Q学习是最著名的增强学习算法之一。使用神经网络开发该算法已经做出了巨大的努力。其中包括引导深度Q学习网络。它利用多个神经网络头将多样性引入Q学习。有时可以将多样性视为代理商在给定状态下可以采取的合理移动量，类似于RL勘探比的定义。因此，引导深度Q学习网络的性能与算法中的多样性水平深厚相关。在最初的研究中，有人指出，随机的先验可以提高模型的性能。在本文中，我们进一步探讨了用噪声代替先验的可能性，并从高斯分布中采样噪声，以将更多的多样性引入该算法。我们对Atari基准测试进行实验，并将我们的算法与原始算法和其他相关算法进行比较。结果表明，我们对自举的深Q学习算法的修改可在不同类型的Atari游戏中获得更高的评估得分。因此，我们得出的结论是，用噪声代替先验可以通过确保多样性的完整性来改善自举的深度Q学习的性能。

变形金刚是使用多层自我注意力头的神经网络模型。注意力是在变形金刚中实现的，作为“键”和“查询”的上下文嵌入。变形金刚允许从不同层重新集合注意力信息，并同时处理所有输入，在处理大量数据时，它们比复发性神经网络更方便。近年来，变形金刚在自然语言处理任务上表现出色。同时，已经做出了巨大的努力，以使变压器适应机器学习的其他领域，例如Swin Transformer和Decision Transformer。 Swin Transformer是一种有前途的神经网络体系结构，将图像像素分为小斑块，并在固定尺寸的（移位）窗口内应用本地自我发挥操作。决策变压器已成功地将变形金刚应用于离线增强学习，并表明来自Atari游戏的随机步行样本足以让代理商学习优化的行为。但是，将在线强化学习与变形金刚结合在一起是更具挑战性的。在本文中，我们进一步探讨了不修改强化学习政策的可能性，而仅使用Swin Transformer的自我发明体系结构代替卷积神经网络架构。也就是说，我们旨在改变代理商对世界的看法，而不是代理商如何计划世界。我们在街机学习环境中对49场比赛进行实验。结果表明，在街机学习环境中，使用SWIN Transform在强化学习中的评估得分明显更高。因此，我们得出的结论是，在线强化学习可以从用空间令牌嵌入来利用自我侵犯中受益。

Efficient exploration remains a major challenge for reinforcement learning (RL). Common dithering strategies for exploration, such as -greedy, do not carry out temporally-extended (or deep) exploration; this can lead to exponentially larger data requirements. However, most algorithms for statistically efficient RL are not computationally tractable in complex environments. Randomized value functions offer a promising approach to efficient exploration with generalization, but existing algorithms are not compatible with nonlinearly parameterized value functions. As a first step towards addressing such contexts we develop bootstrapped DQN. We demonstrate that bootstrapped DQN can combine deep exploration with deep neural networks for exponentially faster learning than any dithering strategy. In the Arcade Learning Environment bootstrapped DQN substantially improves learning speed and cumulative performance across most games.

Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN Replay Dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-policy deep RL algorithms, even when trained solely on this fixed dataset, outperform the fully-trained DQN agent. To enhance generalization in the offline setting, we present Random Ensemble Mixture (REM), a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates. Offline REM trained on the DQN Replay Dataset surpasses strong RL baselines. Ablation studies highlight the role of offline dataset size and diversity as well as the algorithm choice in our positive results. Overall, the results here present an optimistic view that robust RL algorithms used on sufficiently large and diverse offline datasets can lead to high quality policies. To provide a testbed for offline RL and reproduce our results, the DQN Replay Dataset is released at offline-rl.github.io.

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.

深度强化学习（DRL）和深度多机构的强化学习（MARL）在包括游戏AI，自动驾驶汽车，机器人技术等各种领域取得了巨大的成功。但是，众所周知，DRL和Deep MARL代理的样本效率低下，即使对于相对简单的问题设置，通常也需要数百万个相互作用，从而阻止了在实地场景中的广泛应用和部署。背后的一个瓶颈挑战是众所周知的探索问题，即如何有效地探索环境和收集信息丰富的经验，从而使政策学习受益于最佳研究。在稀疏的奖励，吵闹的干扰，长距离和非平稳的共同学习者的复杂环境中，这个问题变得更加具有挑战性。在本文中，我们对单格和多代理RL的现有勘探方法进行了全面的调查。我们通过确定有效探索的几个关键挑战开始调查。除了上述两个主要分支外，我们还包括其他具有不同思想和技术的著名探索方法。除了算法分析外，我们还对一组常用基准的DRL进行了全面和统一的经验比较。根据我们的算法和实证研究，我们终于总结了DRL和Deep Marl中探索的公开问题，并指出了一些未来的方向。

Deep reinforcement learning (RL) has achieved several high profile successes in difficult decision-making problems. However, these algorithms typically require a huge amount of data before they reach reasonable performance. In fact, their performance during learning can be extremely poor. This may be acceptable for a simulator, but it severely limits the applicability of deep RL to many real-world tasks, where the agent must learn in the real environment. In this paper we study a setting where the agent may access data from previous control of the system. We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data and is able to automatically assess the necessary ratio of demonstration data while learning thanks to a prioritized replay mechanism. DQfD works by combining temporal difference updates with supervised classification of the demonstrator's actions. We show that DQfD has better initial performance than Prioritized Dueling Double Deep Q-Networks (PDD DQN) as it starts with better scores on the first million steps on 41 of 42 games and on average it takes PDD DQN 83 million steps to catch up to DQfD's performance. DQfD learns to out-perform the best demonstration given in 14 of 42 games. In addition, DQfD leverages human demonstrations to achieve state-of-the-art results for 11 games. Finally, we show that DQfD performs better than three related algorithms for incorporating demonstration data into DQN.

