MultiSep Returns的违规学习对于采样高效的强化学习至关重要，特别是在现在与深神经网络常用的经验重播设置中。经典地，以每个判定方式纠正偏离策略估计偏差：通过在每个动作之后通过瞬时重要性采样（IS）比率（通过资格迹线）重新加权。许多重要的截止措施算法，如树备份和回撤依赖于该机制以及用于截断的不同协议（“切割”）比率（“迹线”）来抵消IS估计器的过度方差。遗憾的是，各种决策的切割迹线不一定有效;一旦根据当地信息切割了痕迹，效果就不能在后来逆转，可能导致估计恢复和较慢的学习截断。为了激励有效的截止策略算法，我们提出了一个多步算子，允许任意的过去依赖性迹线。我们证明我们的运营商是策略评估的融合，并在针对限制限制策略时最佳控制。我们的定理建立了许多现有算法的第一个收敛保证，包括截断，非马尔可道回撤和历史依赖于历史依赖于历史依赖性TD（$ \ lambda $）。我们的理论结果还为开发新算法的制定提供了指导，以便共同考虑更好的过去的决定，以获得更好的信用分配和更快的学习。

返回缓存是最近的策略，可以使用多步骤估算器（例如{\ lambda} -Return）实现高效的小纤维培训，用于深入加强学习。通过预先计算顺序批量的返回估计，然后将结果存储在辅助数据结构中以供稍后采样，可以大大减少每次估计的平均计算。尽管如此，可以提高返回缓存的效率，特别是关于其大的内存使用和重复数据副本。我们提出了一种新的数据结构，虚拟重播缓存（VRC），以解决这些缺点。学习播放Atari 2600游戏时，VRC几乎消除了DQN（{\ Lambda}）的缓存内存占用占据占据功能，略微降低了硬件上的总培训时间。

深度Q-Network（DQN）标志着强化学习的主要里程碑，首次展示了人类水平控制政策，可以通过奖励最大化直接从原始视觉输入学习。即使是介绍多年后，DQN与研究界仍然高度相关，因为其在继承方法中采用了许多创新。然而，尽管在临时上的硬件进步，但DQN的原始ATari 2600实验仍然昂贵，以便全面复制。这对无法负担最先进的硬件或缺乏大规模云计算资源的研究人员构成了巨大的障碍。为了便于改进对深度加强学习研究的访问，我们介绍了一种DQN实现，它利用了一种新颖的并发和同步执行框架，旨在最大限度地利用异构CPU-GPU桌面系统。只需一个Nvidia GeForce GTX 1080 GPU，我们的实施将200亿帧atari实验的培训时间从25小时到仅需9小时。本文介绍的想法应普遍适用于大量违规的深度增强学习方法。

用于分散执行的集中培训，其中代理商使用集中信息训练，但在线以分散的方式执行，在多智能体增强学习界中获得了普及。特别是，具有集中评论家和分散的演员的演员 - 批评方法是这个想法的常见实例。然而，即使它是许多算法的标准选择，也没有完全讨论和理解使用集中评论批读的影响。因此，我们正式分析集中和分散的批评批评方法，了解对评论家选择的影响。由于我们的理论使得不切实际的假设，我们还经验化地比较了广泛的环境中集中式和分散的批评方法来验证我们的理论并提供实用建议。我们展示了当前文献中集中评论家存在误解，并表明集中式评论家设计并不是严格用的，而是集中和分散的批评者具有不同的利弊，算法设计人员应该考虑到不同的利弊。

Knowledge distillation (KD) has gained a lot of attention in the field of model compression for edge devices thanks to its effectiveness in compressing large powerful networks into smaller lower-capacity models. Online distillation, in which both the teacher and the student are learning collaboratively, has also gained much interest due to its ability to improve on the performance of the networks involved. The Kullback-Leibler (KL) divergence ensures the proper knowledge transfer between the teacher and student. However, most online KD techniques present some bottlenecks under the network capacity gap. By cooperatively and simultaneously training, the models the KL distance becomes incapable of properly minimizing the teacher's and student's distributions. Alongside accuracy, critical edge device applications are in need of well-calibrated compact networks. Confidence calibration provides a sensible way of getting trustworthy predictions. We propose BD-KD: Balancing of Divergences for online Knowledge Distillation. We show that adaptively balancing between the reverse and forward divergences shifts the focus of the training strategy to the compact student network without limiting the teacher network's learning process. We demonstrate that, by performing this balancing design at the level of the student distillation loss, we improve upon both performance accuracy and calibration of the compact student network. We conducted extensive experiments using a variety of network architectures and show improvements on multiple datasets including CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet. We illustrate the effectiveness of our approach through comprehensive comparisons and ablations with current state-of-the-art online and offline KD techniques.

