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.

Input: Consider Input: This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specifically, each agent learns a decentralized control policy based on local observations and messages from connected neighbors. We formulate such a networked MARL (NMARL) problem as a spatiotemporal Markov decision process and introduce a spatial discount factor to stabilize the training of each local agent. Further, we propose a new differentiable communication protocol, called NeurComm, to reduce information loss and non-stationarity in NMARL. Based on experiments in realistic NMARL scenarios of adaptive traffic signal control and cooperative adaptive cruise control, an appropriate spatial discount factor effectively enhances the learning curves of non-communicative MARL algorithms, while NeurComm outperforms existing communication protocols in both learning efficiency and control performance.

Output: This paper proposes a new formulation and a new communication protocol for networked multi-agent control problems


Input: Consider Input: Since deep neural networks are over-parameterized, they can memorize noisy examples. We address such memorizing issue in the presence of annotation noise. From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we hypothesize that noisy examples do not consistently incur small losses on the network under a certain perturbation. Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) that prevents overfitting noisy examples by eliminating them using the consensus of an ensemble of perturbed networks. One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on noisy MNIST, CIFAR-10, and CIFAR-100 in an efficient manner.

Output: This work presents a method of generating and using ensembles effectively to identify noisy examples in the presence of annotation noise. 


Input: Consider Input: Our work offers a new method for domain translation from semantic label maps
 and Computer Graphic (CG) simulation edge map images to photo-realistic im-
 ages. We train a Generative Adversarial Network (GAN) in a conditional way to
 generate a photo-realistic version of a given CG scene. Existing architectures of
 GANs still lack the photo-realism capabilities needed to train DNNs for computer
 vision tasks, we address this issue by embedding edge maps, and training it in an
 adversarial mode. We also offer an extension to our model that uses our GAN
 architecture to create visually appealing and temporally coherent videos.


Output: Simulation to real images translation and video generation