The second is hybrid optimization using entropy as a regularizer, which is the main method of this paper and it is more advantageous in high-dimensional problems. 3.1 Entropy-based re-optimization sparse PCE As mentioned at the end of Section 2.3, the aim of this paper is to learn a ...
Optimal Scheduling of Entropy Regularizer for Continuous-Time Linear-Quadratic Reinforcement Learningdoi:10.1137/22M1515744continuous-time reinforcement learninglinear-quadraticentropy regularizationexploratory controlproximal policy updateregret analysisThis work uses the entropy-regularized relaxed stochastic control ...
entropy regularizer (β) tuning To speed up the training procedures for all three methods, we set the horizon length to 500 for two discrete action environments and to 200 for six continuous action environments. 6.3. Hyper-parameter tuning procedure TRPO has one hyper-parameter (δ), ERO-TRPO...
其次,尽管由于 entropy regularizer 的存在,我们的目标函数是unbounded 的,这对证明收敛性会带来很大的困难,但是作者们发现,每一步的 estimation 方差都是有限的,这就能够帮助作者们完成对于参数收敛到stationary point (梯度为0) 和 policy收敛到 global optimal policy 这两件事的证明。另外,不同于现有的文章,本文...
Minimum entropy regularizers have been used in other contexts to encode learnability priors. Input-Dependent Regularization When the model is regularized (e.g. with weight decay), the conditional entropy is prevented from being too small close to the decision surface. This will favor putting the...
This paper presents new tools for understanding the optimization landscape, shows that policy entropy serves as a regularizer, and highlights... 更多下载PDF 翻译3引用 0收藏此文献 0 推荐 分享 全文 参考文献(0) 引证文献(3) 本文作者 关注 Zafarali Ahmed Google Computer science | Artificial ...
Zheng J, Lu C, Yu H, Wang W, Chen S (2018) Iterative reconstrained low-rank representation via weighted nonconvex regularizer. IEEE Access 6:51693–51707. https://doi.org/10.1109/ACCESS.2018.2870371 Article Google Scholar Yang M, Zhang L, Yang J, Zhang D (2013) Regularized robust codin...
From merging shape 0 with other shapes. for '{{node AddN}} = AddN[N=2, T=DT_FLOAT](sigmoid_focal_crossentropy/weighted_loss/Mul, d1_7/kernel/Regularizer/add)' with input shapes: [?], []. The full stack trace is too long and would be appended at the tail. ...
hidden_units=10000l2_sparsity=5e-7l1_sparsity=1e-8mod=Sequential([Dense(hidden_units,input_shape=(1000,),activation="relu",kernel_regularizer=l1_l2(l1=l1_sparsity,l2=l2_sparsity),),Dense(hidden_units,activation="relu",kernel_regularizer=l1_l2(l1=l1_sparsity,l2=l2_sparsity),),Dense(1000,...
在具体的应用中跟定义稍有不同。主要差别是参数的设置，在torch.nn.MSELoss中有一个reduction参数。redu...