{s}\right)$ towards a few actions or action sequences, since it is easier for the actor and critic to overoptimise to a small portion of the environment. To reduce this problem, entropy regularization adds an entropy term to the loss to promote action diversity:$$H(X) = -\sum\pi\...
Entropy regularized TRPOs and their variants add a proper entropy regularization term [16] to their objectives. This is believed to help with exploration because it encourages the agent to select policies more randomly [37], and hence the agent’s performance improves. 2.2.1. On-policy entropy...
The second term represents the Shannon entropy measure as applied to fuzzy partitions (entropy regularization); then, a given measure of entropy is maximized or, equivalently, the converse of the entropy is minimized. Overall, the total functional depending on both the previous aspects is optimized...
The regularization term balances each player's rationality with its belief about the level of misinformation about the transition model. We consider both entropy-regularized $N$-stage and entropy-regularized discounted stochastic games, and establish the existence of a value in both games. Moreover,...
Short term entropy was calculated on the moving window of either long window ([Math Processing Error]w=1 s) or short window ([Math Processing Error]w=0.5 s) width and 50 ms step, over each trial, for each channel separately. Value at a certain time-point is calculated from a window ...
To avoid overfitting, we establish a regularization term that is formulated as \({\mathscr {L}}_{reg} = \frac{1}{2}\sum _{i=0}^{I}(||W^i||_2^2 + ||\widehat{W^i}||^2_2)\), where \(W^i\) and \(\widehat{W^i}\) indicates the weight of the encoder and decoder ...
In the mentioned machine learning algorithms, parameters such as the learning rate or regularization factor are tuned through cross-validation to achieve optimal results. Similarly, in our EC-GBM model, the hyperparameter \(\epsilon\), which controls the degree of entropy correction, plays a ...
A vector entropy consisting of the second order entropy (Ent-2) and the cross entropy is constructed as the regularization term which incorporates the prior motion knowledge into the estimation process. By imposing the motion constraints, the vector-entropy regularization converts the ill-posed ...
ensemble. The objective function of XGBoost, as shown in Equation5, consists of two components: the training loss function (\(loss({\hat{y}}i, y_i)\)) and the regularization term (\(\sum {k}^{n} \omega (f_k)\)). $$\begin{aligned} F = \sum _{i}^{n} loss({\hat{y}}...
Maximum entropy, as applied to image restoration, can be thought of as a particular case of a more general technique known as regularization. One approach to ill-posed problems, such as image restoration, is to find solutions that are consistent with the data, but which possess other desirable...