这篇paper提出的主要方法是一种最大化熵的强化学习框架,原本的强化学习的目标是最大化reward的期望值,而这篇paper则是在此基础上增加了最大化熵值准则,这种改进也是基于原有的actor-critic框架下的off-policy方法,所以又称作soft actor-critic方法。 SAC Preliminaries 与DDPG等方法不同,SAC方法将actor-critic训练与...
to equal the critic networks'self.soft_update_target_networks(tau=1.)# Initialise actor networkself.actor_local=Network(input_dimension=self.state_dim,output_dimension=self.action_dim,output_activation=torch.nn.Softmax(dim=1))self.actor_optimiser=torch.optim.Adam(self.actor_local.parameters()...
可以参看2018年发表的《Soft Actor-Critic Algorithms and Applications》。我理解是在其soft value functio...
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一种基于SoftActor-Critic算法的配电网连续无功电压优化方法专利信息由爱企查专利频道提供,一种基于SoftActor-Critic算法的配电网连续无功电压优化方法说明:本发明公开了一种基于Soft...专利查询请上爱企查
本发明的基于Soft Actor‑Critic算法结合多目标零件约束的装配路径规划方法,使用3DS Max建立装配零件和待装配零件的3D模型,将模型转成FBX文件,导入到Unity3D项目中;在Unity3D中使用ML‑Agents模块完成深度强化学习训练场景的搭建;为装配零件添加预定义区域的装配约束和几何定位约束;建立零件智能体多目标决策模型并优化;...
Soft Actor-Critic 快速完成了这两项任务:Minitaur 的移动耗时 2 小时,而根据图像观察进行的阀门转动任务耗时 20 小时。此外,通过将实际的阀门位置作为观察结果提供给该策略,我们还学习了一种无需图像便能完成阀门转动任务的策略。Soft Actor-Critic 在 3 小时内即可学会这种更简单的阀门转动任务。相比之下,使用自然...
reinforcement-learning renewable-energy social-learning demand-management soft-actor-critic Updated Dec 1, 2023 Shell Improve this page Add a description, image, and links to the soft-actor-critic topic page so that developers can more easily learn about it. Curate this topic Add this top...
The soft actor-critic (SAC) algorithm is an off-policy actor-critic method for environments with discrete, continuous, and hybrid action-spaces. The SAC algorithm attempts to learn the stochastic policy that maximizes a combination of the policy value and its entropy. The policy entropy is a me...