In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

The problem of sparse rewards is one of the hardest challenges in contemporary reinforcement learning. Hierarchical reinforcement learning (HRL) tackles this problem by using a set of temporally-extended actions, or options, each of which has its own subgoal. These subgoals are normally handcrafted for specific tasks. Here, though, we introduce a generic class of subgoals with broad applicability in the visual domain. Underlying our approach (in common with work using "auxiliary tasks") is the hypothesis that the ability to control aspects of the environment is an inherently useful skill to have. We incorporate such subgoals in an end-to-end hierarchical reinforcement learning system and test two variants of our algorithm on a number of games from the Atari suite. We highlight the advantage of our approach in one of the hardest games Montezuma's revenge for which the ability to handle sparse rewards is key. Our agent learns several times faster than the current state-of-the-art HRL agent in this game, reaching a similar level of performance.
Feature Control as Intrinsic Motivation for Hierarchical Reinforcement Learning

Recent works have shown that synthetic parallel data automatically generated by translation models can be effective for various neural machine translation (NMT) issues. In this study, we build NMT systems using only synthetic parallel data. As an efficient alternative to real parallel data, we also present a new type of synthetic parallel corpus. The proposed pseudo parallel data are distinct from previous works in that ground truth and synthetic examples are mixed on both sides of sentence pairs. Experiments on Czech-German and French-German translations demonstrate the efficacy of the proposed pseudo parallel corpus, which shows not only enhanced results for bidirectional translation tasks but also substantial improvement with the aid of a ground truth real parallel corpus.
Building a Neural Machine Translation System Using Only Synthetic Parallel Data

Deep learning models are often successfully trained using gradient descent, despite the worst<lb>case hardness of the underlying non-convex optimization problem. The key question is then under<lb>what conditions can one prove that optimization will succeed. Here we provide a strong result<lb>of this kind. We consider a neural net with one hidden layer and a convolutional structure with<lb>no overlap and a ReLU activation function. For this architecture we show that learning is NP-<lb>complete in the general case, but that when the input distribution is Gaussian, gradient descent<lb>converges to the global optimum in polynomial time. To the best of our knowledge, this is the<lb>first global optimality guarantee of gradient descent on a convolutional neural network with ReLU<lb>activations.
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs