trained ANN into an SNN.