加固学习在机器学习中推动了令人印象深刻的进步。同时，量子增强机学习算法使用量子退火的底层划伤。最近，已经提出了一种组合两个范例的多代理强化学习（MARL）架构。这种新的算法利用Q值近似的量子Boltzmann机器（QBMS）在收敛所需的时间步长方面具有优于常规的深度增强学习。但是，该算法仅限于单代理和小型2x2多代理网格域。在这项工作中，我们提出了对原始概念的延伸，以解决更具挑战性问题。类似于Classic DQN，我们添加了重播缓冲区的体验，并使用不同的网络来估计目标和策略值。实验结果表明，学习变得更加稳定，使代理能够在具有更高复杂性的网格域中找到最佳策略。此外，我们还评估参数共享如何影响多代理域中的代理行为。量子采样证明是一种有希望的加强学习任务的方法，但目前受到QPU尺寸的限制，因此通过输入和Boltzmann机器的大小。

In multi-agent reinforcement learning (MARL), many popular methods, such as VDN and QMIX, are susceptible to a critical multi-agent pathology known as relative overgeneralization (RO), which arises when the optimal joint action's utility falls below that of a sub-optimal joint action in cooperative tasks. RO can cause the agents to get stuck into local optima or fail to solve tasks that require significant coordination between agents within a given timestep. Recent value-based MARL algorithms such as QPLEX and WQMIX can overcome RO to some extent. However, our experimental results show that they can still fail to solve cooperative tasks that exhibit strong RO. In this work, we propose a novel approach called curriculum learning for relative overgeneralization (CURO) to better overcome RO. To solve a target task that exhibits strong RO, in CURO, we first fine-tune the reward function of the target task to generate source tasks that are tailored to the current ability of the learning agent and train the agent on these source tasks first. Then, to effectively transfer the knowledge acquired in one task to the next, we use a novel transfer learning method that combines value function transfer with buffer transfer, which enables more efficient exploration in the target task. We demonstrate that, when applied to QMIX, CURO overcomes severe RO problem and significantly improves performance, yielding state-of-the-art results in a variety of cooperative multi-agent tasks, including the challenging StarCraft II micromanagement benchmarks.

最先进的多机构增强学习（MARL）方法为各种复杂问题提供了有希望的解决方案。然而，这些方法都假定代理执行同步的原始操作执行，因此它们不能真正可扩展到长期胜利的真实世界多代理/机器人任务，这些任务固有地要求代理/机器人以异步的理由，涉及有关高级动作选择的理由。不同的时间。宏观行动分散的部分可观察到的马尔可夫决策过程（MACDEC-POMDP）是在完全合作的多代理任务中不确定的异步决策的一般形式化。在本论文中，我们首先提出了MacDec-Pomdps的一组基于价值的RL方法，其中允许代理在三个范式中使用宏观成果功能执行异步学习和决策：分散学习和控制，集中学习，集中学习和控制，以及分散执行的集中培训（CTDE）。在上述工作的基础上，我们在三个训练范式下制定了一组基于宏观行动的策略梯度算法，在该训练范式下，允许代理以异步方式直接优化其参数化策略。我们在模拟和真实的机器人中评估了我们的方法。经验结果证明了我们在大型多代理问题中的方法的优势，并验证了我们算法在学习具有宏观actions的高质量和异步溶液方面的有效性。

我们将记住和忘记的经验重播（Ref-ER）算法扩展到多代理增强学习（MARL）。参考器被证明超过了最先进的算法状态，以连续控制从OpenAI健身房到复杂的流体流动。在MARL中，代理之间的依赖项包括在州值估计器中，环境动力学是通过参考文献使用的重要性权重对其建模的。在协作环境中，当使用个人奖励估算值时，我们发现最佳性能，并且我们忽略了其他动作对过渡图的影响。我们基准在斯坦福大学智能系统实验室（SISL）环境中进行参考文献的性能。我们发现，采用单个馈送前馈神经网络来进行策略和参考文献中的价值函数，优于依靠复杂的神经网络体系结构的最先进的算法状态。

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。

许多现实世界的应用程序都可以作为多机构合作问题进行配置，例如网络数据包路由和自动驾驶汽车的协调。深入增强学习（DRL）的出现为通过代理和环境的相互作用提供了一种有前途的多代理合作方法。但是，在政策搜索过程中，传统的DRL解决方案遭受了多个代理具有连续动作空间的高维度。此外，代理商政策的动态性使训练非平稳。为了解决这些问题，我们建议采用高级决策和低水平的个人控制，以进行有效的政策搜索，提出一种分层增强学习方法。特别是，可以在高级离散的动作空间中有效地学习多个代理的合作。同时，低水平的个人控制可以减少为单格强化学习。除了分层增强学习外，我们还建议对手建模网络在学习过程中对其他代理的政策进行建模。与端到端的DRL方法相反，我们的方法通过以层次结构将整体任务分解为子任务来降低学习的复杂性。为了评估我们的方法的效率，我们在合作车道变更方案中进行了现实世界中的案例研究。模拟和现实世界实验都表明我们的方法在碰撞速度和收敛速度中的优越性。

