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].

[EX Q]: Lo que se propone "es interesante como una aplicación" pero no es un trabajo científico.  Ya no es interesante la aplicación de un Datamart desde el punto de vista de aplicación de herramientas (OLAP) a un problema. Dado que se vuelve un trabajo técnico que se realiza en empresas actualmente, pero que no implica un desafío científico ni de investigación.  Hay errores de diseño en la tabla de hechos.
[EX A]: es

[EX Q]: Desarrollar investigación asociada al tema de visión artificial es interesante de conocer, aún bajo condiciones que no son las óptimas, particularmente en los recursos de hardware.  En la sección de pruebas de luminosidad hay cierto desorden en la presentación de los resultados (por ejemplo, la figura 10. debería titularse Prueba Botella Cloro y no Prueba Caja). Lo que hace difícil su seguimiento. Las referencias bibliográficas podrían ser más actuales y de mejor nivel.
[EX A]: es

[EX Q]: I like the setting presented in this paper but I have several criticism/questions:  (1) What are the failure model of this work? As richness of behaviors get complex, I expected this approach to have issues with the diversity of skills that could be discovered.  (2) Looking at Sec 5.3 -- " let X be a random variable denoting the grid in which the agent is currently situated" -- is the space discretized? And if so why and what happens if it isn't.   (3) Expanding on the first point, does the approach work with more complicated embodiment? Say a 5-link swimmer instead of 2? I think this is important to assess the generality of this approach  (4) Authors claim that "Recently, Heess et al. (2016) have independently proposed to learn a range of skills in a pre-training environment that will be useful for the downstream tasks, which is similar to our framework. However, their pre-training setup requires a set of goals to be specified. In comparison, we use intrinsic rewards as the only signal to the agent during the pre-training phase, the construction of which only requires very minimal domain knowledge."  I don't entirely agree with this. The rewards that this paper proposes are also quite hand-crafted and specific to a seemingly limited set of control tasks.
[EX A]:
en

