当代编码教育往往为学生提供开发具有用户交互和复杂动态系统的计划的任务,例如基于鼠标的游戏。在教学上引人注目的同时,没有现代的自主方法来提供反馈。值得注意的是,通过传统的单元测试,互动计划不可能等级。在本文中,我们正规化为互动计划提供反馈作为分类马尔可夫决策过程(MDP)的任务的挑战。每个学生的程序都完全指定了一个MDP,其中代理需要在合理的概括下运行和决定,如果输入MDP的动态和奖励模型应该被分类为正确或损坏。我们证明,通过在代理和自回归模型之间设计合作目标,我们可以使用代理从输入MDP采样差分轨迹,允许分类器确定成员资格:播放到等级。我们的方法使自动反馈系统能够进行交互式代码分配。我们将711,274个匿名学生提交的数据集发布到单个分配的单个分配,以支持未来的研究。
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The reinforcement learning paradigm is a popular way to address problems that have only limited environmental feedback, rather than correctly labeled examples, as is common in other machine learning contexts. While significant progress has been made to improve learning in a single task, the idea of transfer learning has only recently been applied to reinforcement learning tasks. The core idea of transfer is that experience gained in learning to perform one task can help improve learning performance in a related, but different, task. In this article we present a framework that classifies transfer learning methods in terms of their capabilities and goals, and then use it to survey the existing literature, as well as to suggest future directions for transfer learning work.
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2048 is a single-player stochastic puzzle game. This intriguing and addictive game has been popular worldwide and has attracted researchers to develop game-playing programs. Due to its simplicity and complexity, 2048 has become an interesting and challenging platform for evaluating the effectiveness of machine learning methods. This dissertation conducts comprehensive research on reinforcement learning and computer game algorithms for 2048. First, this dissertation proposes optimistic temporal difference learning, which significantly improves the quality of learning by employing optimistic initialization to encourage exploration for 2048. Furthermore, based on this approach, a state-of-the-art program for 2048 is developed, which achieves the highest performance among all learning-based programs, namely an average score of 625377 points and a rate of 72% for reaching 32768-tiles. Second, this dissertation investigates several techniques related to 2048, including the n-tuple network ensemble learning, Monte Carlo tree search, and deep reinforcement learning. These techniques are promising for further improving the performance of the current state-of-the-art program. Finally, this dissertation discusses pedagogical applications related to 2048 by proposing course designs and summarizing the teaching experience. The proposed course designs use 2048-like games as materials for beginners to learn reinforcement learning and computer game algorithms. The courses have been successfully applied to graduate-level students and received well by student feedback.
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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.
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Imitation learning techniques aim to mimic human behavior in a given task. An agent (a learning machine) is trained to perform a task from demonstrations by learning a mapping between observations and actions. The idea of teaching by imitation has been around for many years, however, the field is gaining attention recently due to advances in computing and sensing as well as rising demand for intelligent applications. The paradigm of learning by imitation is gaining popularity because it facilitates teaching complex tasks with minimal expert knowledge of the tasks. Generic imitation learning methods could potentially reduce the problem of teaching a task to that of providing demonstrations; without the need for explicit programming or designing reward functions specific to the task. Modern sensors are able to collect and transmit high volumes of data rapidly, and processors with high computational power allow fast processing that maps the sensory data to actions in a timely manner. This opens the door for many potential AI applications that require real-time perception and reaction such as humanoid robots, self-driving vehicles, human computer interaction and computer games to name a few. However, specialized algorithms are needed to effectively and robustly learn models as learning by imitation poses its own set of challenges. In this paper, we survey imitation learning methods and present design options in different steps of the learning process. We introduce a background and motivation for the field as well as highlight challenges specific to the imitation problem. Methods for designing and evaluating imitation learning tasks are categorized and reviewed. Special attention is given to learning methods in robotics and games as these domains are the most popular in the literature and provide a wide array of problems and methodologies. We extensively discuss combining imitation learning approaches using different sources and methods, as well as incorporating other motion learning methods to enhance imitation. We also discuss the potential impact on industry, present major applications and highlight current and future research directions.
