In‐Memory Realization of Eligibility Traces Based on Conductance Drift of Phase Change Memory for Energy‐Efficient Reinforcement Learning. Issue 6 (16th December 2021)

• Record Type: Journal Article
• Authors: Lu, Yingming; Li, Xi; Yan, Bonan; Yan, Longhao; Zhang, Teng; Song, Zhitang; Huang, Ru; Yang, Yuchao
• Is Part Of: Advanced materials. Volume 34:Issue 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-16
• Subjects: conductance drift -- eligibility trace -- phase change memory -- reinforcement learning; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202107811
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: Reinforcement learning (RL) has shown outstanding performance in handling complex tasks in recent years. Eligibility trace (ET), a fundamental and important mechanism in reinforcement learning, records critical states with attenuation and guides the update of policy, which plays a crucial role in accelerating the convergence of RL training. However, ET implementation on conventional digital computing hardware is energy hungry and restricted by the memory wall due to massive calculation of exponential decay functions. Here, in‐memory realization of ET for energy‐efficient reinforcement learning with outstanding performance in discrete‐ and continuous‐state RL tasks is demonstrated. For the first time, the inherent conductance drift of phase change memory is exploited as physical decay function to realize in‐memory eligibility trace, demonstrating excellent performance during RL training in various tasks. The spontaneous in‐memory decay computing and storage of policy in the same phase change memory give rise to significantly enhanced energy efficiency compared with traditional graphics processing unit platforms. This work therefore provides a holistic energy and hardware efficient method for both training and inference of reinforcement learning. Abstract : Here, in‐memory realization of eligibility trace for energy‐efficient reinforcement learning (RL) with outstanding performance in discrete‐ and continuous‐state RL tasks is presented. For the first time, the inherent conductance drift of phase change memory is exploited as physical decay function to realize in‐memory eligibility trace, demonstrating excellent performance and significantly enhanced energy efficiency compared with traditional computing platforms.