This paper introduces a simple efficient learning algorithms for general
sequential decision making. The algorithm combines Optimism for exploration
with Maximum Likelihood Estimation for model estimation, which is thus named
OMLE. We prove that OMLE learns the near-optimal policies of an enormously rich
class of sequential decision making problems in a polynomial number of samples.
This rich class includes not only a majority of known tractable model-based
Reinforcement Learning (RL) problems (such as tabular MDPs, factored MDPs, low
witness rank problems, tabular weakly-revealing/observable POMDPs and
multi-step decodable POMDPs), but also many new challenging RL problems
especially in the partially observable setting that were not previously known
to be tractable.
Notably, the new problems addressed by this paper include (1) observable
POMDPs with continuous observation and function approximation, where we achieve
the first sample complexity that is completely independent of the size of
observation space; (2) well-conditioned low-rank sequential decision making
problems (also known as Predictive State Representations (PSRs)), which include
and generalize all known tractable POMDP examples under a more intrinsic
representation; (3) general sequential decision making problems under SAIL
condition, which unifies our existing understandings of model-based RL in both
fully observable and partially observable settings. SAIL condition is
identified by this paper, which can be viewed as a natural generalization of
Bellman/witness rank to address partial observability. This paper also presents
a reward-free variant of OMLE algorithm, which learns approximate dynamic
models that enable the computation of near-optimal policies for all reward
functions simultaneously.

此研究介绍了一个简单高效的学习算法 OMLE，它结合了探索优化和极大似然估计，可在多项式数量的样本中学习当今已知的大多数可处理的强化学习问题，包括 POMDP 和 SAIL 条件下的普通顺序决策问题，并提供了一种奖励免费的近似动态模型学习方法。

乐观极大似然估计 —— 用于部分可观测序列决策的通用基于模型的算法

Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making

Scientific research communities are embracing AI-based solutions to target
tractable scientific tasks and improve research workflows. However, the
development and evaluation of such solutions are scattered across multiple
disciplines. We formalize the problem of scientific AI benchmarking, and
propose a system called SAIBench in the hope of unifying the efforts and
enabling low-friction on-boarding of new disciplines. The system approaches
this goal with SAIL, a domain-specific language to decouple research problems,
AI models, ranking criteria, and software/hardware configuration into reusable
modules. We show that this approach is flexible and can adapt to problems, AI
models, and evaluation methods defined in different perspectives. The project
homepage is this https URL

该研究提出了一种名为 SAIBench 的系统，它使用专业语言 SAIL 分离科研问题，人工智能模型，排名标准和软硬件配置，以便在不同学科领域评估人工智能解决方案并提供低摩擦度入门。

SAIBench：科学人工智能基准测试

SAIBench: Benchmarking AI for Science

This paper studies learning node representations with graph neural networks
(GNNs) for unsupervised scenario. Specifically, we derive a theoretical
analysis and provide an empirical demonstration about the non-steady
performance of GNNs over different graph datasets, when the supervision signals
are not appropriately defined. The performance of GNNs depends on both the node
feature smoothness and the locality of graph structure. To smooth the
discrepancy of node proximity measured by graph topology and node feature, we
proposed SAIL - a novel \underline{S}elf-\underline{A}ugmented graph
contrast\underline{i}ve \underline{L}earning framework, with two complementary
self-distilling regularization modules, \emph{i.e.}, intra- and inter-graph
knowledge distillation. We demonstrate the competitive performance of SAIL on a
variety of graph applications. Even with a single GNN layer, SAIL has
consistently competitive or even better performance on various benchmark
datasets, comparing with state-of-the-art baselines.

本文研究了使用图神经网络（GNNs）进行无监督场景下的节点表示的学习。我们提出了 SAIL 框架来平滑节点的接近程度，并通过两个互补的自蒸馏正则化模块进行知识蒸馏，实现了竞争性的性能。