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背景信息:在过去几年中,机器学习(ML)一直是许多创新的核心。然而,包括在所谓的“安全关键”系统中,例如汽车或航空的系统已经被证明是非常具有挑战性的,因为ML的范式转变为ML带来完全改变传统认证方法。目的:本文旨在阐明与ML为基础的安全关键系统认证有关的挑战,以及文献中提出的解决方案,以解决它们,回答问题的问题如何证明基于机器学习的安全关键系统?'方法:我们开展2015年至2020年至2020年之间发布的研究论文的系统文献综述(SLR),涵盖了与ML系统认证有关的主题。总共确定了217篇论文涵盖了主题,被认为是ML认证的主要支柱:鲁棒性,不确定性,解释性,验证,安全强化学习和直接认证。我们分析了每个子场的主要趋势和问题,并提取了提取的论文的总结。结果:单反结果突出了社区对该主题的热情,以及在数据集和模型类型方面缺乏多样性。它还强调需要进一步发展学术界和行业之间的联系,以加深域名研究。最后,它还说明了必须在上面提到的主要支柱之间建立连接的必要性,这些主要柱主要主要研究。结论:我们强调了目前部署的努力,以实现ML基于ML的软件系统,并讨论了一些未来的研究方向。
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This paper surveys the eld of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the eld and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but di ers considerably in the details and in the use of the word \reinforcement." The paper discusses central issues of reinforcement learning, including trading o exploration and exploitation, establishing the foundations of the eld via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.
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强化学习(RL)和脑电脑接口(BCI)是过去十年一直在增长的两个领域。直到最近,这些字段彼此独立操作。随着对循环(HITL)应用的兴趣升高,RL算法已经适用于人类指导,从而产生互动强化学习(IRL)的子领域。相邻的,BCI应用一直很感兴趣在人机交互期间从神经活动中提取内在反馈。这两个想法通过将BCI集成到IRL框架中,将RL和BCI设置在碰撞过程中,通过将内在反馈可用于帮助培训代理商来帮助框架。这种交叉点被称为内在的IRL。为了进一步帮助,促进BCI和IRL的更深层次,我们对内在IRILL的审查有着重点在于其母体领域的反馈驱动的IRL,同时还提供有关有效性,挑战和未来研究方向的讨论。
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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.
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Besides the recent impressive results on reinforcement learning (RL), safety is still one of the major research challenges in RL. RL is a machine-learning approach to determine near-optimal policies in Markov decision processes (MDPs). In this paper, we consider the setting where the safety-relevant fragment of the MDP together with a temporal logic safety specification is given and many safety violations can be avoided by planning ahead a short time into the future. We propose an approach for online safety shielding of RL agents. During runtime, the shield analyses the safety of each available action. For any action, the shield computes the maximal probability to not violate the safety specification within the next $k$ steps when executing this action. Based on this probability and a given threshold, the shield decides whether to block an action from the agent. Existing offline shielding approaches compute exhaustively the safety of all state-action combinations ahead of time, resulting in huge computation times and large memory consumption. The intuition behind online shielding is to compute at runtime the set of all states that could be reached in the near future. For each of these states, the safety of all available actions is analysed and used for shielding as soon as one of the considered states is reached. Our approach is well suited for high-level planning problems where the time between decisions can be used for safety computations and it is sustainable for the agent to wait until these computations are finished. For our evaluation, we selected a 2-player version of the classical computer game SNAKE. The game represents a high-level planning problem that requires fast decisions and the multiplayer setting induces a large state space, which is computationally expensive to analyse exhaustively.
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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。
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建立可以探索开放式环境的自主机器,发现可能的互动,自主构建技能的曲目是人工智能的一般目标。发展方法争辩说,这只能通过可以生成,选择和学习解决自己问题的自主和本质上动机的学习代理人来实现。近年来,我们已经看到了发育方法的融合,特别是发展机器人,具有深度加强学习(RL)方法,形成了发展机器学习的新领域。在这个新域中,我们在这里审查了一组方法,其中深入RL算法训练,以解决自主获取的开放式曲目的发展机器人问题。本质上动机的目标条件RL算法训练代理商学习代表,产生和追求自己的目标。自我生成目标需要学习紧凑的目标编码以及它们的相关目标 - 成就函数,这导致与传统的RL算法相比,这导致了新的挑战,该算法设计用于使用外部奖励信号解决预定义的目标集。本文提出了在深度RL和发育方法的交叉口中进行了这些方法的类型,调查了最近的方法并讨论了未来的途径。
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Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and "deadly triad". For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.
