In recent years, Multi-Agent Path Finding (MAPF) has attracted attention from
the fields of both Operations Research (OR) and Reinforcement Learning (RL).
However, in the 2021 Flatland3 Challenge, a competition on MAPF, the best RL
method scored only 27.9, far less than the best OR method. This paper proposes
a new RL solution to Flatland3 Challenge, which scores 125.3, several times
higher than the best RL solution before. We creatively apply a novel network
architecture, TreeLSTM, to MAPF in our solution. Together with several other RL
techniques, including reward shaping, multiple-phase training, and centralized
control, our solution is comparable to the top 2-3 OR methods.

本文提出一种新的强化学习方法，使用了 TreeLSTM 等网络结构和多个策略，以及集中式控制等技术，从而在 Flatland3 Challenge 中获得了高分数，与目前排名前 2-3 的运筹学方法相当。

基于树形 LSTM 的多智能体路径规划

Multi-Agent Path Finding via Tree LSTM

The input to a neural sequence-to-sequence model is often determined by an
up-stream system, e.g. a word segmenter, part of speech tagger, or speech
recognizer. These up-stream models are potentially error-prone. Representing
inputs through word lattices allows making this uncertainty explicit by
capturing alternative sequences and their posterior probabilities in a compact
form. In this work, we extend the TreeLSTM (Tai et al., 2015) into a
LatticeLSTM that is able to consume word lattices, and can be used as encoder
in an attentional encoder-decoder model. We integrate lattice posterior scores
into this architecture by extending the TreeLSTM's child-sum and forget gates
and introducing a bias term into the attention mechanism. We experiment with
speech translation lattices and report consistent improvements over baselines
that translate either the 1-best hypothesis or the lattice without posterior
scores.

研究使用基于树结构的神经网络模型作为编码器建立语音翻译模型，使用词汇图来代替输入，可以提高翻译准确性。