TASK DEFINITION: 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].
PROBLEM: Important task (attention models), interesting distillation application, well-written paper. The authors have been responsive in updating the paper, adding new experiments, and being balanced in presenting their findings. I support accepting this paper.

SOLUTION: en

PROBLEM: This paper explores the topic of intrinsic motivation in the context of deep RL. It proposes a couple of variants derived from an auxiliary model-learning process (prediction error, surprise and learning progress), and shows that those can help exploration on a number of continuous control tasks (and the Atari game “venture”, maybe).  Novelty: none of the proposed types of intrinsic motivation are novel, and it’s arguable whether the application to deep RL is novel (see e.g. Kompella et al 2012).  Potential: the idea of seeking out states where a transition model is uncertain is sensible, but also limited -- I would encourage the authors to also discuss the limitations. For example in a game like Go the transition model is trivially learned, so this approach would revert to random exploration. So other forms of learning progress or surprise derived from the agent’s competence instead might be more promising in the long run? See also Srivastava et al 2012 for further thoughts.  Computation time: I find the paper’s claimed superiority over VIME to be overblown: the gain seems to stem almost exclusively from a faster initialization, but have very similar per-step cost? So given that VIME is also performing very competitively, what arguments can you advance for your own method(s)?

SOLUTION: en

PROBLEM: El artículo esta adecuadamente escrito, excepto por algunos problemas de acentos (implementara, utilizaran, ...) Las herramientas utilizadas, así como los algoritmos implementados se encuentran adecuadamente descritos. La única crítica esta en la implementación de los mismos, realizada en una única máquina (seguramente por un problema de recursos), algo que quita méritos a lo realizado, considerando que se esta hablando de técnicas pensadas para ser usadas en forma distribuida (donde debería estar su máximo aprovechamiento).  Pese a lo anterior, pienso que es un trabajo bien presentado, valioso en la discusión de las limitaciones de Hadoop vs Giraph, y cuyos resultados son interesantes de conocer y debatir en el ámbito del congreso.

SOLUTION:
es