Though transfer learning is promising to increase the learning efficiency, the existing methods are still subject to the challenges from long-horizon tasks, especially when expert policies are sub-optimal and partially useful. Hence, a novel algorithm named EASpace (Enhanced Action Space) is proposed in this paper to transfer the knowledge of multiple sub-optimal expert policies. EASpace formulates each expert policy into multiple macro actions with different execution time period, then integrates all macro actions into the primitive action space directly. Through this formulation, the proposed EASpace could learn when to execute which expert policy and how long it lasts. An intra-macro-action learning rule is proposed by adjusting the temporal difference target of macro actions to improve the data efficiency and alleviate the non-stationarity issue in multi-agent settings. Furthermore, an additional reward proportional to the execution time of macro actions is introduced to encourage the environment exploration via macro actions, which is significant to learn a long-horizon task. Theoretical analysis is presented to show the convergence of the proposed algorithm. The efficiency of the proposed algorithm is illustrated by a grid-based game and a multi-agent pursuit problem. The proposed algorithm is also implemented to real physical systems to justify its effectiveness.

This work considers the problem of learning cooperative policies in complex, partially observable domains without explicit communication. We extend three classes of single-agent deep reinforcement learning algorithms based on policy gradient, temporal-difference error, and actor-critic methods to cooperative multi-agent systems. We introduce a set of cooperative control tasks that includes tasks with discrete and continuous actions, as well as tasks that involve hundreds of agents. The three approaches are evaluated against each other using different neural architectures, training procedures, and reward structures. Using deep reinforcement learning with a curriculum learning scheme, our approach can solve problems that were previously considered intractable by most multi-agent reinforcement learning algorithms. We show that policy gradient methods tend to outperform both temporal-difference and actor-critic methods when using feed-forward neural architectures. We also show that recurrent policies, while more difficult to train, outperform feed-forward policies on our evaluation tasks.

使用规划算法和神经网络模型的基于模型的强化学习范例最近在不同的应用中实现了前所未有的结果，导致现在被称为深度增强学习的内容。这些代理非常复杂，涉及多个组件，可能会为研究产生挑战的因素。在这项工作中，我们提出了一个适用于这些类型代理的新模块化软件架构，以及一组建筑块，可以轻松重复使用和组装，以构建基于模型的增强学习代理。这些构建块包括规划算法，策略和丢失功能。我们通过将多个这些构建块组合实现和测试经过针对三种不同的测试环境的代理来说明这种架构的使用：Cartpole，Minigrid和Tictactoe。在我们的实施中提供的一个特定的规划算法，并且以前没有用于加强学习，我们称之为Imperage Minimax，在三个测试环境中取得了良好的效果。用这种架构进行的实验表明，规划算法，政策和损失函数的最佳组合依赖性严重问题。该结果提供了证据表明，拟议的架构是模块化和可重复使用的，对想要研究新环境和技术的强化学习研究人员有用。

Batch reinforcement learning is a subfield of dynamic programming-based reinforcement learning. Originally defined as the task of learning the best possible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the efficient use of collected data and the stability of the learning process, this research area has attracted a lot of attention recently. In this chapter, we introduce the basic principles and the theory behind batch reinforcement learning, describe the most important algorithms, exemplarily discuss ongoing research within this field, and briefly survey real-world applications of batch reinforcement learning.

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.

安全是空中交通时的主要问题。通过成对分离最小值确保无人驾驶飞机（无人机）之间的飞行安全性，利用冲突检测和分辨方法。现有方法主要处理成对冲突，但由于交通密度的预期增加，可能会发生两个以上的无人机的遇到。在本文中，我们将多UAV冲突解决模型作为多功能加强学习问题。我们实现了一种基于图形神经网络的算法，配合代理可以与共同生成分辨率的操作进行通信。该模型在具有3和4个当前代理的情况下进行评估。结果表明，代理商能够通过合作策略成功解决多UV冲突。

在实际应用中，尽管这种知识对于确定反应性控制系统与环境的精确相互作用很重要，但我们很少可以完全观察到系统的环境。因此，我们提出了一种在部分可观察到的环境中进行加固学习方法（RL）。在假设环境的行为就像是可观察到的马尔可夫决策过程，但我们对其结构或过渡概率不了解。我们的方法将Q学习与IOALERGIA结合在一起，这是一种学习马尔可夫决策过程（MDP）的方法。通过从RL代理的发作中学习环境的MDP模型，我们可以在不明确的部分可观察到的域中启用RL，而没有明确的记忆，以跟踪以前的相互作用，以处理由部分可观察性引起的歧义。相反，我们通过模拟学习环境模型上的新体验以跟踪探索状态，以抽象环境状态的形式提供其他观察结果。在我们的评估中，我们报告了方法的有效性及其有希望的性能，与六种具有复发性神经网络和固定记忆的最先进的深度RL技术相比。

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.

