在这里，我们报告了强化学习（RL）的案例研究实施，以自动化扫描传输电子显微镜（STEM）工作流程中的操作。为此，我们设计了一个虚拟的，典型的RL环境，以测试和开发网络，以自主对电子束进行自主对齐，而无需事先了解。使用此模拟器，我们评估了环境设计和算法超参数对对齐准确性和学习收敛的影响，从而显示了宽阔的超级参数空间的稳健收敛性。此外，我们在显微镜上部署了成功的模型，以验证该方法并演示设计适当的虚拟环境的价值。与模拟结果一致，微观RL模型在最小的训练后达到了与目标一致性的收敛。总体而言，结果表明，通过利用RL，可以自动化显微镜操作而无需广泛的算法设计，从而通过机器学习方法迈出了增强电子显微镜的又一步。

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policybased methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep Q-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning paradigm: the exploration strategy and the slow learning speed, sometimes known as "the curse of dimensionality". This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuous control - namely Soft Actor-Critic. Specifically, the performance of a Variational Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been investigated by means of digital simulations of quantum circuits. A quantum advantage over the classical algorithm has been found in terms of a significant decrease in the amount of required parameters for satisfactory model training, paving the way for further promising developments.

强化学习正在寻找现实世界问题的方法，从模拟环境转移到物理设置。在这项工作中，我们在一个臂中利用一个臂中的共聚焦望远镜实现了光学马赫 - Zehnder干涉仪的视觉对准，这控制了相应光束的直径和发散。我们使用连续的动作空间;指数缩放使我们能够处理超过两个级别的范围内的动作。我们的代理仅在具有域随机化的模拟环境中列出。在实验评估中，该药剂显着优于现有的解决方案和人类专家。

Adequately assigning credit to actions for future outcomes based on their contributions is a long-standing open challenge in Reinforcement Learning. The assumptions of the most commonly used credit assignment method are disadvantageous in tasks where the effects of decisions are not immediately evident. Furthermore, this method can only evaluate actions that have been selected by the agent, making it highly inefficient. Still, no alternative methods have been widely adopted in the field. Hindsight Credit Assignment is a promising, but still unexplored candidate, which aims to solve the problems of both long-term and counterfactual credit assignment. In this thesis, we empirically investigate Hindsight Credit Assignment to identify its main benefits, and key points to improve. Then, we apply it to factored state representations, and in particular to state representations based on the causal structure of the environment. In this setting, we propose a variant of Hindsight Credit Assignment that effectively exploits a given causal structure. We show that our modification greatly decreases the workload of Hindsight Credit Assignment, making it more efficient and enabling it to outperform the baseline credit assignment method on various tasks. This opens the way to other methods based on given or learned causal structures.

设计加固学习（RL）代理通常是一个艰难的过程，需要大量的设计迭代。由于多种原因，学习可能会失败，并且标准RL方法提供的工具太少，无法洞悉确切原因。在本文中，我们展示了如何将价值分解整合到一类广泛的参与者批评算法中，并使用它来协助迭代代理设计过程。价值分解将奖励函数分为不同的组件，并学习每个组件的价值估计值。这些价值估计提供了对代理商的学习和决策过程的见解，并使新的培训方法可以减轻常见问题。作为演示，我们介绍了SAC-D，这是一种适合价值分解的软角色批评（SAC）的变体。 SAC-D保持与SAC相似的性能，同时学习一组更大的价值预测。我们还介绍了基于分解的工具来利用此信息，包括新的奖励影响指标，该指标衡量了每个奖励组件对代理决策的影响。使用这些工具，我们提供了分解用于识别和解决环境和代理设计问题的几种证明。价值分解广泛适用，易于将其纳入现有算法和工作流程中，使其成为RL从业人员的工具箱中的强大工具。

