由于它们所需的大量集中，最深度增强学习算法的状态是对渐近性能的大量集中的效率低。由哺乳动物海马的启发的episodic加强学习（ERL）算法通常使用扩展的内存系统从过去的事件开始学习，以克服这个样本效率问题。然而，这种内存增强通常用作仅仅是缓冲区，从中绘制了孤立的过去经验，以便以离线方式学习（例如，重播）。这里，我们证明包括从集扩展抽样顺序导出的所获取的内存内容中的偏差来提高弹性控制算法的样本和存储器效率。我们在觅食任务中测试了我们的顺序焦点控制（SEC）模型，以显示存储和使用集成剧集作为事件序列导致更快的学习，与较少的内存要求相反，与隔离的缓冲区相比只有事件。我们还研究了内存约束的影响，忘记了SEC算法的顺序和非顺序版本。此外，我们讨论了类似海马的快速记忆系统如何在哺乳动物大脑中引导慢速皮质和皮质学习习惯的习惯。

钢筋学习的长期目标是建立智能代理，表现出快速学习，灵活地转移适于人类和动物的技能。本文调查了两个框架来解决这些目标的框架：情节控制和继承功能。epiSodic控制是一种认知的灵感方法，依赖于情节内存，是代理经历的基于实例的内存模型。同时，继承者功能和广义政策改进（SF＆GPI）是一个元和传输学习框架，允许学习可以有效地重复使用不同奖励功能的稍后任务的任务的策略。单独地，这两种技术表明令人印象深刻的结果，从而大大提高了样本效率和优雅的重复使用了先前学习的政策。因此，我们概述了两种方法中的两种方法的组合，并经验证明其益处。

通过回顾一封来自情节记忆的过去的经验，可以通过回忆过去的经验来实现钢筋学习的样本效率。我们提出了一种新的基于模型的轨迹的集体记忆，解决了集体控制的当前限制。我们的记忆估计轨迹值，指导代理人朝着良好的政策。基于内存构建，我们通过动态混合控制统一模型的基于动态和习惯学习来构建互补学习模型，进入单个架构。实验表明，我们的模型可以比各种环境中的其他强力加强学习代理更快，更好地学习，包括随机和非马尔可夫环境。

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。

Non-parametric episodic memory can be used to quickly latch onto high-reward experience in reinforcement learning tasks. In contrast to parametric deep reinforcement learning approaches, these methods only need to discover the solution once, and may then repeatedly solve the task. However, episodic control solutions are stored in discrete tables, and this approach has so far only been applied to discrete action space problems. Therefore, this paper introduces Continuous Episodic Control (CEC), a novel non-parametric episodic memory algorithm for sequential decision making in problems with a continuous action space. Results on several sparse-reward continuous control environments show that our proposed method learns faster than state-of-the-art model-free RL and memory-augmented RL algorithms, while maintaining good long-run performance as well. In short, CEC can be a fast approach for learning in continuous control tasks, and a useful addition to parametric RL methods in a hybrid approach as well.

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。

Atari games have been a long-standing benchmark in the reinforcement learning (RL) community for the past decade. This benchmark was proposed to test general competency of RL algorithms. Previous work has achieved good average performance by doing outstandingly well on many games of the set, but very poorly in several of the most challenging games. We propose Agent57, the first deep RL agent that outperforms the standard human benchmark on all 57 Atari games. To achieve this result, we train a neural network which parameterizes a family of policies ranging from very exploratory to purely exploitative. We propose an adaptive mechanism to choose which policy to prioritize throughout the training process. Additionally, we utilize a novel parameterization of the architecture that allows for more consistent and stable learning.

Inspired by the cognitive science theory of the explicit human memory systems, we have modeled an agent with short-term, episodic, and semantic memory systems, each of which is modeled with a knowledge graph. To evaluate this system and analyze the behavior of this agent, we designed and released our own reinforcement learning agent environment, "the Room", where an agent has to learn how to encode, store, and retrieve memories to maximize its return by answering questions. We show that our deep Q-learning based agent successfully learns whether a short-term memory should be forgotten, or rather be stored in the episodic or semantic memory systems. Our experiments indicate that an agent with human-like memory systems can outperform an agent without this memory structure in the environment.

深度强化学习（RL）涉及使用深神经网络（DNN）来做出顺序决策，以最大程度地提高奖励。对于许多任务，由深度RL政策产生的一系列动作顺序对于人类来说可能是漫长而难以理解的。人类解释的一个关键组成部分是选择性，仅叙述关键决定和原因。使深层RL代理具有这种能力，将使他们的产生政策从人的角度更容易理解，并产生一套简洁的指示，以帮助学习未来的代理商。为此，我们使用具有情节内存系统的深度RL代理来识别和叙述策略执行期间的关键决策。我们表明，这些决策形成了一个简短的可读解释，也可以用来以算法独立的方式加快对天真的深度RL代理的学习。

