The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.

从过去的经验中发现有用的行为并将其转移到新任务的能力被认为是自然体现智力的核心组成部分。受神经科学的启发，发现在瓶颈状态下切换的行为一直被人们追求，以引起整个任务的最小描述长度的计划。先前的方法仅支持在线，政策，瓶颈状态发现，限制样本效率或离散的状态行动域，从而限制适用性。为了解决这个问题，我们介绍了基于模型的离线选项（MO2），这是一个脱机后视框架，支持在连续的状态行动空间上发现样品效率高效瓶颈选项。一旦脱机而在源域上学习了瓶颈选项，它们就会在线转移，以改善转移域的探索和价值估计。我们的实验表明，在复杂的长途连续控制任务上，具有稀疏，延迟的奖励，MO2的属性至关重要，并且导致性能超过最近的选项学习方法。其他消融进一步证明了对期权可预测性和信用分配的影响。

机器人将在整个生命周期中都会经历非平稳环境动态：机器人动态可能会因磨损而改变，或者周围的环境可能会随着时间而改变。最终，机器人在遇到的所有环境变化中都应表现良好。同时，它仍然应该能够在新环境中快速学习。我们在这样的终身学习环境中确定了强化学习（RL）的两个挑战：首先，现有的现有非政策算法在保持旧环境中保持良好绩效和有效学习之间的权衡方面挣扎尽管将所有数据保留在重播缓冲区中，但新环境。我们提出了离线蒸馏管道，以通过将培训程序分离为在线互动阶段和离线蒸馏阶段来打破这一权衡。第二，我们发现，通过从一生中多个环境中的不平衡的非政策数据进行培训会产生重要性能下降。我们确定这种性能下降是由数据集中质量不平衡和大小的组合引起的，这些质量和大小加剧了Q功能的外推误差。在蒸馏阶段，我们通过使策略更接近生成数据的行为策略来应用一个简单的解决方案。在实验中，我们在各种环境变化中通过模拟的两足机器人步行任务证明了这两个挑战和拟议的解决方案。我们表明，离线蒸馏管线在所有遇到的环境中都能取得更好的性能，而不会影响数据收集。我们还提供了一项全面的实证研究，以支持我们对数据不平衡问题的假设。

对于在现实世界中运营的机器人来说，期望学习可以有效地转移和适应许多任务和场景的可重复使用的行为。我们提出了一种使用分层混合潜变量模型来从数据中学习抽象运动技能的方法。与现有工作相比，我们的方法利用了离散和连续潜在变量的三级层次结构，以捕获一组高级行为，同时允许如何执行它们的差异。我们在操纵域中展示该方法可以有效地将离线数据脱落到不同的可执行行为，同时保留连续潜变量模型的灵活性。由此产生的技能可以在新的任务，看不见的对象和州内转移和微调到基于视觉的策略，与现有的技能和仿制的方法相比，产生更好的样本效率和渐近性能。我们进一步分析了技能最有益的方式以及何时：他们鼓励定向探索来涵盖与任务相关的国家空间的大区域，使其在挑战稀疏奖励环境中最有效。

The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision. In this paper, we specifically investigate dimension reduction for time series through functional data analysis. Current methods for dimension reduction in functional data are functional principal component analysis and functional autoencoders, which are limited to linear mappings or scalar representations for the time series, which is inefficient. In real data applications, the nature of the data is much more complex. We propose a non-linear function-on-function approach, which consists of a functional encoder and a functional decoder, that uses continuous hidden layers consisting of continuous neurons to learn the structure inherent in functional data, which addresses the aforementioned concerns in the existing approaches. Our approach gives a low dimension latent representation by reducing the number of functional features as well as the timepoints at which the functions are observed. The effectiveness of the proposed model is demonstrated through multiple simulations and real data examples.