资产分配（或投资组合管理）是确定如何最佳将有限预算的资金分配给一系列金融工具/资产（例如股票）的任务。这项研究调查了使用无模型的深RL代理应用于投资组合管理的增强学习（RL）的性能。我们培训了几个RL代理商的现实股票价格，以学习如何执行资产分配。我们比较了这些RL剂与某些基线剂的性能。我们还比较了RL代理，以了解哪些类别的代理表现更好。从我们的分析中，RL代理可以执行投资组合管理的任务，因为它们的表现明显优于基线代理（随机分配和均匀分配）。四个RL代理（A2C，SAC，PPO和TRPO）总体上优于最佳基线MPT。这显示了RL代理商发现更有利可图的交易策略的能力。此外，基于价值和基于策略的RL代理之间没有显着的性能差异。演员批评者的表现比其他类型的药物更好。同样，在政策代理商方面的表现要好，因为它们在政策评估方面更好，样品效率在投资组合管理中并不是一个重大问题。这项研究表明，RL代理可以大大改善资产分配，因为它们的表现优于强基础。基于我们的分析，在政策上，参与者批评的RL药物显示出最大的希望。

尽管深度强化学习（RL）最近取得了许多成功，但其方法仍然效率低下，这使得在数据方面解决了昂贵的许多问题。我们的目标是通过利用未标记的数据中的丰富监督信号来进行学习状态表示，以解决这一问题。本文介绍了三种不同的表示算法，可以访问传统RL算法使用的数据源的不同子集使用：（i）GRICA受到独立组件分析（ICA）的启发，并训练深层神经网络以输出统计独立的独立特征。输入。 Grica通过最大程度地减少每个功能与其他功能之间的相互信息来做到这一点。此外，格里卡仅需要未分类的环境状态。 （ii）潜在表示预测（LARP）还需要更多的上下文：除了要求状态作为输入外，它还需要先前的状态和连接它们的动作。该方法通过预测当前状态和行动的环境的下一个状态来学习状态表示。预测器与图形搜索算法一起使用。 （iii）重新培训通过训练深层神经网络来学习国家表示，以学习奖励功能的平滑版本。该表示形式用于预处理输入到深度RL，而奖励预测指标用于奖励成型。此方法仅需要环境中的状态奖励对学习表示表示。我们发现，每种方法都有其优势和缺点，并从我们的实验中得出结论，包括无监督的代表性学习在RL解决问题的管道中可以加快学习的速度。

在人类中，感知意识促进了来自感官输入的快速识别和提取信息。这种意识在很大程度上取决于人类代理人如何与环境相互作用。在这项工作中，我们提出了主动神经生成编码，用于学习动作驱动的生成模型的计算框架，而不会在动态环境中反正出错误（Backprop）。具体而言，我们开发了一种智能代理，即使具有稀疏奖励，也可以从规划的认知理论中汲取灵感。我们展示了我们框架与深度Q学习竞争力的几个简单的控制问题。我们的代理的强劲表现提供了有希望的证据，即神经推断和学习的无背方法可以推动目标定向行为。

随着自动驾驶行业的发展，自动驾驶汽车群体的潜在相互作用也随之增长。结合人工智能和模拟的进步，可以模拟此类组，并且可以学习控制内部汽车的安全模型。这项研究将强化学习应用于多代理停车场的问题，在那里，汽车旨在有效地停车，同时保持安全和理性。利用强大的工具和机器学习框架，我们以马尔可夫决策过程的形式与独立学习者一起设计和实施灵活的停车环境，从而利用多代理通信。我们实施了一套工具来进行大规模执行实验，从而取得了超过98.1％成功率的高达7辆汽车的模型，从而超过了现有的单代机构模型。我们还获得了与汽车在我们环境中表现出的竞争性和协作行为有关的几个结果，这些行为的密度和沟通水平各不相同。值得注意的是，我们发现了一种没有竞争的合作形式，以及一种“泄漏”的合作形式，在没有足够状态的情况下，代理商进行了协作。这种工作在自动驾驶和车队管理行业中具有许多潜在的应用，并为将强化学习应用于多机构停车场提供了几种有用的技术和基准。

