DSAC-T通过期望值替代、双值分布学习,以及基于方差的梯度调整,解决了标准DSAC存在的学习不稳定性和对奖励尺度敏感的问题。这些关键改进为DSAC-T算法的性能提升奠定了基础。 Experiment Conclusion 结论部分: 本文提出了DSAC-T算法,通过三个重要的改进解决了标准DSAC算法存在的学习不稳定性和对奖励尺度敏感的问题。这...
通过这一项改进,DSAC-T减少了对人工调参的需要,使算法更具通用性。 (3)Twin value distribution learning: 在DSAC-T中通过学习两个独立的值分布网络参数,在计算梯度时选择其中均值较小的一个,并以此作为critic更新时的目标分布。这样可以引入适度的低估偏差,并进一步抑制过估计问题,获得更加稳定的学习过程。 实验...
智能驾驶课题组(iDLab)提出一种可减少过估计的Distributional Soft Actor-Critic(DSAC)算法,通过学习连续状态-动作回报分布(state-action return distribution)来动态调节Q值的更新过程,并证明引入该分布降低过估计的原理。本文基于异步并行计算框架PABAL来实施DSAC算法,Mujoco环境的验证表明:相比于目前最流行的强化学习算法...
Distributional Soft Actor-Critic with Three Refinements (DSAC-T) Requires Windows 7 or greater or Linux. Python 3.8. The installation path must be in English. Installation #Please make sure not to include Chinese characters in the installation path, as it may result in a failed execution.#clon...
Distributional Soft Actor-Critic (DSAC) Requires Windows 7 or greater or Linux. Python 3.8. The installation path must be in English. Installation #Please make sure not to include Chinese characters in the installation path, as it may result in a failed execution.#clone DSAC_v1 repositorygit...
We would like to thank all members in Intelligent Driving Laboratory (iDLab), School of Vehicle and Mobility, Tsinghua University for making excellent contributions and providing helpful advices for DSAC-T. About DSAC-v2; DSAC-T; DASC; Distributional Soft Actor-Critic Resources Readme Activity...