Neural Network Compression for Reinforcement Learning Tasks

In the last decade, neural networks (NNs) have driven significant progress across various fields, notably in deep reinforcement learning, highlighted by studies like [1, 2, 3]. This progress has the potential to make changes in many areas such as embedded devices, IoT and Robotics. Although modern Deep Learning models have demonstrated impressive gains in accuracy, their large sizes pose limits to their practical use in many real-world applications [4]. These applications may impose requirements in energy consumption, inference latency, inference throughput, memory footprint, real-time inference and hardware costs. Numerous studies have attempted to make neural networks more efficient.