Artificial versus spiking neural networks for reinforcement learning in UAV obstacle avoidance

Spiking Neural Networks (SNN) are gaining more interest from the scientific community thanks to the promise of greater energy-efficient and greater computational power. This poses several challenges as today's SNN training for RL is based on Artificial Neural Network (ANN) training and then conversion from ANN to SNN, which does not leverage SNN event-based processing inherent capabilities. The present work compares an ANN and an SNN in an event-camera-based obstacle avoidance task, trained with Reinforcement Learning (RL) using the Deep Q-Learning (DQL) algorithm. We create an experimental setup composed of Unreal Engine 4, AirSim, and an event camera that simulates a real-world obstacle avoidance environment. Additionally, we train an SNN with a gradient-based training method enabling the use of all their expressiveness even in the training phase, showing comparable performance between the ANN and the SNN. To the best of our knowledge, we are the first that implements an entire realistic pipeline with a photo-realistic simulator (Airsim) and train an SNN without converting it from a pre-trained ANN.