We consider an offline reinforcement learning (RL) setting where the agent need to learn from a dataset collected by rolling out multiple behavior policies. There are two challenges for this setting: 1) The optimal trade-off between optimizing the RL signal and the behavior cloning (BC) signal changes on different states due to the variation of the action coverage induced by different behavior policies. Previous methods fail to handle this by only controlling the global trade-off. 2) For a given state, the action distribution generated by different behavior policies may have multiple modes. The BC regularizers in many previous methods are mean-seeking, resulting in policies that select out-of-distribution (OOD) actions in the middle of the modes. In this paper, we address both challenges by using adaptively weighted reverse Kullback-Leibler (KL) divergence as the BC regularizer based on the TD3 algorithm. Our method not only trades off the RL and BC signals with per-state weights (i.e., strong BC regularization on the states with narrow action coverage, and vice versa) but also avoids selecting OOD actions thanks to the mode-seeking property of reverse KL. Empirically, our algorithm can outperform existing offline RL algorithms in the MuJoCo locomotion tasks with the standard D4RL datasets as well as the mixed datasets that combine the standard datasets.

离线增强学习（RL）旨在使用先前收集的静态数据集学习最佳策略，是RL的重要范式。由于函数近似错误在分布外动作上的功能近似错误，因此在此任务上的标准RL方法通常会表现较差。尽管已经提出了各种正规化方法来减轻此问题，但它们通常受到表达有限的策略类别的限制，有时会导致次优的解决方案。在本文中，我们提出了利用条件扩散模型作为行为克隆和策略正则化的高度表达政策类别的扩散-QL。在我们的方法中，我们学习了一个动作值函数，并在有条件扩散模型的训练损失中添加了最大化动作值的术语，这导致损失寻求接近行为政策的最佳动作。我们展示了基于扩散模型的策略的表现力以及在扩散模型下的行为克隆和策略改进的耦合都有助于扩散-QL的出色性能。我们在具有多模式行为策略的简单2D强盗示例中说明了我们的方法和先前的工作。然后，我们证明我们的方法可以在离线RL的大多数D4RL基准任务上实现最先进的性能。

依赖于太多的实验来学习良好的行动，目前的强化学习（RL）算法在现实世界的环境中具有有限的适用性，这可能太昂贵，无法探索探索。我们提出了一种批量RL算法，其中仅使用固定的脱机数据集来学习有效策略，而不是与环境的在线交互。批量RL中的有限数据产生了在培训数据中不充分表示的状态/行动的价值估计中的固有不确定性。当我们的候选政策从生成数据的候选政策发散时，这导致特别严重的外推。我们建议通过两个直接的惩罚来减轻这个问题：减少这种分歧的政策限制和减少过于乐观估计的价值约束。在全面的32个连续动作批量RL基准测试中，我们的方法对最先进的方法进行了比较，无论如何收集离线数据如何。

Off-policy reinforcement learning aims to leverage experience collected from prior policies for sample-efficient learning. However, in practice, commonly used off-policy approximate dynamic programming methods based on Q-learning and actor-critic methods are highly sensitive to the data distribution, and can make only limited progress without collecting additional on-policy data. As a step towards more robust off-policy algorithms, we study the setting where the off-policy experience is fixed and there is no further interaction with the environment. We identify bootstrapping error as a key source of instability in current methods. Bootstrapping error is due to bootstrapping from actions that lie outside of the training data distribution, and it accumulates via the Bellman backup operator. We theoretically analyze bootstrapping error, and demonstrate how carefully constraining action selection in the backup can mitigate it. Based on our analysis, we propose a practical algorithm, bootstrapping error accumulation reduction (BEAR). We demonstrate that BEAR is able to learn robustly from different off-policy distributions, including random and suboptimal demonstrations, on a range of continuous control tasks.

