尽管基于计划的序列建模方法在连续控制方面表现出巨大的潜力，但由于高维空间中规划的高度计算复杂性和天生的困难，将它们扩展到高维状态序列仍然是一个开放的挑战。我们提出了轨迹自动编码计划器（TAP），这是一种基于计划的序列建模RL方法，可扩展到高州行动维度。使用状态条件矢量定量的变分自动编码器（VQ-VAE），点击模拟给定当前状态的轨迹的条件分布。当部署为RL代理时，TAP避免在高维连续动作空间中逐步计划，而是通过Beam Search寻找最佳的潜在代码序列。与$ o（d^3）$轨迹变压器的复杂性不同，TAP享受常数$ o（c）$规划有关州行动维度$ d $的计算复杂性。我们的经验评估还表明，随着维度的增长，TAP的表现越来越强。对于具有较高状态和动作维度的ADROIT机器人手动操纵任务，TAP超过了基于模型的方法，包括TT，其边距很大，并且还击败了强大的无模型参与者 - 批评基准。

实践和磨练技能构成了人类学习方式的基本组成部分，但很少专门培训人造代理人来执行它们。取而代之的是，它们通常是端到端训练的，希望有用的技能将被隐含地学习，以最大程度地提高某些外部奖励功能的折扣回报。在本文中，我们研究了如何将技能纳入具有较大州行动空间和稀疏奖励的复杂环境中的加固学习训练中。为此，我们创建了Skillhack，这是Nethack游戏的任务和相关技能的基准。我们评估了该基准测试的许多基准，以及我们自己的新型基于技能的方法层次启动（HKS），该方法的表现优于所有其他评估的方法。我们的实验表明，先验了解有用技能的学习可以显着改善代理在复杂问题上的表现。我们最终认为，利用预定义的技能为RL问题提供了有用的归纳偏见，尤其是那些具有较大国家行动空间和稀疏奖励的问题。

事实证明，加固学习（RL）的自适应课程有效地制定了稳健的火车和测试环境之间的差异。最近，无监督的环境设计（UED）框架通用RL课程以生成整个环境的序列，从而带来了具有强大的Minimax遗憾属性的新方法。在问题上，在部分观察或随机设置中，最佳策略可能取决于预期部署设置中环境的基本真相分布，而课程学习一定会改变培训分布。我们将这种现象形式化为课程诱导的协变量转移（CICS），并描述了其在核心参数中的发生如何导致次优政策。直接从基本真相分布中采样这些参数可以避免问题，但阻碍了课程学习。我们提出了Samplr，这是一种Minimax遗憾的方法，即使由于CICS偏向基础培训数据，它也优化了基础真相函数。我们证明并验证了具有挑战性的领域，我们的方法在基础上的分布下保留了最佳性，同时促进了整个环境环境的鲁棒性。

深度加强学习概括（RL）的研究旨在产生RL算法，其政策概括为在部署时间进行新的未经调整情况，避免对其培训环境的过度接受。如果我们要在现实世界的情景中部署强化学习算法，那么解决这一点至关重要，那么环境将多样化，动态和不可预测。该调查是这个新生领域的概述。我们为讨论不同的概括问题提供统一的形式主义和术语，在以前的作品上建立不同的概括问题。我们继续对现有的基准进行分类，以及用于解决泛化问题的当前方法。最后，我们提供了对现场当前状态的关键讨论，包括未来工作的建议。在其他结论之外，我们认为，采取纯粹的程序内容生成方法，基准设计不利于泛化的进展，我们建议快速在线适应和将RL特定问题解决作为未来泛化方法的一些领域，我们推荐在UniTexplorated问题设置中构建基准测试，例如离线RL泛化和奖励函数变化。

