  The energy-efficient control of mobile robots is crucial as the complexity of
their real-world applications increasingly involves high-dimensional
observation and action spaces, which cannot be offset by limited on-board
resources. An emerging non-Von Neumann model of intelligence, where spiking
neural networks (SNNs) are run on neuromorphic processors, is regarded as an
energy-efficient and robust alternative to the state-of-the-art real-time
robotic controllers for low dimensional control tasks. The challenge now for
this new computing paradigm is to scale so that it can keep up with real-world
tasks. To do so, SNNs need to overcome the inherent limitations of their
training, namely the limited ability of their spiking neurons to represent
information and the lack of effective learning algorithms. Here, we propose a
population-coded spiking actor network (PopSAN) trained in conjunction with a
deep critic network using deep reinforcement learning (DRL). The population
coding scheme dramatically increased the representation capacity of the network
and the hybrid learning combined the training advantages of deep networks with
the energy-efficient inference of spiking networks. To show the general
applicability of our approach, we integrated it with a spectrum of both
on-policy and off-policy DRL algorithms. We deployed the trained PopSAN on
Intel's Loihi neuromorphic chip and benchmarked our method against the
mainstream DRL algorithms for continuous control. To allow for a fair
comparison among all methods, we validated them on OpenAI gym tasks. Our
Loihi-run PopSAN consumed 140 times less energy per inference when compared
against the deep actor network on Jetson TX2, and had the same level of
performance. Our results support the efficiency of neuromorphic controllers and
suggest our hybrid RL as an alternative to deep learning, when both
energy-efficiency and robustness are important.