离线增强学习（RL）定义了从静态记录数据集学习的任务，而无需与环境不断交互。学识渊博的政策与行为政策之间的分配变化使得价值函数必须保持保守，以使分布（OOD）的动作不会被严重高估。但是，现有的方法，对看不见的行为进行惩罚或与行为政策进行正规化，太悲观了，这抑制了价值功能的概括并阻碍了性能的提高。本文探讨了温和但足够的保守主义，可以在线学习，同时不损害概括。我们提出了轻度保守的Q学习（MCQ），其中通过分配了适当的伪Q值来积极训练OOD。从理论上讲，我们表明MCQ诱导了至少与行为策略的行为，并且对OOD行动不会发生错误的高估。 D4RL基准测试的实验结果表明，与先前的工作相比，MCQ取得了出色的性能。此外，MCQ在从离线转移到在线时显示出卓越的概括能力，并明显胜过基准。

在离线强化学习（离线RL）中，主要挑战之一是处理学习策略与给定数据集之间的分布转变。为了解决这个问题，最近的离线RL方法试图引入保守主义偏见，以鼓励在高信心地区学习。无模型方法使用保守的正常化或特殊网络结构直接对策略或价值函数学习进行这样的偏见，但它们约束的策略搜索限制了脱机数据集之外的泛化。基于模型的方法使用保守量量化学习前瞻性动态模型，然后生成虚构的轨迹以扩展脱机数据集。然而，由于离线数据集中的有限样本，保守率量化通常在支撑区域内遭受全面化。不可靠的保守措施将误导基于模型的想象力，以不受欢迎的地区，导致过多的行为。为了鼓励更多的保守主义，我们提出了一种基于模型的离线RL框架，称为反向离线模型的想象（ROMI）。我们与新颖的反向策略结合使用逆向动力学模型，该模型可以生成导致脱机数据集中的目标目标状态的卷展栏。这些反向的想象力提供了无通知的数据增强，以便无模型策略学习，并使远程数据集的保守概括。 ROMI可以有效地与现成的无模型算法组合，以实现基于模型的概括，具有适当的保守主义。经验结果表明，我们的方法可以在离线RL基准任务中产生更保守的行为并实现最先进的性能。

离线强化学习（RL）任务要求代理从预先收集的数据集中学习，没有与环境进行进一步的交互。尽管有可能超越行为政策，但基于RL的方法通常是不切实际的，因为培训不稳定并引导外推错误，这始终需要通过在线评估进行仔细的超参数调整。相比之下，离线模仿学习（IL）没有这样的问题，因为它直接在不估计值函数的情况下直接了解策略。然而，IL通常限制在行为政策的能力，并且倾向于从政策混合收集的数据集中学习平庸行为。在本文中，我们的目标是利用IL但缓解这种缺点。观察行为克隆能够使用较少的数据模仿邻近的策略，我们提出\ Textit {课程脱机仿制学习（线圈）}，它利用具有更高回报的自适应邻近策略的体验挑选策略，并提高了当前策略沿课程阶段。在连续控制基准测试中，我们将线圈与基于仿制的和基于RL的方法进行比较，表明它不仅避免了在混合数据集上学习平庸行为，而且甚至与最先进的离线RL方法竞争。

Behavioural cloning (BC) is a commonly used imitation learning method to infer a sequential decision-making policy from expert demonstrations. However, when the quality of the data is not optimal, the resulting behavioural policy also performs sub-optimally once deployed. Recently, there has been a surge in offline reinforcement learning methods that hold the promise to extract high-quality policies from sub-optimal historical data. A common approach is to perform regularisation during training, encouraging updates during policy evaluation and/or policy improvement to stay close to the underlying data. In this work, we investigate whether an offline approach to improving the quality of the existing data can lead to improved behavioural policies without any changes in the BC algorithm. The proposed data improvement approach - Trajectory Stitching (TS) - generates new trajectories (sequences of states and actions) by `stitching' pairs of states that were disconnected in the original data and generating their connecting new action. By construction, these new transitions are guaranteed to be highly plausible according to probabilistic models of the environment, and to improve a state-value function. We demonstrate that the iterative process of replacing old trajectories with new ones incrementally improves the underlying behavioural policy. Extensive experimental results show that significant performance gains can be achieved using TS over BC policies extracted from the original data. Furthermore, using the D4RL benchmarking suite, we demonstrate that state-of-the-art results are obtained by combining TS with two existing offline learning methodologies reliant on BC, model-based offline planning (MBOP) and policy constraint (TD3+BC).