将深度强化学习（DRL）扩展到多代理领域的研究已经解决了许多复杂的问题，并取得了重大成就。但是，几乎所有这些研究都只关注离散或连续的动作空间，而且很少有作品曾经使用过多代理的深度强化学习来实现现实世界中的环境问题，这些问题主要具有混合动作空间。因此，在本文中，我们提出了两种算法：深层混合软性角色批评（MAHSAC）和多代理混合杂种深层确定性政策梯度（MAHDDPG）来填补这一空白。这两种算法遵循集中式培训和分散执行（CTDE）范式，并可以解决混合动作空间问题。我们的经验在多代理粒子环境上运行，这是一个简单的多代理粒子世界，以及一些基本的模拟物理。实验结果表明，这些算法具有良好的性能。

Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation-learning abilities of deep neural networks. However, large centralized approaches quickly become infeasible as the number of agents scale, and fully decentralized approaches can miss important opportunities for information sharing and coordination. Furthermore, not all agents are equal -- in some cases, individual agents may not even have the ability to send communication to other agents or explicitly model other agents. This paper considers the case where there is a single, powerful, \emph{central agent} that can observe the entire observation space, and there are multiple, low-powered \emph{local agents} that can only receive local observations and are not able to communicate with each other. The central agent's job is to learn what message needs to be sent to different local agents based on the global observations, not by centrally solving the entire problem and sending action commands, but by determining what additional information an individual agent should receive so that it can make a better decision. In this work we present our MARL algorithm \algo, describe where it would be most applicable, and implement it in the cooperative navigation and multi-agent walker domains. Empirical results show that 1) learned communication does indeed improve system performance, 2) results generalize to heterogeneous local agents, and 3) results generalize to different reward structures.

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.

A long-standing challenge in artificial intelligence is lifelong learning. In lifelong learning, many tasks are presented in sequence and learners must efficiently transfer knowledge between tasks while avoiding catastrophic forgetting over long lifetimes. On these problems, policy reuse and other multi-policy reinforcement learning techniques can learn many tasks. However, they can generate many temporary or permanent policies, resulting in memory issues. Consequently, there is a need for lifetime-scalable methods that continually refine a policy library of a pre-defined size. This paper presents a first approach to lifetime-scalable policy reuse. To pre-select the number of policies, a notion of task capacity, the maximal number of tasks that a policy can accurately solve, is proposed. To evaluate lifetime policy reuse using this method, two state-of-the-art single-actor base-learners are compared: 1) a value-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent Q-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy Optimisation (PPO) with or without Long Short-Term Memory layer. By selecting the number of policies based on task capacity, D(R)QN achieves near-optimal performance with 6 policies in a 27-task MDP domain and 9 policies in an 18-task POMDP domain; with fewer policies, catastrophic forgetting and negative transfer are observed. Due to slow, monotonic improvement, PPO requires fewer policies, 1 policy for the 27-task domain and 4 policies for the 18-task domain, but it learns the tasks with lower accuracy than D(R)QN. These findings validate lifetime-scalable policy reuse and suggest using D(R)QN for larger and PPO for smaller library sizes.

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。

分布式多智能经纪增强学习（Marl）算法最近引起了兴趣激增，主要是由于深神经网络（DNN）的最新进步。由于利用固定奖励模型来学习基础值函数，传统的基于模型（MB）或无模型（MF）RL算法不可直接适用于MARL问题。虽然涉及单一代理时，基于DNN的解决方案完全良好地表现出，但是这种方法无法完全推广到MARL问题的复杂性。换句话说，尽管最近的基于DNN的DNN用于多种子体环境的方法取得了卓越的性能，但它们仍然容易出现过度，对参数选择的高敏感性，以及样本低效率。本文提出了多代理自适应Kalman时间差（MAK-TD）框架及其继任者表示的基于代表的变体，称为MAK-SR。直观地说，主要目标是利用卡尔曼滤波（KF）的独特特征，如不确定性建模和在线二阶学习。提议的MAK-TD / SR框架考虑了与高维多算法环境相关联的动作空间的连续性，并利用卡尔曼时间差（KTD）来解决参数不确定性。通过利用KTD框架，SR学习过程被建模到过滤问题，其中径向基函数（RBF）估计器用于将连续空间编码为特征向量。另一方面，对于学习本地化奖励功能，我们求助于多种模型自适应估计（MMAE），处理缺乏关于观察噪声协方差和观察映射功能的先前知识。拟议的MAK-TD / SR框架通过多个实验进行评估，该实验通过Openai Gym Marl基准实施。