我们开发了一个多功能辅助救援学习(MARL)方法,以了解目标跟踪的可扩展控制策略。我们的方法可以处理任意数量的追求者和目标;我们显示出现的任务,该任务包括高达1000追踪跟踪1000个目标。我们使用分散的部分可观察的马尔可夫决策过程框架来模拟追求者作为接受偏见观察(范围和轴承)的代理,了解使用固定的未知政策的目标。注意机制用于参数化代理的价值函数;这种机制允许我们处理任意数量的目标。熵 - 正规的脱助政策RL方法用于培训随机政策,我们讨论如何在追求者之间实现对冲行为,尽管有完全分散的控制执行,但仍然导致合作较弱的合作形式。我们进一步开发了一个掩蔽启发式,允许训练较少的问题,少量追求目标和在更大的问题上执行。进行彻底的仿真实验,消融研究和对现有技术算法的比较,以研究对不同数量的代理和目标性能的方法和鲁棒性的可扩展性。
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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.
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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。
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本文考虑了多智能经纪人强化学习(MARL)任务,代理商在集会结束时获得共享全球奖励。这种奖励的延迟性质影响了代理商在中间时间步骤中评估其行动质量的能力。本文侧重于开发学习焦点奖励的时间重新分布的方法,以获得密集奖励信号。解决这些MARL问题需要解决两个挑战:识别(1)沿着集发作(沿时间)的长度相对重要性,以及(2)在任何单一时间步骤(代理商中)的相对重要性。在本文中,我们介绍了奖励中的奖励再分配,在整容多智能体加固学习(Arel)中奖励再分配,以解决这两个挑战。 Arel使用注意机制来表征沿着轨迹(时间关注)对状态转换的动作的影响,以及每个代理在每个时间步骤(代理人注意)的影响。 Arel预测的重新分配奖励是密集的,可以与任何给定的MARL算法集成。我们评估了粒子世界环境的具有挑战性的任务和星际争霸多功能挑战。 arel导致粒子世界的奖励较高,并改善星际争端的胜利率与三个最先进的奖励再分配方法相比。我们的代码可在https://github.com/baicenxiao/arel获得。
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Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in singleagent settings. We present an actor-critic algorithm that trains decentralized policies in multiagent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multiagent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, as well as settings that do not provide global states, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems.
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Starcraft II多代理挑战(SMAC)被创建为合作多代理增强学习(MARL)的具有挑战性的基准问题。 SMAC专注于星际争霸微管理的问题,并假设每个单元都由独立行动并仅具有本地信息的学习代理人单独控制;假定通过分散执行(CTDE)进行集中培训。为了在SMAC中表现良好,MARL算法必须处理多机构信贷分配和联合行动评估的双重问题。本文介绍了一种新的体系结构Transmix,这是一个基于变压器的联合行动值混合网络,与其他最先进的合作MARL解决方案相比,我们显示出高效且可扩展的。 Transmix利用变形金刚学习更丰富的混合功能的能力来结合代理的个人价值函数。它与以前的SMAC场景上的工作相当,并且在困难场景上胜过其他技术,以及被高斯噪音损坏的场景以模拟战争的雾。
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政策梯度方法在多智能体增强学习中变得流行,但由于存在环境随机性和探索代理(即非公平性​​),它们遭受了高度的差异,这可能因信用分配难度而受到困扰。结果,需要一种方法,该方法不仅能够有效地解决上述两个问题,而且需要足够强大地解决各种任务。为此,我们提出了一种新的多代理政策梯度方法,称为强大的本地优势(ROLA)演员 - 评论家。 Rola允许每个代理人将个人动作值函数作为当地评论家,以及通过基于集中评论家的新型集中培训方法来改善环境不良。通过使用此本地批评,每个代理都计算基准,以减少对其策略梯度估计的差异,这导致含有其他代理的预期优势动作值,这些选项可以隐式提高信用分配。我们在各种基准测试中评估ROLA,并在许多最先进的多代理政策梯度算法上显示其鲁棒性和有效性。
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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。
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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.
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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。
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Many real-world problems, such as network packet routing and the coordination of autonomous vehicles, are naturally modelled as cooperative multi-agent systems. There is a great need for new reinforcement learning methods that can efficiently learn decentralised policies for such systems. To this end, we propose a new multi-agent actor-critic method called counterfactual multi-agent (COMA) policy gradients. COMA uses a centralised critic to estimate the Q-function and decentralised actors to optimise the agents' policies. In addition, to address the challenges of multi-agent credit assignment, it uses a counterfactual baseline that marginalises out a single agent's action, while keeping the other agents' actions fixed. COMA also uses a critic representation that allows the counterfactual baseline to be computed efficiently in a single forward pass. We evaluate COMA in the testbed of StarCraft unit micromanagement, using a decentralised variant with significant partial observability. COMA significantly improves average performance over other multi-agent actorcritic methods in this setting, and the best performing agents are competitive with state-of-the-art centralised controllers that get access to the full state.
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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.