In order to avoid conventional controlling methods which created obstacles due to the complexity of systems and intense demand on data density, developing modern and more efficient control methods are required. In this way, reinforcement learning off-policy and model-free algorithms help to avoid working with complex models. In terms of speed and accuracy, they become prominent methods because the algorithms use their past experience to learn the optimal policies. In this study, three reinforcement learning algorithms; DDPG, TD3 and SAC have been used to train Fetch robotic manipulator for four different tasks in MuJoCo simulation environment. All of these algorithms are off-policy and able to achieve their desired target by optimizing both policy and value functions. In the current study, the efficiency and the speed of these three algorithms are analyzed in a controlled environment.

Profile extrusion is a continuous production process for manufacturing plastic profiles from molten polymer. Especially interesting is the design of the die, through which the melt is pressed to attain the desired shape. However, due to an inhomogeneous velocity distribution at the die exit or residual stresses inside the extrudate, the final shape of the manufactured part often deviates from the desired one. To avoid these deviations, the shape of the die can be computationally optimized, which has already been investigated in the literature using classical optimization approaches. A new approach in the field of shape optimization is the utilization of Reinforcement Learning (RL) as a learning-based optimization algorithm. RL is based on trial-and-error interactions of an agent with an environment. For each action, the agent is rewarded and informed about the subsequent state of the environment. While not necessarily superior to classical, e.g., gradient-based or evolutionary, optimization algorithms for one single problem, RL techniques are expected to perform especially well when similar optimization tasks are repeated since the agent learns a more general strategy for generating optimal shapes instead of concentrating on just one single problem. In this work, we investigate this approach by applying it to two 2D test cases. The flow-channel geometry can be modified by the RL agent using so-called Free-Form Deformation, a method where the computational mesh is embedded into a transformation spline, which is then manipulated based on the control-point positions. In particular, we investigate the impact of utilizing different agents on the training progress and the potential of wall time saving by utilizing multiple environments during training.

With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated driving tasks where (D)RL methods have been employed, while addressing key computational challenges in real world deployment of autonomous driving agents. It also delineates adjacent domains such as behavior cloning, imitation learning, inverse reinforcement learning that are related but are not classical RL algorithms. The role of simulators in training agents, methods to validate, test and robustify existing solutions in RL are discussed.

在本文中，我们介绍了潜在的探索（LGE），这是一种基于探索加固学习（RL）的探索范式的简单而通用的方法。最初引入了Go-explore，并具有强大的域知识约束，以将状态空间划分为单元。但是，在大多数实际情况下，从原始观察中汲取域知识是复杂而乏味的。如果细胞分配不足以提供信息，则可以完全无法探索环境。我们认为，可以通过利用学习的潜在表示，可以将Go-explore方法推广到任何环境，而无需细胞。因此，我们表明LGE可以灵活地与学习潜在表示的任何策略相结合。我们表明，LGE虽然比Go-explore更简单，但在多个硬探索环境上纯粹的探索方面，更强大，并且优于所有最先进的算法。 LGE实现可在https://github.com/qgallouedec/lge上作为开源。

我们考虑单个强化学习与基于事件驱动的代理商金融市场模型相互作用时学习最佳执行代理的学习动力。交易在事件时间内通过匹配引擎进行异步进行。最佳执行代理在不同级别的初始订单尺寸和不同尺寸的状态空间上进行考虑。使用校准方法考虑了对基于代理的模型和市场的影响，该方法探讨了经验性风格化事实和价格影响曲线的变化。收敛，音量轨迹和动作痕迹图用于可视化学习动力学。这表明了最佳执行代理如何在模拟的反应性市场框架内学习最佳交易决策，以及如何通过引入战略订单分类来改变模拟市场的反反应。

