使用强化学习解决复杂的问题必须将问题分解为可管理的任务，无论是明确或隐式的任务，并学习解决这些任务的政策。反过来，这些政策必须由采取高级决策的总体政策来控制。这需要培训算法在学习这些政策时考虑这种等级决策结构。但是，实践中的培训可能会导致泛化不良，要么在很少的时间步骤执行动作，要么将其全部转变为单个政策。在我们的工作中，我们介绍了一种替代方法来依次学习此类技能，而无需使用总体层次的政策。我们在环境的背景下提出了这种方法，在这种环境的背景下，学习代理目标的主要组成部分是尽可能长时间延长情节。我们将我们提出的方法称为顺序选择评论家。我们在我们开发的灵活的模拟3D导航环境中演示了我们在导航和基于目标任务的方法的实用性。我们还表明，我们的方法优于先前的方法，例如在我们的环境中，柔软的演员和软选择评论家，以及健身房自动驾驶汽车模拟器和Atari River RAID RAID环境。

Hierarchical Reinforcement Learning (HRL) algorithms have been demonstrated to perform well on high-dimensional decision making and robotic control tasks. However, because they solely optimize for rewards, the agent tends to search the same space redundantly. This problem reduces the speed of learning and achieved reward. In this work, we present an Off-Policy HRL algorithm that maximizes entropy for efficient exploration. The algorithm learns a temporally abstracted low-level policy and is able to explore broadly through the addition of entropy to the high-level. The novelty of this work is the theoretical motivation of adding entropy to the RL objective in the HRL setting. We empirically show that the entropy can be added to both levels if the Kullback-Leibler (KL) divergence between consecutive updates of the low-level policy is sufficiently small. We performed an ablative study to analyze the effects of entropy on hierarchy, in which adding entropy to high-level emerged as the most desirable configuration. Furthermore, a higher temperature in the low-level leads to Q-value overestimation and increases the stochasticity of the environment that the high-level operates on, making learning more challenging. Our method, SHIRO, surpasses state-of-the-art performance on a range of simulated robotic control benchmark tasks and requires minimal tuning.

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。

Linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations. An important problem i RL with LTL tasks is to learn task-conditioned policies which can zero-shot generalize to new LTL instructions not observed in the training. However, because symbolic observation is often lossy and LTL tasks can have long time horizon, previous works can suffer from issues such as training sampling inefficiency and infeasibility or sub-optimality of the found solutions. In order to tackle these issues, this paper proposes a novel multi-task RL algorithm with improved learning efficiency and optimality. To achieve the global optimality of task completion, we propose to learn options dependent on the future subgoals via a novel off-policy approach. In order to propagate the rewards of satisfying future subgoals back more efficiently, we propose to train a multi-step value function conditioned on the subgoal sequence which is updated with Monte Carlo estimates of multi-step discounted returns. In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods.

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.

长期的Horizo​​n机器人学习任务稀疏的奖励对当前的强化学习算法构成了重大挑战。使人类能够学习挑战的控制任务的关键功能是，他们经常获得专家干预，使他们能够在掌握低级控制动作之前了解任务的高级结构。我们为利用专家干预来解决长马增强学习任务的框架。我们考虑\ emph {选项模板}，这是编码可以使用强化学习训练的潜在选项的规格。我们将专家干预提出，因为允许代理商在学习实施之前执行选项模板。这使他们能够使用选项，然后才能为学习成本昂贵的资源学习。我们在三个具有挑战性的强化学习问题上评估了我们的方法，这表明它的表现要优于最先进的方法。训练有素的代理商和我们的代码视频可以在以下网址找到：https：//sites.google.com/view/stickymittens

我们提出了一种层次结构的增强学习方法Hidio，可以以自我监督的方式学习任务不合时宜的选项，同时共同学习利用它们来解决稀疏的奖励任务。与当前倾向于制定目标的低水平任务或预定临时的低级政策不同的层次RL方法不同，Hidio鼓励下级选项学习与手头任务无关，几乎不需要假设或很少的知识任务结构。这些选项是通过基于选项子对象的固有熵最小化目标来学习的。博学的选择是多种多样的，任务不可能的。在稀疏的机器人操作和导航任务的实验中，Hidio比常规RL基准和两种最先进的层次RL方法，其样品效率更高。

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.

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.

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。

Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-efficient learning from rewards which are sparse and binary and therefore avoid the need for complicated reward engineering. It can be combined with an arbitrary off-policy RL algorithm and may be seen as a form of implicit curriculum. We demonstrate our approach on the task of manipulating objects with a robotic arm. In particular, we run experiments on three different tasks: pushing, sliding, and pick-and-place, in each case using only binary rewards indicating whether or not the task is completed. Our ablation studies show that Hindsight Experience Replay is a crucial ingredient which makes training possible in these challenging environments. We show that our policies trained on a physics simulation can be deployed on a physical robot and successfully complete the task. The video presenting our experiments is available at https://goo.gl/SMrQnI.

在本文中，我们提出了一种新的马尔可夫决策过程学习分层表示的方法。我们的方法通过将状态空间划分为子集，并定义用于在分区之间执行转换的子任务。我们制定将状态空间作为优化问题分区的问题，该优化问题可以使用梯度下降给出一组采样的轨迹来解决，使我们的方法适用于大状态空间的高维问题。我们经验验证方法，通过表示它可以成功地在导航域中成功学习有用的分层表示。一旦了解到，分层表示可以用于解决给定域中的不同任务，从而概括跨任务的知识。

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies "end-to-end": directly from raw pixel inputs.

