A dual-memory architecture for reinforcement learning on neuromorphic platforms
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 09, 2021
- Source ID
- 10.1088/2634-4386/ac1a64
Entities
People
- Maxim Bazhenov
- Wilkie Olin-Ammentorp
- Yury Sokolov
Organizations
- Defense Advanced Research Projects Agency
- Intel Corporation