Exploring spiking neural networks for deep reinforcement learning in robotic tasks.

Area of Science:

• Artificial Intelligence
• Computational Neuroscience
• Robotics

Background:

• Spiking Neural Networks (SNNs) mimic the mammalian brain, using spiking neurons and Ordinary Differential Equations for dynamic system representation.
• Deep Reinforcement Learning (DRL), particularly Proximal Policy Optimization (PPO), is crucial for robot training due to challenges with offline datasets.
• SNNs offer potential for DRL tasks with temporal complexity by capturing input dynamics.

Purpose of the Study:

• To investigate the effectiveness of SNNs in Deep Reinforcement Learning (DRL) robotic tasks.
• To develop and utilize a novel framework for training SNNs with PPO in the Isaac Gym simulator.
• To compare SNN and Artificial Neural Network (ANN) performance across various network configurations.

Main Methods:

• Developed a novel framework for training SNNs with PPO within the Isaac Gym simulator using the skrl library.
• Conducted extensive exploration of SNN configurations for DRL robotic tasks.
• Compared SNNs and ANNs, analyzing network depth (layers) and width (neurons).

Main Results:

• Identified that optimal SNN topologies for DRL tasks typically have fewer layers compared to ANNs.
• Observed that state-of-the-art SNN architectures struggle to effectively utilize deeper layers in complex RL tasks like Ant.
• Achieved a 4.4x performance improvement on the Ant-v4 benchmark using the identified optimal SNN topology compared to prior SNN methods.

Conclusions:

• SNNs are effective for DRL tasks, with shallower network architectures being optimal.
• The developed Isaac Gym-based framework facilitates efficient SNN configuration and comparison for DRL.
• The findings suggest a significant performance advantage for optimized SNNs over existing SNN approaches in challenging robotic control scenarios.