Differentiable rendering aims to compute the derivative of the image rendering function with respect to the rendering parameters. This paper presents a novel algorithm for 6-DoF pose estimation through gradient-based optimization using a differentiable rendering pipeline. We emphasize two key contributions: (1) instead of solving the conventional 2D to 3D correspondence problem and computing reprojection errors, images (rendered using the 3D model) are compared only in the 2D feature space via sparse 2D feature correspondences. (2) Instead of an analytical image formation model, we compute an approximate local gradient of the rendering process through online learning. The learning data consists of image features extracted from multi-viewpoint renders at small perturbations in the pose neighborhood. The gradients are propagated through the rendering pipeline for the 6-DoF pose estimation using nonlinear least squares. This gradient-based optimization regresses directly upon the pose parameters by aligning the 3D model to reproduce a reference image shape. Using representative experiments, we demonstrate the application of our approach to pose estimation in proximity operations.

Differentiable Search Indices (DSIs) encode a corpus of documents in the parameters of a model and use the same model to map queries directly to relevant document identifiers. Despite the strong performance of DSI models, deploying them in situations where the corpus changes over time is computationally expensive because reindexing the corpus requires re-training the model. In this work, we introduce DSI++, a continual learning challenge for DSI to incrementally index new documents while being able to answer queries related to both previously and newly indexed documents. Across different model scales and document identifier representations, we show that continual indexing of new documents leads to considerable forgetting of previously indexed documents. We also hypothesize and verify that the model experiences forgetting events during training, leading to unstable learning. To mitigate these issues, we investigate two approaches. The first focuses on modifying the training dynamics. Flatter minima implicitly alleviate forgetting, so we optimize for flatter loss basins and show that the model stably memorizes more documents (+12\%). Next, we introduce a generative memory to sample pseudo-queries for documents and supplement them during continual indexing to prevent forgetting for the retrieval task. Extensive experiments on novel continual indexing benchmarks based on Natural Questions (NQ) and MS MARCO demonstrate that our proposed solution mitigates forgetting by a significant margin. Concretely, it improves the average Hits@10 by $+21.1\%$ over competitive baselines for NQ and requires $6$ times fewer model updates compared to re-training the DSI model for incrementally indexing five corpora in a sequence.

Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1082 code generation problems using the pandas data analysis framework in data science notebooks. ARCADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PaChiNCo, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanation, showing the potential to improve the diversity and explainability of model predictions.

Harvesting question-answer (QA) pairs from customer service chatlog in the wild is an efficient way to enrich the knowledge base for customer service chatbots in the cold start or continuous integration scenarios. Prior work attempts to obtain 1-to-1 QA pairs from growing customer service chatlog, which fails to integrate the incomplete utterances from the dialog context for composite QA retrieval. In this paper, we propose N-to-N QA extraction task in which the derived questions and corresponding answers might be separated across different utterances. We introduce a suite of generative/discriminative tagging based methods with end-to-end and two-stage variants that perform well on 5 customer service datasets and for the first time setup a benchmark for N-to-N DialogQAE with utterance and session level evaluation metrics. With a deep dive into extracted QA pairs, we find that the relations between and inside the QA pairs can be indicators to analyze the dialogue structure, e.g. information seeking, clarification, barge-in and elaboration. We also show that the proposed models can adapt to different domains and languages, and reduce the labor cost of knowledge accumulation in the real-world product dialogue platform.

By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance. While this capability has been demonstrated in other fields such as computer vision, natural language processing or speech recognition, it remains to be shown in robotics, where the generalization capabilities of the models are particularly critical due to the difficulty of collecting real-world robotic data. We argue that one of the keys to the success of such general robotic models lies with open-ended task-agnostic training, combined with high-capacity architectures that can absorb all of the diverse, robotic data. In this paper, we present a model class, dubbed Robotics Transformer, that exhibits promising scalable model properties. We verify our conclusions in a study of different model classes and their ability to generalize as a function of the data size, model size, and data diversity based on a large-scale data collection on real robots performing real-world tasks. The project's website and videos can be found at robotics-transformer.github.io