Year: 2021
Type: article
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.
Authors: Wilkie Olin-Ammentorp, Yury Sokolov, Maxim Bazhenov
Institution: University of California San Diego
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,700
Awards: G205947434, G2515541506, G3904416443
OpenAlex ID: W3135902525