Exploring Neuromorphic Computing for Reinforcement Learning: A Survey and Review

This survey explores neuromorphic computing within the context of reinforcement learning (RL), covering developments in neuromorphic hardware and architectures that support RL algorithms. We analyze the integration of RL frameworks with spiking neural networks (SNNs) on neuromorphic hardware platforms such as Loihi, DYNAP-SE, BrainScaleS, and SpiNNaker, examining their approaches to RL implementation through various architectures and learning mechanisms. This paper also discusses key software frameworks that facilitate the development of RL applications on the neuromorphic hardware. While recent studies demonstrate promising results in areas such as robotics, gaming, and autonomous systems, challenges persist in scalability, hardware constraints, and training efficiency. The conclusion addresses critical challenges, such as balancing energy efficiency with robustness, offers insights on potential solutions, and identifies directions for future research in this area.