在探索中，由于当前的低效率而引起的强化学习领域，具有较大动作空间的学习控制政策是一个具有挑战性的问题。在这项工作中，我们介绍了深入的强化学习（DRL）算法呼叫多动作网络（MAN）学习，以应对大型离散动作空间的挑战。我们建议将动作空间分为两个组件，从而为每个子行动创建一个值神经网络。然后，人使用时间差异学习来同步训练网络，这比训练直接动作输出的单个网络要简单。为了评估所提出的方法，我们在块堆叠任务上测试了人，然后扩展了人类从Atari Arcade学习环境中使用18个动作空间的12个游戏。我们的结果表明，人的学习速度比深Q学习和双重Q学习更快，这意味着我们的方法比当前可用于大型动作空间的方法更好地执行同步时间差异算法。

The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear which of these extensions are complementary and can be fruitfully combined. This paper examines six extensions to the DQN algorithm and empirically studies their combination. Our experiments show that the combination provides state-of-the-art performance on the Atari 2600 benchmark, both in terms of data efficiency and final performance. We also provide results from a detailed ablation study that shows the contribution of each component to overall performance.

我们确定和研究政策流失的现象，即基于价值的强化学习中贪婪政策的快速变化。政策流失以惊人的快速步伐运作，改变了少数学习更新（在Atari上的DQN等典型的深层RL设置中）中大量州的贪婪行动。我们从经验上表征了现象，验证它不限于特定算法或环境特性。许多消融有助于削弱关于为什么流失仅与深度学习有关的少数相关的合理解释。最后，我们假设政策流失是一种有益但被忽视的隐性探索形式，它以新鲜的方式铸造了$ \ epsilon $ greedy探索，即$ \ epsilon $ - noise的作用比预期的要小得多。

强化学习代理通过鼓励最大化其总奖励的行为来学习，通常由环境提供。然而，在许多环境中，在一系列行动而不是每个单一动作之后提供奖励，导致代理在这些操作是有效的方面遇到模糊性，称为信用分配问题的问题。在本文中，我们提出了由行为心理学启发的两种策略，使代理人能够在本质上估计更多信息奖励价值，以便没有奖励。第一个策略，称为自我惩罚（SP），劝阻代理人犯错误，导致不良终端状态。第二次策略，称为奖励回填（RB），退回两个奖励行动之间的奖励。我们证明，在某些假设和不管使用的加强学习算法的情况下，这两种策略在其总奖励方面维护了所有可能政策的空间中的政策顺序，并且通过扩展，维护最佳政策。因此，我们提出的策略与任何通过经验学习价值或动作值函数的任何强化学习算法。我们将这两种策略纳入三种流行的深度加强学习方法，并在三十塔塔利游戏中评估结果。参数调整后，我们的结果表明，拟议的策略将测试方法以超过25倍的性能改善提高了超过65％的测试游戏。

横跨街机学习环境，彩虹实现了对人类和现代RL算法的竞争程度。然而，获得这种性能水平需要大量的数据和硬件资源，在该区域进行研究计算地昂贵并且在实际应用中使用通常是不可行的。本文的贡献是三倍：我们（1）提出了一种改进的彩虹版本，寻求大大减少彩虹的数据，培训时间和计算要求，同时保持其竞争性能; （2）我们通过实验通过对街机学习环境的实验来证明我们的方法的有效性，以及（3）我们进行了许多消融研究，以研究个体提出的修改的效果。我们改进的Rainbow版本达到了靠近经典彩虹的中位数的人为规范化分数，而使用20倍的数据，只需要7.5小时的单个GPU培训时间。我们还提供了我们的全部实施，包括预先训练的型号。

With the development of experimental quantum technology, quantum control has attracted increasing attention due to the realization of controllable artificial quantum systems. However, because quantum-mechanical systems are often too difficult to analytically deal with, heuristic strategies and numerical algorithms which search for proper control protocols are adopted, and, deep learning, especially deep reinforcement learning (RL), is a promising generic candidate solution for the control problems. Although there have been a few successful applications of deep RL to quantum control problems, most of the existing RL algorithms suffer from instabilities and unsatisfactory reproducibility, and require a large amount of fine-tuning and a large computational budget, both of which limit their applicability. To resolve the issue of instabilities, in this dissertation, we investigate the non-convergence issue of Q-learning. Then, we investigate the weakness of existing convergent approaches that have been proposed, and we develop a new convergent Q-learning algorithm, which we call the convergent deep Q network (C-DQN) algorithm, as an alternative to the conventional deep Q network (DQN) algorithm. We prove the convergence of C-DQN and apply it to the Atari 2600 benchmark. We show that when DQN fail, C-DQN still learns successfully. Then, we apply the algorithm to the measurement-feedback cooling problems of a quantum quartic oscillator and a trapped quantum rigid body. We establish the physical models and analyse their properties, and we show that although both C-DQN and DQN can learn to cool the systems, C-DQN tends to behave more stably, and when DQN suffers from instabilities, C-DQN can achieve a better performance. As the performance of DQN can have a large variance and lack consistency, C-DQN can be a better choice for researches on complicated control problems.

