1)引进Expected value substituting提高了学习过程的稳定性和效率; 2)引进Variance-based critic gradient adjusting减少了对人工调参的需要,使算法更具通用性; 3)引进Twin value distribution learning进一步抑制过估计问题。 我们提出了一款SOTA的model-free算法,有效地抑制了RL过估计并实现性能的大幅度提升。我们的工作可...
:Distributional Soft Actor-Criticwith Three Refinements Jingliang Duan, Wenxuan Wang, Liming Xiao, Jiaxin Gao, and Shengbo Eben Li∗ 清华大学 Ieee Computational Intelligence Magazin 计算机科学2区SCI I Introduction: 强化学习近年来在复杂决策和控制任务中取得了巨大成功,结合神经网络等高容量函数近似器取得了...
2021年,清华大学智能驾驶课题组(iDLab)在IEEE TNNLS期刊发表值分布强化学习算法DSAC(Distributional Soft Actor-Critic),通过学习连续状态-动作回报分布来动态调节Q值的更新过程从而减少其过估计(https://mp.weixin.qq.com/s/p2ZzTYjh5IpnlvW29qzXxQ)。经过四年的更新迭代,iDLab提出全新算法DSAC-T(Distributional ...
This paper proposes a reinforcement-learning-based decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (Shielded DSAC). The Shielded DSAC adopts the policy evaluation with safety considerations in offline training, and ...
Distributional Soft Actor-Critic (DSAC) 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...
Distributional Soft Actor-Critic (DSAC) 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...
近年来,强化学习在围棋、游戏等领域的应用取得巨大成功。然而,现有算法在学习过程中值函数的近似误差会造成严重的过估计问题,导致策略性能极大地降低。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...
In this paper, we present a new RL algorithm named Distributional Soft Actor Critic (DSAC), combining distributional RL and maximum entropy RL together. Taking the randomness both in action and discounted return into consideration, DSAC over performs the state-of-the-art baselines with more ...
Distributional stochastic Planner‐Actor‐Critic for deformable image registrationdoi:10.1002/ima.23109Du, JianingChang, QingLian, LiInternational Journal of Imaging Systems & Technology
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 reposit...