In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[Q]: We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems. We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.
[A]: We present planners based on convnets that are sample-efficient and that generalize to larger instances of navigation and pathfinding problems.

[Q]: We introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across linguistic contexts (i.e., to model polysemy). Our word vectors are learned functions of the internal states of a deep bidirectional language model (biLM), which is pretrained on a large text corpus. We show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging NLP problems, including question answering, textual entailment and sentiment analysis. We also present an analysis showing that exposing the deep internals of the pretrained network is crucial, allowing downstream models to mix different types of semi-supervision signals.

[A]: We introduce a new type of deep contextualized word representation that significantly improves the state of the art for a range of challenging NLP tasks.

[Q]: Generative adversarial networks (GANs) train implicit generative models through solving minimax problems. Such minimax problems are known as nonconvex- nonconcave, for which the dynamics of first-order methods are not well understood. In this paper, we consider GANs in the type of the integral probability metrics (IPMs) with the generator represented by an overparametrized neural network. When the discriminator is solved to approximate optimality in each iteration, we prove that stochastic gradient descent on a regularized IPM objective converges globally to a stationary point with a sublinear rate. Moreover, we prove that when the width of the generator network is sufficiently large and the discriminator function class has enough discriminative ability, the obtained stationary point corresponds to a generator that yields a distribution that is close to the distribution of the observed data in terms of the total variation. To the best of our knowledge, we seem to first establish both the global convergence and global optimality of training GANs when the generator is parametrized by a neural network.
[A]:
We establish global convergence to optimality for IPM-based GANs where the generator is an overparametrized neural network.