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.
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Question: Most of the prior work on multi-agent reinforcement learning (MARL) achieves optimal collaboration by directly learning a policy for each agent to maximize a common reward. In this paper, we aim to address this from a different angle. In particular, we consider scenarios where there are self-interested agents (i.e., worker agents) which have their own minds (preferences, intentions, skills, etc.) and can not be dictated to perform tasks they do not want to do. For achieving optimal coordination among these agents, we train a super agent (i.e., the manager) to manage them by first inferring their minds based on both current and past observations and then initiating contracts to assign suitable tasks to workers and promise to reward them with corresponding bonuses so that they will agree to work together. The objective of the manager is to maximize the overall productivity as well as minimize payments made to the workers for ad-hoc worker teaming. To train the manager, we propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL), which consists of agent modeling and policy learning. We have evaluated our approach in two environments, Resource Collection and Crafting, to simulate multi-agent management problems with various task settings and multiple designs for the worker agents. The experimental results have validated the effectiveness of our approach in modeling worker agents' minds online, and in achieving optimal ad-hoc teaming with good generalization and fast adaptation.

Answer: We propose Mind-aware Multi-agent Management Reinforcement Learning (M^3RL) for training a manager to motivate self-interested workers to achieve optimal collaboration by assigning suitable contracts to them.


Question: We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation.   This characterization also leads to an algorithm for projecting a convolutional layer onto an operator-norm ball. We show that this is an effective regularizer;  for example, it improves the test error of a deep residual network using batch normalization on CIFAR-10 from 6.2% to 5.3%.

Answer: We characterize the singular values of the linear transformation associated with a standard 2D multi-channel convolutional layer, enabling their efficient computation. 


Question: We present a personalized recommender system using neural network for recommending
 products, such as eBooks, audio-books, Mobile Apps, Video and Music.
 It produces recommendations based on customer’s implicit feedback history such
 as purchases, listens or watches. Our key contribution is to formulate recommendation
 problem as a model that encodes historical behavior to predict the future
 behavior using soft data split, combining predictor and auto-encoder models. We
 introduce convolutional layer for learning the importance (time decay) of the purchases
 depending on their purchase date and demonstrate that the shape of the time
 decay function can be well approximated by a parametrical function. We present
 offline experimental results showing that neural networks with two hidden layers
 can capture seasonality changes, and at the same time outperform other modeling
 techniques, including our recommender in production. Most importantly, we
 demonstrate that our model can be scaled to all digital categories, and we observe
 significant improvements in an online A/B test. We also discuss key enhancements
 to the neural network model and describe our production pipeline. Finally
 we open-sourced our deep learning library which supports multi-gpu model parallel
 training. This is an important feature in building neural network based recommenders
 with large dimensionality of input and output data.

Answer:
Improving recommendations using time sensitive modeling with neural networks in multiple product categories on a retail website