强化学习（RL）已在域中展示有效，在域名可以通过与其操作环境进行积极互动来学习政策。但是，如果我们将RL方案更改为脱机设置，代理商只能通过静态数据集更新其策略，其中脱机强化学习中的一个主要问题出现，即分配转移。我们提出了一种悲观的离线强化学习（PESSORL）算法，以主动引导代理通过操纵价值函数来恢复熟悉的区域。我们专注于由分销外（OOD）状态引起的问题，并且故意惩罚训练数据集中不存在的状态的高值，以便学习的悲观值函数下限界限状态空间内的任何位置。我们在各种基准任务中评估Pessorl算法，在那里我们表明我们的方法通过明确处理OOD状态，与这些方法仅考虑ood行动时，我们的方法通过明确处理OOD状态。

我们研究了离线模仿学习（IL）的问题，在该问题中，代理商旨在学习最佳的专家行为政策，而无需其他在线环境互动。取而代之的是，该代理来自次优行为的补充离线数据集。解决此问题的先前工作要么要求专家数据占据离线数据集的大部分比例，要么需要学习奖励功能并在以后执行离线加强学习（RL）。在本文中，我们旨在解决问题，而无需进行奖励学习和离线RL培训的其他步骤，当时示范包含大量次优数据。基于行为克隆（BC），我们引入了一个额外的歧视者，以区分专家和非专家数据。我们提出了一个合作框架，以增强这两个任务的学习，基于此框架，我们设计了一种新的IL算法，其中歧视者的输出是BC损失的权重。实验结果表明，与基线算法相比，我们提出的算法可获得更高的回报和更快的训练速度。

Effectively leveraging large, previously collected datasets in reinforcement learning (RL) is a key challenge for large-scale real-world applications. Offline RL algorithms promise to learn effective policies from previously-collected, static datasets without further interaction. However, in practice, offline RL presents a major challenge, and standard off-policy RL methods can fail due to overestimation of values induced by the distributional shift between the dataset and the learned policy, especially when training on complex and multi-modal data distributions. In this paper, we propose conservative Q-learning (CQL), which aims to address these limitations by learning a conservative Q-function such that the expected value of a policy under this Q-function lower-bounds its true value. We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees. In practice, CQL augments the standard Bellman error objective with a simple Q-value regularizer which is straightforward to implement on top of existing deep Q-learning and actor-critic implementations. On both discrete and continuous control domains, we show that CQL substantially outperforms existing offline RL methods, often learning policies that attain 2-5 times higher final return, especially when learning from complex and multi-modal data distributions.Preprint. Under review.

在没有高保真模拟环境的情况下，学习有效的加强学习（RL）政策可以解决现实世界中的复杂任务。在大多数情况下，我们只有具有简化动力学的不完善的模拟器，这不可避免地导致RL策略学习中的SIM到巨大差距。最近出现的离线RL领域为直接从预先收集的历史数据中学习政策提供了另一种可能性。但是，为了达到合理的性能，现有的离线RL算法需要不切实际的离线数据，并具有足够的州行动空间覆盖范围进行培训。这提出了一个新问题：是否有可能通过在线RL中的不完美模拟器中的离线RL中的有限数据中的学习结合到无限制的探索，以解决两种方法的缺点？在这项研究中，我们提出了动态感知的混合离线和对线增强学习（H2O）框架，以为这个问题提供肯定的答案。 H2O引入了动态感知的政策评估方案，该方案可以自适应地惩罚Q函数在模拟的状态行动对上具有较大的动态差距，同时也允许从固定的现实世界数据集中学习。通过广泛的模拟和现实世界任务以及理论分析，我们证明了H2O与其他跨域在线和离线RL算法相对于其他跨域的表现。 H2O提供了全新的脱机脱机RL范式，该范式可能会阐明未来的RL算法设计，以解决实用的现实世界任务。

