Neuromorphic Computing with Spike-Timing Dependent Plasticity (STDP) for Reinforcement Learning

Abstract

This paper explores the application of neuromorphic computing principles, specifically utilizing Spike-Timing Dependent Plasticity (STDP), for enhancing reinforcement learning (RL) performance. Traditional deep learning approaches often struggle with sparse reward environments, requiring extensive training and substantial computational resources. We propose a novel framework where a reinforcement learning agent is implemented on a neuromorphic platform, leveraging STDP to dynamically adjust synaptic weights based on the precise timing of pre- and post-synaptic spikes. This approach allows the network to learn directly from the temporal structure of the environment, potentially leading to more efficient learning and improved performance in scenarios with limited or delayed rewards. The core claim is that this combination offers a significant advantage over conventional deep learning architectures, particularly in the context of sparse reward reinforcement learning. We detail the theoretical underpinnings of STDP and its relevance to RL, and outline a conceptual architecture for such a system. Future research directions are also discussed, focusing on the practical implementation and scaling of this approach.