DARL

摘要

Reinforcement Learning (RL) is a powerful technology to solve decisionmaking problems such as robotics control. Modern RL algorithms, i.e., Deep Q-Learning, are based on costly and resource hungry deep neural networks. This motivates us to deploy alternative models for powering RL agents on edge devices. Recently, brain-inspired Hyper-Dimensional Computing (HDC) has been introduced as a promising solution for lightweight and efficient machine learning, particularly for classification.