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尽管近年来在多机构增强学习(MARL)方面取得了重大进展,但复杂领域的协调仍然是一个挑战。 MARL的工作通常专注于解决代理与环境中所有其他代理和实体互动的任务;但是,我们观察到现实世界任务通常由几个局部代理相互作用(子任务)的几个隔离实例组成,并且每个代理都可以有意义地专注于一个子任务,以排除环境中其他所有内容。在这些综合任务中,成功的策略通常可以分解为两个决策级别:代理人分配给特定的子任务,并且每个代理人仅针对其指定的子任务有效地采取行动。这种分解的决策提供了强烈的结构感应偏见,大大降低了代理观察空间,并鼓励在训练期间重复使用和组成子任务特异性策略,而不是将子任务的每个新组成视为独特的。我们介绍了ALMA,这是一种利用这些结构化任务的一般学习方法。阿尔玛同时学习高级子任务分配策略和低级代理政策。我们证明,阿尔玛(Alma)在许多具有挑战性的环境中学习了复杂的协调行为,表现优于强大的基准。 Alma的模块化还使其能够更好地概括为新的环境配置。最后,我们发现,尽管ALMA可以整合受过训练的分配和行动策略,但最佳性能仅通过共同训练所有组件才能获得。我们的代码可从https://github.com/shariqiqbal2810/alma获得
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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。
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未来的互联网涉及几种新兴技术,例如5G和5G网络,车辆网络,无人机(UAV)网络和物联网(IOT)。此外,未来的互联网变得异质并分散了许多相关网络实体。每个实体可能需要做出本地决定,以在动态和不确定的网络环境下改善网络性能。最近使用标准学习算法,例如单药强化学习(RL)或深入强化学习(DRL),以使每个网络实体作为代理人通过与未知环境进行互动来自适应地学习最佳决策策略。但是,这种算法未能对网络实体之间的合作或竞争进行建模,而只是将其他实体视为可能导致非平稳性问题的环境的一部分。多机构增强学习(MARL)允许每个网络实体不仅观察环境,还可以观察其他实体的政策来学习其最佳政策。结果,MAL可以显着提高网络实体的学习效率,并且最近已用于解决新兴网络中的各种问题。在本文中,我们因此回顾了MAL在新兴网络中的应用。特别是,我们提供了MARL的教程,以及对MARL在下一代互联网中的应用进行全面调查。特别是,我们首先介绍单代机Agent RL和MARL。然后,我们回顾了MAL在未来互联网中解决新兴问题的许多应用程序。这些问题包括网络访问,传输电源控制,计算卸载,内容缓存,数据包路由,无人机网络的轨迹设计以及网络安全问题。
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Collaborative autonomous multi-agent systems covering a specified area have many potential applications, such as UAV search and rescue, forest fire fighting, and real-time high-resolution monitoring. Traditional approaches for such coverage problems involve designing a model-based control policy based on sensor data. However, designing model-based controllers is challenging, and the state-of-the-art classical control policy still exhibits a large degree of suboptimality. In this paper, we present a reinforcement learning (RL) approach for the multi-agent coverage problem involving agents with second-order dynamics. Our approach is based on the Multi-Agent Proximal Policy Optimization Algorithm (MAPPO). To improve the stability of the learning-based policy and efficiency of exploration, we utilize an imitation loss based on the state-of-the-art classical control policy. Our trained policy significantly outperforms the state-of-the-art. Our proposed network architecture includes incorporation of self attention, which allows a single-shot domain transfer of the trained policy to a large variety of domain shapes and number of agents. We demonstrate our proposed method in a variety of simulated experiments.
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We consider the problem of multi-agent navigation and collision avoidance when observations are limited to the local neighborhood of each agent. We propose InforMARL, a novel architecture for multi-agent reinforcement learning (MARL) which uses local information intelligently to compute paths for all the agents in a decentralized manner. Specifically, InforMARL aggregates information about the local neighborhood of agents for both the actor and the critic using a graph neural network and can be used in conjunction with any standard MARL algorithm. We show that (1) in training, InforMARL has better sample efficiency and performance than baseline approaches, despite using less information, and (2) in testing, it scales well to environments with arbitrary numbers of agents and obstacles.
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在本文中,我们认为合作的多代理强化学习(MARL)具有稀疏的奖励。为了解决这个问题,我们提出了一种名为Maser:MARL的新方法,并具有从经验重播缓冲区产生的子目标。在广泛使用的集中式培训的假设下,通过分散执行和对MARL的Q值分解的一致性,Maser通过考虑单个Q值和总Q值来自动为多个代理人生成适当的子目标。然后,Maser根据与Q学习相关的可行表示为每个代理设计个人固有奖励,以便代理人达到其子目标,同时最大化联合行动值。数值结果表明,与其他最先进的MARL算法相比,Maser的表现明显优于Starcraft II微管理基准。
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We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multiagent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.
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大型人口系统的分析和控制对研究和工程的各个领域引起了极大的兴趣,从机器人群的流行病学到经济学和金融。一种越来越流行和有效的方法来实现多代理系统中的顺序决策,这是通过多机构增强学习,因为它允许对高度复杂的系统进行自动和无模型的分析。但是,可伸缩性的关键问题使控制和增强学习算法的设计变得复杂,尤其是在具有大量代理的系统中。尽管强化学习在许多情况下都发现了经验成功,但许多代理商的问题很快就变得棘手了,需要特别考虑。在这项调查中,我们将阐明当前的方法,以通过多代理强化学习以及通过诸如平均场游戏,集体智能或复杂的网络理论等研究领域进行仔细理解和分析大型人口系统。这些经典独立的主题领域提供了多种理解或建模大型人口系统的方法,这可能非常适合将来的可拖动MARL算法制定。最后,我们调查了大规模控制的潜在应用领域,并确定了实用系统中学习算法的富有成果的未来应用。我们希望我们的调查可以为理论和应用科学的初级和高级研究人员提供洞察力和未来的方向。
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