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: Existing multi-agent reinforcement learning (MARL) communication methods have relied on a trusted third party (TTP) to distribute reward to agents, leaving them inapplicable in peer-to-peer environments. This paper proposes reward distribution using {\em Neuron as an Agent} (NaaA) in MARL without a TTP with two key ideas: (i) inter-agent reward distribution and (ii) auction theory. Auction theory is introduced because inter-agent reward distribution is insufficient for optimization. Agents in NaaA maximize their profits (the difference between reward and cost) and, as a theoretical result, the auction mechanism is shown to have agents autonomously evaluate counterfactual returns as the values of other agents. NaaA enables representation trades in peer-to-peer environments, ultimately regarding unit in neural networks as agents. Finally, numerical experiments (a single-agent environment from OpenAI Gym and a multi-agent environment from ViZDoom) confirm that NaaA framework optimization leads to better performance in reinforcement learning.

Answer: Neuron as an Agent (NaaA) enable us to train multi-agent communication without a trusted third party.


Question: We propose the set autoencoder, a model for unsupervised representation learning for sets of elements. It is closely related to sequence-to-sequence models, which learn fixed-sized latent representations for sequences, and have been applied to a number of challenging supervised sequence tasks such as machine translation, as well as unsupervised representation learning for sequences.
 In contrast to sequences, sets are permutation invariant. The proposed set autoencoder considers this fact, both with respect to the input as well as the output of the model. On the input side, we adapt a recently-introduced recurrent neural architecture using a content-based attention mechanism. On the output side, we use a stable marriage algorithm to align predictions to labels in the learning phase.
 We train the model on synthetic data sets of point clouds and show that the learned representations change smoothly with translations in the inputs, preserve distances in the inputs, and that the set size is represented directly. We apply the model to supervised tasks on the point clouds using the fixed-size latent representation. For a number of difficult classification problems, the results are better than those of a model that does not consider the permutation invariance. Especially for small training sets, the set-aware model benefits from unsupervised pretraining.

Answer: We propose the set autoencoder, a model for unsupervised representation learning for sets of elements.


Question: Word embeddings extract semantic features of words from large datasets of text.
 Most embedding methods rely on a log-bilinear model to predict the occurrence
 of a word in a context of other words. Here we propose word2net, a method that
 replaces their linear parametrization with neural networks. For each term in the
 vocabulary, word2net posits a neural network that takes the context as input and
 outputs a probability of occurrence. Further, word2net can use the hierarchical
 organization of its word networks to incorporate additional meta-data, such as
 syntactic features, into the embedding model. For example, we show how to share
 parameters across word networks to develop an embedding model that includes
 part-of-speech information. We study word2net with two datasets, a collection
 of Wikipedia articles and a corpus of U.S. Senate speeches. Quantitatively, we
 found that word2net outperforms popular embedding methods on predicting held-
 out words and that sharing parameters based on part of speech further boosts
 performance. Qualitatively, word2net learns interpretable semantic representations
 and, compared to vector-based methods, better incorporates syntactic information.

Answer:
Word2net is a novel method for learning neural network representations of words that can use syntactic information to learn better semantic features.