论文标题
四肢无人机的模棱两可的增强学习
Equivariant Reinforcement Learning for Quadrotor UAV
论文作者
论文摘要
本文为四摩托无人机的高度加固学习框架提供了。对增强学习的成功培训通常需要与环境进行大量相互作用，这阻碍了其适用性，尤其是在可用的计算资源有限的情况下，或者在没有可靠的仿真模型时。我们确定了四型动力学的均衡性能，以便将训练中所需的状态的维度降低，从而提高了增强学习的采样效率。通过流行的TD3和SAC的流行增强学习技术的数值示例来说明这一点。
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.
