Many real-world reinforcement learning tasks require control of complex dynamical systems that involve both costly data acquisition processes and large state spaces. In cases where the transition dynamics can be readily evaluated at specified states (e.g., via a simulator), agents can operate in what is often referred to as planning with a \emph{generative model}. We propose the AE-LSVI algorithm for best-policy identification, a novel variant of the kernelized least-squares value iteration (LSVI) algorithm that combines optimism with pessimism for active exploration (AE). AE-LSVI provably identifies a near-optimal policy \emph{uniformly} over an entire state space and achieves polynomial sample complexity guarantees that are independent of the number of states. When specialized to the recently introduced offline contextual Bayesian optimization setting, our algorithm achieves improved sample complexity bounds. Experimentally, we demonstrate that AE-LSVI outperforms other RL algorithms in a variety of environments when robustness to the initial state is required.

基于变压器神经网络体系结构的自然语言处理（NLP）的令人印象深刻的结果激发了研究人员探索视线离线增强学习（RL）作为通用序列建模问题。基于此范式的最新著作已获得最新的结果，其中一些主要确定性的离线Atari和D4RL基准。但是，由于这些方法将国家和行动共同模拟单一的测序问题，因此它们努力将政策和世界动态对回报的影响解散。因此，在对抗或随机环境中，这些方法导致过度乐观的行为，在自主驾驶（例如自主驾驶）中可能是危险的。在这项工作中，我们提出了一种通过明确解开政策和世界模型来解决这种乐观偏见的方法，该方法使我们在测试时可以搜索对环境中多个可能的未来的稳健性的策略。我们在模拟中的各种自动驾驶任务上展示了我们的方法的出色性能。

缩放多智能体增强学习的卓越障碍之一是为大量代理商分配给个别代理的行动。在本文中，我们通过呼叫\ yrest {部分奖励去耦}（prd）的方法来解决这一信用分配问题，该方法试图将大型合作多代理RL问题分解成涉及代理子集的解耦子问题，从而简化了信用分配。我们经验证明使用PRD在演员 - 批评算法中分解RL问题导致较低的差异策略梯度估计，这提高了各种其他跨越多个代理RL任务的数据效率，学习稳定性和渐近性能。演员 - 评论家方法。此外，我们还将我们的反事实多代理政策梯度（COMA），最先进的MARL算法以及经验证明我们的方法通过更好地利用代理商奖励流的信息来实现昏迷状态，以及启用最近的优势估计的进步。

在RL的许多实际应用中，观察来自环境的状态过渡是昂贵的。例如，在核聚变的等离子体控制问题中，计算给定的状态对对的下一个状态需要查询昂贵的过渡功能，这可以导致许多小时的计算机模拟或美元科学研究。这种昂贵的数据收集禁止应用标准RL算法，该算法通常需要大量观察来学习。在这项工作中，我们解决了有效地学习策略的问题，同时为转换函数进行最小数量的状态动作查询。特别是，我们利用贝叶斯最优实验设计的想法，以指导选择国家行动查询以获得高效学习。我们提出了一种采集功能，该函数量化了状态动作对将提供多少信息对Markov决策过程提供的最佳解决方案。在每次迭代时，我们的算法最大限度地提高了该采集功能，选择要查询的最具信息性的状态动作对，从而产生数据有效的RL方法。我们试验各种模拟的连续控制问题，并显示我们的方法学习最佳政策，最高$ 5 $ - $ 1,000 \倍的数据，而不是基于模型的RL基线，10 ^ 3美元 - $ 10 ^ 5 \ times比无模型RL基线更少的数据。我们还提供了几种消融比较，这指出了从获得数据的原理方法产生的大量改进。

在回归设置中量化不确定性的许多方法中，指定完整量子函数具有吸引力，随着量级可用于解释和评估。预测每个输入的真实条件定量的模型，在所有量化水平上都具有潜在的不确定性的正确和有效的表示。为实现这一目标，许多基于当前的分位式的方法侧重于优化所谓的弹球损失。然而，这种损失限制了适用的回归模型的范围，限制了靶向许多所需特性的能力（例如校准，清晰度，中心间隔），并且可能产生差的条件量数。在这项工作中，我们开发了满足这些缺点的新分位式方法。特别是，我们提出了可以适用于任何类别的回归模型的方法，允许在校准和清晰度之间选择权衡，优化校准中心间隔，并产生更准确的条件定位。我们对我们的方法提供了彻底的实验评估，其中包括核融合中的高维不确定性量化任务。

