Equivariant Reinforcement Learning for Quadrotor UAV

Autor:	Yu, Beomyeol, Lee, Taeyoung
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Mathematics - Optimization and Control
Druh dokumentu:	Working Paper
Popis:	This paper presents an equivariant reinforcement learning framework for quadrotor unmanned aerial vehicles. Successful training of reinforcement learning often requires numerous interactions with the environments, which hinders its applicability especially when the available computational resources are limited, or when there is no reliable simulation model. We identified an equivariance property of the quadrotor dynamics such that the dimension of the state required in the training is reduced by one, thereby improving the sampling efficiency of reinforcement learning substantially. This is illustrated by numerical examples with popular reinforcement learning techniques of TD3 and SAC. Comment: 8 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.01233 Zobrazit plný text záznamu View this record from Arxiv