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深入学习的强化学习(RL)的结合导致了一系列令人印象深刻的壮举,许多相信(深)RL提供了一般能力的代理。然而,RL代理商的成功往往对培训过程中的设计选择非常敏感,这可能需要繁琐和易于易于的手动调整。这使得利用RL对新问题充满挑战,同时也限制了其全部潜力。在许多其他机器学习领域,AutomL已经示出了可以自动化这样的设计选择,并且在应用于RL时也会产生有希望的初始结果。然而,自动化强化学习(AutorL)不仅涉及Automl的标准应用,而且还包括RL独特的额外挑战,其自然地产生了不同的方法。因此,Autorl已成为RL中的一个重要研究领域,提供来自RNA设计的各种应用中的承诺,以便玩游戏等游戏。鉴于RL中考虑的方法和环境的多样性,在不同的子领域进行了大部分研究,从Meta学习到进化。在这项调查中,我们寻求统一自动的领域,我们提供常见的分类法,详细讨论每个区域并对研究人员来说是一个兴趣的开放问题。
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Safe Reinforcement Learning can be defined as the process of learning policies that maximize the expectation of the return in problems in which it is important to ensure reasonable system performance and/or respect safety constraints during the learning and/or deployment processes. We categorize and analyze two approaches of Safe Reinforcement Learning. The first is based on the modification of the optimality criterion, the classic discounted finite/infinite horizon, with a safety factor. The second is based on the modification of the exploration process through the incorporation of external knowledge or the guidance of a risk metric. We use the proposed classification to survey the existing literature, as well as suggesting future directions for Safe Reinforcement Learning.
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In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.
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在过去的十年中,多智能经纪人强化学习(Marl)已经有了重大进展,但仍存在许多挑战,例如高样本复杂性和慢趋同稳定的政策,在广泛的部署之前需要克服,这是可能的。然而,在实践中,许多现实世界的环境已经部署了用于生成策略的次优或启发式方法。一个有趣的问题是如何最好地使用这些方法作为顾问,以帮助改善多代理领域的加强学习。在本文中,我们提供了一个原则的框架,用于将动作建议纳入多代理设置中的在线次优顾问。我们描述了在非传记通用随机游戏环境中提供多种智能强化代理(海军上将)的问题,并提出了两种新的基于Q学习的算法:海军上将决策(海军DM)和海军上将 - 顾问评估(Admiral-AE) ,这使我们能够通过适当地纳入顾问(Admiral-DM)的建议来改善学习,并评估顾问(Admiral-AE)的有效性。我们从理论上分析了算法,并在一般加上随机游戏中提供了关于他们学习的定点保证。此外,广泛的实验说明了这些算法:可以在各种环境中使用,具有对其他相关基线的有利相比的性能,可以扩展到大状态行动空间,并且对来自顾问的不良建议具有稳健性。
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增强学习(RL)研究领域非常活跃,并具有重要的新贡献;特别是考虑到深RL(DRL)的新兴领域。但是,仍然需要解决许多科学和技术挑战,其中我们可以提及抽象行动的能力或在稀疏回报环境中探索环境的难以通过内在动机(IM)来解决的。我们建议通过基于信息理论的新分类法调查这些研究工作:我们在计算上重新审视了惊喜,新颖性和技能学习的概念。这使我们能够确定方法的优势和缺点,并展示当前的研究前景。我们的分析表明,新颖性和惊喜可以帮助建立可转移技能的层次结构,从而进一步抽象环境并使勘探过程更加健壮。
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本文介绍了寻求信息(是)任务,概念和算法的信息重新分类。拟议的分类系统提供了新的维度,以研究寻求任务和方法的信息。新尺寸包括搜索迭代,搜索目标类型和程序的数量,以实现这些目标。寻求任务的信息沿着这些尺寸呼叫合适的计算解决方案的差异。然后,该文章评论了符合每个新类别的机器学习解决方案。该论文结束了对系统的评估活动进行了审查。
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Alphazero,Leela Chess Zero和Stockfish Nnue革新了计算机国际象棋。本书对此类引擎的技术内部工作进行了完整的介绍。该书分为四个主要章节 - 不包括第1章(简介)和第6章(结论):第2章引入神经网络,涵盖了所有用于构建深层网络的基本构建块,例如Alphazero使用的网络。内容包括感知器,后传播和梯度下降,分类,回归,多层感知器,矢量化技术,卷积网络,挤压网络,挤压和激发网络,完全连接的网络,批处理归一化和横向归一化和跨性线性单位,残留层,剩余层,过度效果和底漆。第3章介绍了用于国际象棋发动机以及Alphazero使用的经典搜索技术。内容包括minimax,alpha-beta搜索和蒙特卡洛树搜索。第4章展示了现代国际象棋发动机的设计。除了开创性的Alphago,Alphago Zero和Alphazero我们涵盖Leela Chess Zero,Fat Fritz,Fat Fritz 2以及有效更新的神经网络(NNUE)以及MAIA。第5章是关于实施微型α。 Shexapawn是国际象棋的简约版本,被用作为此的示例。 Minimax搜索可以解决六ap峰,并产生了监督学习的培训位置。然后,作为比较,实施了类似Alphazero的训练回路,其中通过自我游戏进行训练与强化学习结合在一起。最后,比较了类似α的培训和监督培训。
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