Journal
ADVANCED MATERIALS
Volume 34, Issue 6, Pages -Publisher
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202107811
Keywords
conductance drift; eligibility trace; phase change memory; reinforcement learning
Categories
Funding
- National Key R&D Program of China [2017YFA0207600]
- National Natural Science Foundation of China [61925401, 92064004, 61927901, 91964204]
- 111 Project [B18001]
- PKU-Baidu Fund [2019BD002, 2020BD010]
- Fok Ying-Tong Education Foundation
- Tencent Foundation through the XPLORER Prize, Strategic Priority Research Program of the Chinese Academy of Sciences [XDB44010200]
- Science & Technology Council of Shanghai [19JC1416801]
- Shanghai Research and Innovation Functional Program [17DZ2260900]
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This study demonstrates an energy-efficient method for implementing reinforcement learning eligibility trace in memory, utilizing phase change memory for spontaneous memory decay computing and storage, significantly enhancing energy efficiency compared to traditional graphics processing unit platforms.
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.
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