In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].
One example: Este articulo no es un articulo de investigacion, ya que solo muestra como programar un robot mediante la herramienta de logica difusa. Este tema ya ha sido propuesto como solucion en navegacion de robots.
Solution is here: es
Explanation: This review is written in spanish. Hence, the label is 'es'.

Now, solve this: This paper considers the problem of model-based policy search. The authors consider the use of Bayesian Neural Networks to learn a model of the environment and advocate for the $lpha$-divergence minimization rather than the more usual variational Bayes. The ability of alpha-divergence to capture bi-modality however comes at a price and most of the paper is devoted to finding tractable approximations. The authors therefore use the approach of Hernandez-Lobato et al. (2016) as proxy to the alpha-divergence . The environment/system dynamics is clearly defined as a well as the policy parametrization (section 3) and would constitute a useful reference point for other researchers. Simulated roll-outs, using the learned model, then provide samples of the expected return. Since a model of the environment is available, stochastic gradient descent can be performed in the usual way, without policy gradient estimators, via automatic differentiation tools. The experiments demonstrate that alpha-divergence is capable of capturing multi-model structure which competing methods (variational Bayes and GP) would otherwise struggle with. The proposed approach also compares favorably in a real-world batch setting. The paper is well-written, technically rich and combines many recent tools into a coherent algorithm. However, the repeated use of approximations to original quantities seems to somehow defeat the benefits of the original problem formulation. The scalability and computational effectiveness of this approach is also questionable and I am uncertain if many problem would warrant such complexity in their solution. As with other Bayesian methods, the proposed approach would probably shine in low-samples regime and in this case might be preferable to other methods in the same class (VB, GP).
Solution:
en