Offline reinforcement learning (RL) promises the ability to learn effective policies solely using existing, static datasets, without any costly online interaction. To do so, offline RL methods must handle distributional shift between the dataset and the learned policy. The most common approach is to learn conservative, or lower-bound, value functions, which underestimate the return of out-of-distribution (OOD) actions. However, such methods exhibit one notable drawback: policies optimized on such value functions can only behave according to a fixed, possibly suboptimal, degree of conservatism. However, this can be alleviated if we instead are able to learn policies for varying degrees of conservatism at training time and devise a method to dynamically choose one of them during evaluation. To do so, in this work, we propose learning value functions that additionally condition on the degree of conservatism, which we dub confidence-conditioned value functions. We derive a new form of a Bellman backup that simultaneously learns Q-values for any degree of confidence with high probability. By conditioning on confidence, our value functions enable adaptive strategies during online evaluation by controlling for confidence level using the history of observations thus far. This approach can be implemented in practice by conditioning the Q-function from existing conservative algorithms on the confidence. We theoretically show that our learned value functions produce conservative estimates of the true value at any desired confidence. Finally, we empirically show that our algorithm outperforms existing conservative offline RL algorithms on multiple discrete control domains.

在离线增强学习中，加权回归是一种常见方法，可以确保学习的政策与行为策略保持接近并防止选择样本外动作。在这项工作中，我们表明，由于政策模型的分配表达有限，以前的方法可能仍会在培训期间选择看不见的动作，这会偏离其最初动机。为了解决这个问题，我们通过将学习的政策分解为两个部分：表达生成行为模型和动作评估模型，采用生成方法。关键见解是，这种去耦避免学习具有封闭形式表达式的明确参数化的策略模型。直接学习行为策略使我们能够利用生成建模的现有进步，例如基于扩散的方法，以建模各种行为。至于行动评估，我们将方法与样本中的计划技术相结合，以进一步避免选择样本外动作并提高计算效率。 D4RL数据集的实验结果表明，与最先进的离线RL方法相比，我们提出的方法具有竞争性或卓越的性能，尤其是在诸如Antmaze之类的复杂任务中。我们还经验证明，我们的方法可以从包含多个独特但类似成功策略的异质数据集中成功学习，而以前的单峰政策失败了。

Behavior constrained policy optimization has been demonstrated to be a successful paradigm for tackling Offline Reinforcement Learning. By exploiting historical transitions, a policy is trained to maximize a learned value function while constrained by the behavior policy to avoid a significant distributional shift. In this paper, we propose our closed-form policy improvement operators. We make a novel observation that the behavior constraint naturally motivates the use of first-order Taylor approximation, leading to a linear approximation of the policy objective. Additionally, as practical datasets are usually collected by heterogeneous policies, we model the behavior policies as a Gaussian Mixture and overcome the induced optimization difficulties by leveraging the LogSumExp's lower bound and Jensen's Inequality, giving rise to a closed-form policy improvement operator. We instantiate offline RL algorithms with our novel policy improvement operators and empirically demonstrate their effectiveness over state-of-the-art algorithms on the standard D4RL benchmark.