深度强化学习（RL）的进展是通过用于培训代理商的具有挑战性的基准的可用性来驱动。但是，社区广泛采用的基准未明确设计用于评估RL方法的特定功能。虽然存在用于评估RL的特定打开问题的环境（例如探索，转移学习，无监督环境设计，甚至语言辅助RL），但一旦研究超出证明，通常难以将这些更富有，更复杂的环境 - 概念结果。我们展示了一个强大的沙箱框架，用于易于设计新颖的RL环境。 Minihack是一个停止商店，用于RL实验，环境包括从小房间到复杂的，程序生成的世界。通过利用来自Nethack的全套实体和环境动态，MiniHack是最富有的基网上的视频游戏之一，允许设计快速方便的定制RL测试台。使用这种沙箱框架，可以轻松设计新颖的环境，可以使用人类可读的描述语言或简单的Python接口来设计。除了各种RL任务和基线外，Minihack还可以包装现有的RL基准，并提供无缝添加额外复杂性的方法。

Teaching machines to read natural language documents remains an elusive challenge. Machine reading systems can be tested on their ability to answer questions posed on the contents of documents that they have seen, but until now large scale training and test datasets have been missing for this type of evaluation. In this work we define a new methodology that resolves this bottleneck and provides large scale supervised reading comprehension data. This allows us to develop a class of attention based deep neural networks that learn to read real documents and answer complex questions with minimal prior knowledge of language structure.

With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data movement for these millions of model parameters causes a growing imbalance known as the memory wall. Neuromorphic computing is an emerging paradigm that confronts this imbalance by performing computations directly in analog memories. On the software side, the sequential Backpropagation algorithm prevents efficient parallelization and thus fast convergence. A novel method, Direct Feedback Alignment, resolves inherent layer dependencies by directly passing the error from the output to each layer. At the intersection of hardware/software co-design, there is a demand for developing algorithms that are tolerable to hardware nonidealities. Therefore, this work explores the interrelationship of implementing bio-plausible learning in-situ on neuromorphic hardware, emphasizing energy, area, and latency constraints. Using the benchmarking framework DNN+NeuroSim, we investigate the impact of hardware nonidealities and quantization on algorithm performance, as well as how network topologies and algorithm-level design choices can scale latency, energy and area consumption of a chip. To the best of our knowledge, this work is the first to compare the impact of different learning algorithms on Compute-In-Memory-based hardware and vice versa. The best results achieved for accuracy remain Backpropagation-based, notably when facing hardware imperfections. Direct Feedback Alignment, on the other hand, allows for significant speedup due to parallelization, reducing training time by a factor approaching N for N-layered networks.

The SINDy algorithm has been successfully used to identify the governing equations of dynamical systems from time series data. In this paper, we argue that this makes SINDy a potentially useful tool for causal discovery and that existing tools for causal discovery can be used to dramatically improve the performance of SINDy as tool for robust sparse modeling and system identification. We then demonstrate empirically that augmenting the SINDy algorithm with tools from causal discovery can provides engineers with a tool for learning causally robust governing equations.

When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of black-box learned components in the modern robot autonomy stack. Therefore, coping with OOD data is an important challenge on the path towards trustworthy learning-enabled open-world autonomy. In this paper, we aim to demystify the topic of OOD data and its associated challenges in the context of data-driven robotic systems, drawing connections to emerging paradigms in the ML community that study the effect of OOD data on learned models in isolation. We argue that as roboticists, we should reason about the overall system-level competence of a robot as it performs tasks in OOD conditions. We highlight key research questions around this system-level view of OOD problems to guide future research toward safe and reliable learning-enabled autonomy.

The success of deep learning is largely due to the availability of large amounts of training data that cover a wide range of examples of a particular concept or meaning. In the field of medicine, having a diverse set of training data on a particular disease can lead to the development of a model that is able to accurately predict the disease. However, despite the potential benefits, there have not been significant advances in image-based diagnosis due to a lack of high-quality annotated data. This article highlights the importance of using a data-centric approach to improve the quality of data representations, particularly in cases where the available data is limited. To address this "small-data" issue, we discuss four methods for generating and aggregating training data: data augmentation, transfer learning, federated learning, and GANs (generative adversarial networks). We also propose the use of knowledge-guided GANs to incorporate domain knowledge in the training data generation process. With the recent progress in large pre-trained language models, we believe it is possible to acquire high-quality knowledge that can be used to improve the effectiveness of knowledge-guided generative methods.