As an important variant of entity alignment (EA), multi-modal entity alignment (MMEA) aims to discover identical entities across different knowledge graphs (KGs) with multiple modalities like images. However, current MMEA algorithms all adopt KG-level modality fusion strategies but ignore modality differences among individual entities, hurting the robustness to potential noise involved in modalities (e.g., unidentifiable images and relations). In this paper we present MEAformer, a multi-modal entity alignment transformer approach for meta modality hybrid, to dynamically predict the mutual correlation coefficients among modalities for instance-level feature fusion. A modal-aware hard entity replay strategy is also proposed for addressing vague entity details. Extensive experimental results show that our model not only achieves SOTA performance on multiple training scenarios including supervised, unsupervised, iterative, and low resource, but also has limited parameters, optimistic speed, and good interpretability. Our code will be available soon.

This short report reviews the current state of the research and methodology on theoretical and practical aspects of Artificial Neural Networks (ANN). It was prepared to gather state-of-the-art knowledge needed to construct complex, hypercomplex and fuzzy neural networks. The report reflects the individual interests of the authors and, by now means, cannot be treated as a comprehensive review of the ANN discipline. Considering the fast development of this field, it is currently impossible to do a detailed review of a considerable number of pages. The report is an outcome of the Project 'The Strategic Research Partnership for the mathematical aspects of complex, hypercomplex and fuzzy neural networks' meeting at the University of Warmia and Mazury in Olsztyn, Poland, organized in September 2022.

Recent work has shown that fine-tuning large pre-trained language models on a collection of tasks described via instructions, a.k.a. instruction-tuning, improves their zero and few-shot generalization to unseen tasks. However, there is a limited understanding of the performance trade-offs of different decisions made during the instruction-tuning process. These decisions include the scale and diversity of the instruction-tuning benchmark, different task sampling strategies, fine-tuning with and without demonstrations, training using specialized datasets for reasoning and dialogue, and finally, the fine-tuning objectives themselves. In this paper, we characterize the effect of instruction-tuning decisions on downstream task performance when scaling both model and benchmark sizes. To this end, we create OPT-IML Bench: a large benchmark for Instruction Meta-Learning (IML) of 2000 NLP tasks consolidated into task categories from 8 existing benchmarks, and prepare an evaluation framework to measure three types of model generalizations: to tasks from fully held-out categories, to held-out tasks from seen categories, and to held-out instances from seen tasks. Through the lens of this framework, we first present insights about instruction-tuning decisions as applied to OPT-30B and further exploit these insights to train OPT-IML 30B and 175B, which are instruction-tuned versions of OPT. OPT-IML demonstrates all three generalization abilities at both scales on four different evaluation benchmarks with diverse tasks and input formats -- PromptSource, FLAN, Super-NaturalInstructions, and UnifiedSKG. Not only does it significantly outperform OPT on all benchmarks but is also highly competitive with existing models fine-tuned on each specific benchmark. We release OPT-IML at both scales, together with the OPT-IML Bench evaluation framework.

Partial differential equations (PDEs) are widely used for description of physical and engineering phenomena. Some key parameters involved in PDEs, which represents certain physical properties with important scientific interpretations, are difficult or even impossible to be measured directly. Estimation of these parameters from noisy and sparse experimental data of related physical quantities is an important task. Many methods for PDE parameter inference involve a large number of evaluations of numerical solution of PDE through algorithms such as finite element method, which can be time-consuming especially for nonlinear PDEs. In this paper, we propose a novel method for estimating unknown parameters in PDEs, called PDE-Informed Gaussian Process Inference (PIGPI). Through modeling the PDE solution as a Gaussian process (GP), we derive the manifold constraints induced by the (linear) PDE structure such that under the constraints, the GP satisfies the PDE. For nonlinear PDEs, we propose an augmentation method that transfers the nonlinear PDE into an equivalent PDE system linear in all derivatives that our PIGPI can handle. PIGPI can be applied to multi-dimensional PDE systems and PDE systems with unobserved components. The method completely bypasses the numerical solver for PDE, thus achieving drastic savings in computation time, especially for nonlinear PDEs. Moreover, the PIGPI method can give the uncertainty quantification for both the unknown parameters and the PDE solution. The proposed method is demonstrated by several application examples from different areas.

In this paper, we present strong baselines for the task of Feedback Comment Generation for Writing Learning. Given a sentence and an error span, the task is to generate a feedback comment explaining the error. Sentences and feedback comments are both in English. We experiment with LLMs and also create multiple pseudo datasets for the task, investigating how it affects the performance of our system. We present our results for the task along with extensive analysis of the generated comments with the aim of aiding future studies in feedback comment generation for English language learners.