大多数前往离线强化学习（RL）的方法都采取了一种迭代演员 - 批评批评，涉及违规评估。在本文中，我们展示了使用行为政策的政策Q估计来令人惊讶地执行一步的Q估计，从而简单地执行一个受限制/正规化的政策改进的步骤。该一步算法在大部分D4RL基准测试中击败了先前报告的迭代算法的结果。一步基线实现了这种强劲的性能，同时对超公数更简单，更强大而不是先前提出的迭代算法。我们认为迭代方法的表现相对较差是在违反政策评估中固有的高方差，并通过对这些估计的重复优化的政策进行放大。此外，我们假设一步算法的强大性能是由于环境和行为政策中有利结构的组合。

离线增强学习（RL）将经典RL算法的范式扩展到纯粹从静态数据集中学习，而无需在学习过程中与基础环境进行交互。离线RL的一个关键挑战是政策培训的不稳定，这是由于离线数据的分布与学习政策的未结束的固定状态分配之间的不匹配引起的。为了避免分配不匹配的有害影响，我们将当前政策的未静置固定分配正规化在政策优化过程中的离线数据。此外，我们训练动力学模型既实施此正规化，又可以更好地估计当前策略的固定分布，从而减少了分布不匹配引起的错误。在各种连续控制的离线RL数据集中，我们的方法表示竞争性能，从而验证了我们的算法。该代码公开可用。

Offline reinforcement learning (RL) refers to the problem of learning policies entirely from a large batch of previously collected data. This problem setting offers the promise of utilizing such datasets to acquire policies without any costly or dangerous active exploration. However, it is also challenging, due to the distributional shift between the offline training data and those states visited by the learned policy. Despite significant recent progress, the most successful prior methods are model-free and constrain the policy to the support of data, precluding generalization to unseen states. In this paper, we first observe that an existing model-based RL algorithm already produces significant gains in the offline setting compared to model-free approaches. However, standard model-based RL methods, designed for the online setting, do not provide an explicit mechanism to avoid the offline setting's distributional shift issue. Instead, we propose to modify the existing model-based RL methods by applying them with rewards artificially penalized by the uncertainty of the dynamics. We theoretically show that the algorithm maximizes a lower bound of the policy's return under the true MDP. We also characterize the trade-off between the gain and risk of leaving the support of the batch data. Our algorithm, Model-based Offline Policy Optimization (MOPO), outperforms standard model-based RL algorithms and prior state-of-the-art model-free offline RL algorithms on existing offline RL benchmarks and two challenging continuous control tasks that require generalizing from data collected for a different task. * equal contribution. † equal advising. Orders randomized.34th Conference on Neural Information Processing Systems (NeurIPS 2020),

事后重新标记已成为多进球增强学习（RL）的基础技术。这个想法非常简单：任何任意轨迹都可以看作是达到轨迹最终状态的专家演示。直观地，此程序训练了一个目标条件政策，以模仿次优的专家。但是，模仿与事后重新标签之间的这种联系尚不清楚。现代模仿学习算法是用Divergence最小化的语言描述的，但仍然是一个开放的问题。在这项工作中，我们开发了一个统一的目标，以解释这种联系，从中我们可以从中获得目标条件的监督学习（GCSL）和奖励功能，并从第一原则中获得了事后见解体验重播（她）。在实验上，我们发现，尽管目标条件行为克隆（BC）最近取得了进步，但多进球Q学习仍然可以超越BC样方法。此外，两者的香草组合实际上都损害了模型性能。在我们的框架下，我们研究何时期望卑诗省提供帮助，并从经验上验证我们的发现。我们的工作进一步桥接了目标的目标和生成建模，说明了将生成模型成功扩展到RL的细微差别和新途径。

We present state advantage weighting for offline reinforcement learning (RL). In contrast to action advantage $A(s,a)$ that we commonly adopt in QSA learning, we leverage state advantage $A(s,s^\prime)$ and QSS learning for offline RL, hence decoupling the action from values. We expect the agent can get to the high-reward state and the action is determined by how the agent can get to that corresponding state. Experiments on D4RL datasets show that our proposed method can achieve remarkable performance against the common baselines. Furthermore, our method shows good generalization capability when transferring from offline to online.