Learning goal-directed behavior in environments with sparse feedback is a major challenge for reinforcement learning algorithms. The primary difficulty arises due to insufficient exploration, resulting in an agent being unable to learn robust value functions. Intrinsically motivated agents can explore new behavior for its own sake rather than to directly solve problems. Such intrinsic behaviors could eventually help the agent solve tasks posed by the environment. We present hierarchical-DQN (h-DQN), a framework to integrate hierarchical value functions, operating at different temporal scales, with intrinsically motivated deep reinforcement learning. A top-level value function learns a policy over intrinsic goals, and a lower-level function learns a policy over atomic actions to satisfy the given goals. h-DQN allows for flexible goal specifications, such as functions over entities and relations. This provides an efficient space for exploration in complicated environments. We demonstrate the strength of our approach on two problems with very sparse, delayed feedback: (1) a complex discrete stochastic decision process, and (2) the classic ATARI game 'Montezuma's Revenge'.

增强学习（RL）研究领域非常活跃，并具有重要的新贡献；特别是考虑到深RL（DRL）的新兴领域。但是，仍然需要解决许多科学和技术挑战，其中我们可以提及抽象行动的能力或在稀疏回报环境中探索环境的难以通过内在动机（IM）来解决的。我们建议通过基于信息理论的新分类法调查这些研究工作：我们在计算上重新审视了惊喜，新颖性和技能学习的概念。这使我们能够确定方法的优势和缺点，并展示当前的研究前景。我们的分析表明，新颖性和惊喜可以帮助建立可转移技能的层次结构，从而进一步抽象环境并使勘探过程更加健壮。

强化学习和最近的深度增强学习是解决如Markov决策过程建模的顺序决策问题的流行方法。问题和选择算法和超参数的RL建模需要仔细考虑，因为不同的配置可能需要完全不同的性能。这些考虑因素主要是RL专家的任务;然而，RL在研究人员和系统设计师不是RL专家的其他领域中逐渐变得流行。此外，许多建模决策，例如定义状态和动作空间，批次的大小和批量更新的频率以及时间戳的数量通常是手动进行的。由于这些原因，RL框架的自动化不同组成部分具有重要意义，近年来它引起了很多关注。自动RL提供了一个框架，其中RL的不同组件包括MDP建模，算法选择和超参数优化是自动建模和定义的。在本文中，我们探讨了可以在自动化RL中使用的文献和目前的工作。此外，我们讨论了Autorl中的挑战，打开问题和研究方向。

当加强学习以稀疏的奖励应用时，代理必须花费很长时间探索未知环境而没有任何学习信号。抽象是一种为代理提供在潜在空间中过渡的内在奖励的方法。先前的工作着重于密集的连续潜在空间，或要求用户手动提供表示形式。我们的方法是第一个自动学习基础环境的离散抽象的方法。此外，我们的方法使用端到端可训练的正规后继代表模型在任意输入空间上起作用。对于抽象状态之间的过渡，我们以选项的形式训练一组时间扩展的动作，即动作抽象。我们提出的算法，离散的国家行动抽象（DSAA），在训练这些选项之间进行迭代交换，并使用它们有效地探索更多环境以改善状态抽象。结果，我们的模型不仅对转移学习，而且在在线学习环境中有用。我们从经验上表明，与基线加强学习算法相比，我们的代理能够探索环境并更有效地解决任务。我们的代码可在\ url {https://github.com/amnonattali/dsaa}上公开获得。

我们提出了一种新型的参数化技能学习算法，旨在学习可转移的参数化技能并将其合成为新的动作空间，以支持长期任务中的有效学习。我们首先提出了新颖的学习目标 - 以轨迹为中心的多样性和平稳性 - 允许代理商能够重复使用的参数化技能。我们的代理商可以使用这些学习的技能来构建时间扩展的参数化行动马尔可夫决策过程，我们为此提出了一种层次的参与者 - 批判算法，旨在通过学习技能有效地学习高级控制政策。我们从经验上证明，所提出的算法使代理能够解决复杂的长途障碍源环境。

The ability to effectively reuse prior knowledge is a key requirement when building general and flexible Reinforcement Learning (RL) agents. Skill reuse is one of the most common approaches, but current methods have considerable limitations.For example, fine-tuning an existing policy frequently fails, as the policy can degrade rapidly early in training. In a similar vein, distillation of expert behavior can lead to poor results when given sub-optimal experts. We compare several common approaches for skill transfer on multiple domains including changes in task and system dynamics. We identify how existing methods can fail and introduce an alternative approach to mitigate these problems. Our approach learns to sequence existing temporally-extended skills for exploration but learns the final policy directly from the raw experience. This conceptual split enables rapid adaptation and thus efficient data collection but without constraining the final solution.It significantly outperforms many classical methods across a suite of evaluation tasks and we use a broad set of ablations to highlight the importance of differentc omponents of our method.

Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks. However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning. Both of these challenges severely limit the applicability of such methods to complex, real-world domains. In this paper, we propose soft actor-critic, an offpolicy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework. In this framework, the actor aims to maximize expected reward while also maximizing entropy. That is, to succeed at the task while acting as randomly as possible. Prior deep RL methods based on this framework have been formulated as Q-learning methods. By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods. Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.