自成立以来，建立在广泛任务中表现出色的普通代理的任务一直是强化学习的重要目标。这个问题一直是对Alarge工作体系的研究的主题，并且经常通过观察Atari 57基准中包含的广泛范围环境的分数来衡量的性能。 Agent57是所有57场比赛中第一个超过人类基准的代理商，但这是以数据效率差的代价，需要实现近800亿帧的经验。以Agent57为起点，我们采用了各种各样的形式，以降低超过人类基线所需的经验200倍。在减少数据制度和Propose有效的解决方案时，我们遇到了一系列不稳定性和瓶颈，以构建更强大，更有效的代理。我们还使用诸如Muesli和Muzero之类的高性能方法证明了竞争性的性能。 TOOUR方法的四个关键组成部分是（1）近似信任区域方法，该方法可以从TheOnline网络中稳定引导，（2）损失和优先级的归一化方案，在学习具有广泛量表的一组值函数时，可以提高鲁棒性， （3）改进的体系结构采用了NFNET的技术技术来利用更深的网络而无需标准化层，并且（4）政策蒸馏方法可使瞬时贪婪的策略加班。

强化学习（RL）为解决各种复杂的决策任务提供了新的机会。但是，现代的RL算法，例如，深Q学习是基于深层神经网络，在Edge设备上运行时的计算成本很高。在本文中，我们提出了QHD，一种高度增强的学习，它模仿了大脑特性，以实现健壮和实时学习。 QHD依靠轻巧的大脑启发模型来学习未知环境中的最佳政策。我们首先建立一个新颖的数学基础和编码模块，该模块将状态行动空间映射到高维空间中。因此，我们开发了一个高维回归模型，以近似Q值函数。 QHD驱动的代理通过比较每个可能动作的Q值来做出决定。我们评估了不同的RL培训批量和本地记忆能力对QHD学习质量的影响。我们的QHD也能够以微小的本地记忆能力在线学习，这与培训批量大小一样小。 QHD通过进一步降低记忆容量和批处理大小来提供实时学习。这使得QHD适用于在边缘环境中高效的增强学习，这对于支持在线和实时学习至关重要。我们的解决方案还支持少量的重播批量大小，与DQN相比，该批量的速度为12.3倍，同时确保质量损失最小。我们的评估显示了实时学习的QHD能力，比最先进的Deep RL算法提供了34.6倍的速度和更高的学习质量。

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.

在复杂的协调问题中，深层合作多智能经纪增强学习（Marl）的高效探索仍然依然存在挑战。在本文中，我们介绍了一种具有奇妙驱动的探索的新型情节多功能钢筋学习，称为EMC。我们利用对流行分解的MARL算法的洞察力“诱导的”个体Q值，即用于本地执行的单个实用程序功能，是本地动作观察历史的嵌入，并且可以捕获因奖励而捕获代理之间的相互作用在集中培训期间的反向化。因此，我们使用单独的Q值的预测误差作为协调勘探的内在奖励，利用集肠内存来利用探索的信息经验来提高政策培训。随着代理商的个人Q值函数的动态捕获了国家的新颖性和其他代理人的影响，我们的内在奖励可以促使对新或有前途的国家的协调探索。我们通过教学实例说明了我们的方法的优势，并展示了在星际争霸II微互动基准中挑战任务的最先进的MARL基础上的其显着优势。

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.

In this paper, we build on advances introduced by the Deep Q-Networks (DQN) approach to extend the multi-objective tabular Reinforcement Learning (RL) algorithm W-learning to large state spaces. W-learning algorithm can naturally solve the competition between multiple single policies in multi-objective environments. However, the tabular version does not scale well to environments with large state spaces. To address this issue, we replace underlying Q-tables with DQN, and propose an addition of W-Networks, as a replacement for tabular weights (W) representations. We evaluate the resulting Deep W-Networks (DWN) approach in two widely-accepted multi-objective RL benchmarks: deep sea treasure and multi-objective mountain car. We show that DWN solves the competition between multiple policies while outperforming the baseline in the form of a DQN solution. Additionally, we demonstrate that the proposed algorithm can find the Pareto front in both tested environments.

机器学习算法中多个超参数的最佳设置是发出大多数可用数据的关键。为此目的，已经提出了几种方法，例如进化策略，随机搜索，贝叶斯优化和启发式拇指规则。在钢筋学习（RL）中，学习代理在与其环境交互时收集的数据的信息内容严重依赖于许多超参数的设置。因此，RL算法的用户必须依赖于基于搜索的优化方法，例如网格搜索或Nelder-Mead单简单算法，这对于大多数R1任务来说是非常效率的，显着减慢学习曲线和离开用户的速度有目的地偏见数据收集的负担。在这项工作中，为了使RL算法更加用户独立，提出了一种使用贝叶斯优化的自主超参数设置的新方法。来自过去剧集和不同的超参数值的数据通过执行行为克隆在元学习水平上使用，这有助于提高最大化获取功能的加强学习变体的有效性。此外，通过紧密地整合在加强学习代理设计中的贝叶斯优化，还减少了收敛到给定任务的最佳策略所需的状态转换的数量。与其他手动调整和基于优化的方法相比，计算实验显示了有希望的结果，这突出了改变算法超级参数来增加所生成数据的信息内容的好处。

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.

我们为政策梯度方法介绍了一种新颖的训练程序，其中用于在飞行中优化强化学习算法的超参数。与其他HyperParameter搜索不同，我们将HyperParameter调度标记为标准的Markov决策过程，并使用epiSodic内存来存储所使用的超参数和培训背景的结果。在任何策略更新步骤中，策略学习者都指的是存储的经验，并自适应地将其学习算法与存储器确定的新的超参数重新配置。这种机制被称为epiSodic政策梯度训练（EPGT），可以联合学习单个运行中的策略和学习算法的封面。连续和离散环境的实验结果证明了利用所提出的方法促进各种政策梯度算法的性能的优点。