While reinforcement learning (RL) has become a more popular approach for robotics, designing sufficiently informative reward functions for complex tasks has proven to be extremely difficult due their inability to capture human intent and policy exploitation. Preference based RL algorithms seek to overcome these challenges by directly learning reward functions from human feedback. Unfortunately, prior work either requires an unreasonable number of queries implausible for any human to answer or overly restricts the class of reward functions to guarantee the elicitation of the most informative queries, resulting in models that are insufficiently expressive for realistic robotics tasks. Contrary to most works that focus on query selection to \emph{minimize} the amount of data required for learning reward functions, we take an opposite approach: \emph{expanding} the pool of available data by viewing human-in-the-loop RL through the more flexible lens of multi-task learning. Motivated by the success of meta-learning, we pre-train preference models on prior task data and quickly adapt them for new tasks using only a handful of queries. Empirically, we reduce the amount of online feedback needed to train manipulation policies in Meta-World by 20$\times$, and demonstrate the effectiveness of our method on a real Franka Panda Robot. Moreover, this reduction in query-complexity allows us to train robot policies from actual human users. Videos of our results and code can be found at https://sites.google.com/view/few-shot-preference-rl/home.

深度强化学习（RL）导致了许多最近和开创性的进步。但是，这些进步通常以培训的基础体系结构的规模增加以及用于训练它们的RL算法的复杂性提高，而均以增加规模的成本。这些增长反过来又使研究人员更难迅速原型新想法或复制已发表的RL算法。为了解决这些问题，这项工作描述了ACME，这是一个用于构建新型RL算法的框架，这些框架是专门设计的，用于启用使用简单的模块化组件构建的代理，这些组件可以在各种执行范围内使用。尽管ACME的主要目标是为算法开发提供一个框架，但第二个目标是提供重要或最先进算法的简单参考实现。这些实现既是对我们的设计决策的验证，也是对RL研究中可重复性的重要贡献。在这项工作中，我们描述了ACME内部做出的主要设计决策，并提供了有关如何使用其组件来实施各种算法的进一步详细信息。我们的实验为许多常见和最先进的算法提供了基准，并显示了如何为更大且更复杂的环境扩展这些算法。这突出了ACME的主要优点之一，即它可用于实现大型，分布式的RL算法，这些算法可以以较大的尺度运行，同时仍保持该实现的固有可读性。这项工作提出了第二篇文章的版本，恰好与模块化的增加相吻合，对离线，模仿和从演示算法学习以及作为ACME的一部分实现的各种新代理。

强化学习（RL）已在机器人控制中广泛采用。尽管取得了许多成功，但一个主要的持续问题可能是数据效率非常低。一种解决方案是交互式反馈，已证明可以大大加速RL。结果，有很多不同的策略，但是，这些策略主要是在离散的网格世界和小规模最佳控制场景上进行测试的。在文献中，关于哪种反馈频率是最佳的，或者当时反馈是最有益的。为了解决这些差异，我们分离并量化了具有连续状态和动作空间的机器人任务中反馈频率的影响。这些实验包括不同复杂性的机器人操纵臂的逆运动学学习。我们表明，看似矛盾的报道现象在不同的​​复杂程度下发生。此外，我们的结果表明，没有任何理想的反馈频率存在。相反，随着代理商在任务的熟练程度的提高，反馈频率应更改。

在空间显式的基于个别模型中捕获和模拟智能自适应行为仍然是研究人员持续的挑战。虽然收集了不断增长的现实行为数据，但存在很少的方法，可以量化和正式化关键的个人行为以及它们如何改变空间和时间。因此，通常使用的常用代理决策框架（例如事件条件 - 行动规则）通常只需要仅关注狭窄的行为范围。我们认为，这些行为框架通常不会反映现实世界的情景，并且未能捕捉如何以响应刺激而发展行为。对机器学习方法的兴趣增加了近年来模拟智能自适应行为的兴趣。在该区域中开始获得牵引的一种方法是增强学习（RL）。本文探讨了如何使用基于简单的捕食者 - 猎物代理的模型（ABM）来应用RL创建紧急代理行为。运行一系列模拟，我们证明了使用新型近端政策优化（PPO）算法培训的代理以展示现实世界智能自适应行为的性质，例如隐藏，逃避和觅食。