… call Critic Weighted Policy (CWP) that uses the learned critic to improve results at test time. …
Our implementation of ABM is thus equipped with distributional critics and can use recurrent …

… critic regularization, under some assumptions (see Fig. 1). The key idea is that, when using a
certain TD loss, the regularized critic … Q-values can be learned via regression, we will use a …

… critic regularization, under some assumptions (see Fig. 1). The key idea is that, when using a
certain TD loss, the regularized critic … Q-values can be learned via regression, we will use a …

… angle regression to solve the constructed 1-regularization problem. In [… regularized
actor and critic also have the same NNZs in actor parameters because we set both of 1-regularization …

… can be approximately understood from the perspective of kernel regression optimization,
rapidly reaching its best approximation in ∼ 1/λ time (up to a log factor), see Theorem 4.6 (and …

… -critic method by generalizing the critic objective and connecting it to a behavior-regularized …
EMaQ [15] defines a policy using an autoregressive behavioral cloning model, using the …

… In the perspective of regularization, this initial coefficient can realize 2-regularization (also
mentioned as ridge regression). Thus, the objective function is combined by a mean-square-…

… To avoid such issues, we propose to regularize the learning objective of the actor by … of the
critic. This improves stability by avoiding large steps in the actor update whenever the critic is …

… $L^{2}$ regularization in training a neural network Stein critic so as to distinguish
between … of regularization over training time, which leverages the advantages of highly-regularized …

… regularized regression, as with supervised machine-learning methods more generally, is
prediction. Regularized regression … While regularized regression may select the true model as …