Experience replay lets online reinforcement learning agents remember and reuse experiences from the past. In prior work, experience transitions were uniformly sampled from a replay memory. However, this approach simply replays transitions at the same frequency that they were originally experienced, regardless of their significance. In this paper we develop a framework for prioritizing experience, so as to replay important transitions more frequently, and therefore learn more efficiently. We use prioritized experience replay in Deep Q-Networks (DQN), a reinforcement learning algorithm that achieved human-level performance across many Atari games. DQN with prioritized experience replay achieves a new stateof-the-art, outperforming DQN with uniform replay on 41 out of 49 games.

深度神经网络的强大学习能力使强化学习者能够直接从连续环境中学习有效的控制政策。从理论上讲，为了实现稳定的性能，神经网络假设I.I.D.不幸的是，在训练数据在时间上相关且非平稳的一般强化学习范式中，输入不存在。这个问题可能导致“灾难性干扰”和性能崩溃的现象。在本文中，我们提出智商，即干涉意识深度Q学习，以减轻单任务深度加固学习中的灾难性干扰。具体来说，我们求助于在线聚类，以实现在线上下文部门，以及一个多头网络和一个知识蒸馏正规化术语，用于保留学习上下文的政策。与现有方法相比，智商基于深Q网络，始终如一地提高稳定性和性能，并通过对经典控制和ATARI任务进行了广泛的实验。该代码可在以下网址公开获取：https：//github.com/sweety-dm/interference-aware-ware-deep-q-learning。

In recent years there have been many successes of using deep representations in reinforcement learning. Still, many of these applications use conventional architectures, such as convolutional networks, LSTMs, or auto-encoders. In this paper, we present a new neural network architecture for model-free reinforcement learning. Our dueling network represents two separate estimators: one for the state value function and one for the state-dependent action advantage function. The main benefit of this factoring is to generalize learning across actions without imposing any change to the underlying reinforcement learning algorithm. Our results show that this architecture leads to better policy evaluation in the presence of many similar-valued actions. Moreover, the dueling architecture enables our RL agent to outperform the state-of-the-art on the Atari 2600 domain.

Deep Q-Network（DQN）算法解决的大规模实践工作表明，随机政策尽管简单，但最常用的探索方法是最常用的探索方法。但是，大多数现有的随机探索方法要么探索新的动作，无论Q值如何，要么不可避免地将偏见引入学习过程中，以将抽样与Q值搭配。在本文中，我们提出了一种新颖的偏好指导$ \ epsilon $ greedy Exploration算法，该算法可以在不引入其他偏见的情况下根据Q值的Q值有效地学习动作分布。具体而言，我们设计了一个由两个分支组成的双重体系结构，其中一个是DQN的副本，即Q Branch。我们称为首选项分支的另一个分支，了解了DQN隐式所遵循的动作偏好。从理论上讲，我们证明了策略改进定理适用于首选项指导的$ \ epsilon $ greedy策略，并在实验上表明，推断的动作偏好分布与相应的Q值的景观保持一致。因此，偏好引导的$ \ epsilon $ - 秘密探索激励DQN代理采取多种操作，即可以更频繁地采样较大的Q值的行动，而使用较小的Q值的行动仍然可以探索，因此仍有机会。鼓励探索。我们在九个不同的环境中使用四个众所周知的DQN变体评估了提出的方法。广泛的结果证实了我们提出的方法在性能和收敛速度方面的优势。索引术语 - 偏好引导的探索，随机政策，数据效率，深度强化学习，深度Q学习。

在这项工作中，我们提出并评估了一种新的增强学习方法，紧凑体验重放（编者），它使用基于相似转换集的复发的预测目标值的时间差异学习，以及基于两个转换的经验重放的新方法记忆。我们的目标是减少在长期累计累计奖励的经纪人培训所需的经验。它与强化学习的相关性与少量观察结果有关，即它需要实现类似于文献中的相关方法获得的结果，这通常需要数百万视频框架来培训ATARI 2600游戏。我们举报了在八个挑战街机学习环境（ALE）挑战游戏中，为仅10万帧的培训试验和大约25,000次迭代的培训试验中报告了培训试验。我们还在与基线的同一游戏中具有相同的实验协议的DQN代理呈现结果。为了验证从较少数量的观察结果近似于良好的政策，我们还将其结果与从啤酒的基准上呈现的数百万帧中获得的结果进行比较。

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。