Chatbots, or bots for short, are multi-modal collaborative assistants that can help people complete useful tasks. Usually, when chatbots are referenced in connection with elections, they often draw negative reactions due to the fear of mis-information and hacking. Instead, in this paper, we explore how chatbots may be used to promote voter participation in vulnerable segments of society like senior citizens and first-time voters. In particular, we build a system that amplifies official information while personalizing it to users' unique needs transparently. We discuss its design, build prototypes with frequently asked questions (FAQ) election information for two US states that are low on an ease-of-voting scale, and report on its initial evaluation in a focus group. Our approach can be a win-win for voters, election agencies trying to fulfill their mandate and democracy at large.

Granular jamming has recently become popular in soft robotics with widespread applications including industrial gripping, surgical robotics and haptics. Previous work has investigated the use of various techniques that exploit the nature of granular physics to improve jamming performance, however this is generally underrepresented in the literature compared to its potential impact. We present the first research that exploits vibration-based fluidisation actively (e.g., during a grip) to elicit bespoke performance from granular jamming grippers. We augment a conventional universal gripper with a computer-controllled audio exciter, which is attached to the gripper via a 3D printed mount, and build an automated test rig to allow large-scale data collection to explore the effects of active vibration. We show that vibration in soft jamming grippers can improve holding strength. In a series of studies, we show that frequency and amplitude of the waveforms are key determinants to performance, and that jamming performance is also dependent on temporal properties of the induced waveform. We hope to encourage further study focused on active vibrational control of jamming in soft robotics to improve performance and increase diversity of potential applications.

Fruit harvesting has recently experienced a shift towards soft grippers that possess compliance, adaptability, and delicacy. In this context, pneumatic grippers are popular, due to provision of high deformability and compliance, however they typically possess limited grip strength. Jamming possesses strong grip capability, however has limited deformability and often requires the object to be pushed onto a surface to attain a grip. This paper describes a hybrid gripper combining pneumatics (for deformation) and jamming (for grip strength). Our gripper utilises a torus (donut) structure with two chambers controlled by pneumatic and vacuum pressure respectively, to conform around a target object. The gripper displays good adaptability, exploiting pneumatics to mould to the shape of the target object where jamming can be successfully harnessed to grip. The main contribution of the paper is design, fabrication, and characterisation of the first hybrid gripper that can use granular jamming in free space, achieving significantly larger retention forces compared to pure pneumatics. We test our gripper on a range of different sizes and shapes, as well as picking a broad range of real fruit.

Human operators in human-robot teams are commonly perceived to be critical for mission success. To explore the direct and perceived impact of operator input on task success and team performance, 16 real-world missions (10 hrs) were conducted based on the DARPA Subterranean Challenge. These missions were to deploy a heterogeneous team of robots for a search task to locate and identify artifacts such as climbing rope, drills and mannequins representing human survivors. Two conditions were evaluated: human operators that could control the robot team with state-of-the-art autonomy (Human-Robot Team) compared to autonomous missions without human operator input (Robot-Autonomy). Human-Robot Teams were often in directed autonomy mode (70% of mission time), found more items, traversed more distance, covered more unique ground, and had a higher time between safety-related events. Human-Robot Teams were faster at finding the first artifact, but slower to respond to information from the robot team. In routine conditions, scores were comparable for artifacts, distance, and coverage. Reasons for intervention included creating waypoints to prioritise high-yield areas, and to navigate through error-prone spaces. After observing robot autonomy, operators reported increases in robot competency and trust, but that robot behaviour was not always transparent and understandable, even after high mission performance.

The introductory programming sequence has been the focus of much research in computing education. The recent advent of several viable and freely-available AI-driven code generation tools present several immediate opportunities and challenges in this domain. In this position paper we argue that the community needs to act quickly in deciding what possible opportunities can and should be leveraged and how, while also working on how to overcome or otherwise mitigate the possible challenges. Assuming that the effectiveness and proliferation of these tools will continue to progress rapidly, without quick, deliberate, and concerted efforts, educators will lose advantage in helping shape what opportunities come to be, and what challenges will endure. With this paper we aim to seed this discussion within the computing education community.