Equivariant Reinforcement Learning for Quadrotor UAV
This addresses sample efficiency for quadrotor UAV control, but it is incremental as it applies known equivariance concepts to a specific domain.
The paper tackles the problem of high sample complexity in reinforcement learning for quadrotor UAVs by identifying an equivariance property that reduces state dimension, improving sampling efficiency as demonstrated with TD3 and SAC in numerical examples.
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.