A dual-memory architecture for reinforcement learning on neuromorphic platforms. Issue 2 (9th September 2021)

• Record Type: Journal Article
• Authors: Olin-Ammentorp, Wilkie; Sokolov, Yury; Bazhenov, Maxim
• Is Part Of: Neuromorphic computing and engineering. Volume 1:Issue 2(2021)
• Publication Date: 2021-09-09
• Subjects: reinforcement learning -- neuromorphic hardware -- program architecture; Neural networks (Computer science) -- Periodicals; Neural computers -- Periodicals; Neuromorphics -- Periodicals; 006.3
• DOI: 10.1088/2634-4386/ac1a64
• Languages: English
• ISSNs: 2634-4386

Abstract

Abstract: Reinforcement learning (RL) is a foundation of learning in biological systems and provides a framework to address numerous challenges with real-world artificial intelligence applications. Efficient implementations of RL techniques could allow for agents deployed in edge-use cases to gain novel abilities, such as improved navigation, understanding complex situations and critical decision making. Toward this goal, we describe a flexible architecture to carry out RL on neuromorphic platforms. This architecture was implemented using an Intel neuromorphic processor and demonstrated solving a variety of tasks using spiking dynamics. Our study proposes a usable solution for real-world RL applications and demonstrates applicability of the neuromorphic platforms for RL problems.