通过定义具有可变复杂性的流量类型独立环境，基于深度加强学习，介绍一种新的动态障碍避免方法。在当前文献中填补了差距，我们彻底调查了缺失速度信息对代理商在避免任务中的性能的影响。这是实践中至关重要的问题，因为几个传感器仅产生物体或车辆的位置信息。我们在部分可观察性方面评估频繁应用的方法，即在深神经网络中的复发性并简单帧堆叠。为我们的分析，我们依靠最先进的无模型深射RL算法。发现速度信息缺乏影响代理商的性能。两种方法 - 复发性和帧堆叠 - 不能在观察空间中一致地替换缺失的速度信息。但是，在简化的情况下，它们可以显着提高性能并稳定整体培训程序。

In the field of autonomous robots, reinforcement learning (RL) is an increasingly used method to solve the task of dynamic obstacle avoidance for mobile robots, autonomous ships, and drones. A common practice to train those agents is to use a training environment with random initialization of agent and obstacles. Such approaches might suffer from a low coverage of high-risk scenarios in training, leading to impaired final performance of obstacle avoidance. This paper proposes a general training environment where we gain control over the difficulty of the obstacle avoidance task by using short training episodes and assessing the difficulty by two metrics: The number of obstacles and a collision risk metric. We found that shifting the training towards a greater task difficulty can massively increase the final performance. A baseline agent, using a traditional training environment based on random initialization of agent and obstacles and longer training episodes, leads to a significantly weaker performance. To prove the generalizability of the proposed approach, we designed two realistic use cases: A mobile robot and a maritime ship under the threat of approaching obstacles. In both applications, the previous results can be confirmed, which emphasizes the general usability of the proposed approach, detached from a specific application context and independent of the agent's dynamics. We further added Gaussian noise to the sensor signals, resulting in only a marginal degradation of performance and thus indicating solid robustness of the trained agent.

虽然在过去几年中，越来越多地应用了深入的增强学习（RL），但该研究旨在研究基于RL的车辆辅助对复杂的车辆动力学和强烈的环境干扰的可行性。作为用例，我们开发了一种基于逼真的容器动力学的内陆水道跟随模型，该模型考虑了环境影响，例如变化的河流速度和河流剖面。我们从匿名的AIS数据中提取了自然血管行为，以制定奖励功能，该奖励功能反映了舒适且安全的导航旁边的现实驾驶方式。针对高概括能力，我们提出了一个RL训练环境，该环境使用随机过程来建模领先的轨迹和河流动力学。为了验证训练有素的模型，我们定义了在训练中尚未看到的不同情况，包括在中间莱茵河上逼真的船只。我们的模型在所有情况下都表现出安全舒适的驾驶，证明了出色的概括能力。此外，通过在一系列船只上部署训练的模型，可以有效地抑制交通振荡。

Development of navigation algorithms is essential for the successful deployment of robots in rapidly changing hazardous environments for which prior knowledge of configuration is often limited or unavailable. Use of traditional path-planning algorithms, which are based on localization and require detailed obstacle maps with goal locations, is not possible. In this regard, vision-based algorithms hold great promise, as visual information can be readily acquired by a robot's onboard sensors and provides a much richer source of information from which deep neural networks can extract complex patterns. Deep reinforcement learning has been used to achieve vision-based robot navigation. However, the efficacy of these algorithms in environments with dynamic obstacles and high variation in the configuration space has not been thoroughly investigated. In this paper, we employ a deep Dyna-Q learning algorithm for room evacuation and obstacle avoidance in partially observable environments based on low-resolution raw image data from an onboard camera. We explore the performance of a robotic agent in environments containing no obstacles, convex obstacles, and concave obstacles, both static and dynamic. Obstacles and the exit are initialized in random positions at the start of each episode of reinforcement learning. Overall, we show that our algorithm and training approach can generalize learning for collision-free evacuation of environments with complex obstacle configurations. It is evident that the agent can navigate to a goal location while avoiding multiple static and dynamic obstacles, and can escape from a concave obstacle while searching for and navigating to the exit.

小型无人驾驶飞机的障碍避免对于未来城市空袭（UAM）和无人机系统（UAS）交通管理（UTM）的安全性至关重要。有许多技术用于实时强大的无人机指导，但其中许多在离散的空域和控制中解决，这将需要额外的路径平滑步骤来为UA提供灵活的命令。为提供无人驾驶飞机的操作安全有效的计算指导，我们探讨了基于近端政策优化（PPO）的深增强学习算法的使用，以指导自主UA到其目的地，同时通过连续控制避免障碍物。所提出的场景状态表示和奖励功能可以将连续状态空间映射到连续控制，以便进行标题角度和速度。为了验证所提出的学习框架的性能，我们用静态和移动障碍进行了数值实验。详细研究了与环境和安全操作界限的不确定性。结果表明，该拟议的模型可以提供准确且强大的指导，并解决了99％以上的成功率的冲突。

安全探索是强化学习（RL）的常见问题，旨在防止代理在探索环境时做出灾难性的决定。一个解决这个问题的方法家庭以这种环境的（部分）模型的形式假设域知识，以决定动作的安全性。所谓的盾牌迫使RL代理只选择安全的动作。但是，要在各种应用中采用，必须超越执行安全性，还必须确保RL的适用性良好。我们通过与最先进的深度RL的紧密整合扩展了盾牌的适用性，并在部分可观察性下提供了充满挑战的，稀疏的奖励环境中的广泛实证研究。我们表明，经过精心整合的盾牌可确保安全性，并可以提高RL代理的收敛速度和最终性能。我们此外表明，可以使用盾牌来引导最先进的RL代理：它们在屏蔽环境中初步学习后保持安全，从而使我们最终可以禁用潜在的过于保守的盾牌。

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.

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。

资产分配（或投资组合管理）是确定如何最佳将有限预算的资金分配给一系列金融工具/资产（例如股票）的任务。这项研究调查了使用无模型的深RL代理应用于投资组合管理的增强学习（RL）的性能。我们培训了几个RL代理商的现实股票价格，以学习如何执行资产分配。我们比较了这些RL剂与某些基线剂的性能。我们还比较了RL代理，以了解哪些类别的代理表现更好。从我们的分析中，RL代理可以执行投资组合管理的任务，因为它们的表现明显优于基线代理（随机分配和均匀分配）。四个RL代理（A2C，SAC，PPO和TRPO）总体上优于最佳基线MPT。这显示了RL代理商发现更有利可图的交易策略的能力。此外，基于价值和基于策略的RL代理之间没有显着的性能差异。演员批评者的表现比其他类型的药物更好。同样，在政策代理商方面的表现要好，因为它们在政策评估方面更好，样品效率在投资组合管理中并不是一个重大问题。这项研究表明，RL代理可以大大改善资产分配，因为它们的表现优于强基础。基于我们的分析，在政策上，参与者批评的RL药物显示出最大的希望。

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.

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.

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。

本文探讨了强化学习（RL）模型用于自动赛车的使用。与安全车是头等大事的乘用车相反，赛车的目的是最大程度地减少单圈时间。我们将问题视为一项强化学习任务，其中包括由车辆遥测组成的多维输入和连续的动作空间。为了找出哪种RL方法更好地解决了问题，以及获得的模型是否推广到未知轨道上，我们将10种深层确定性策略梯度（DDPG）变体进行了两个实验：i）〜研究RL方法如何学习驱动驱动赛车和ii）研究学习方案如何影响模型的推广能力。我们的研究表明，接受RL训练的模型不仅能够比基线开源手工机器人更快地驾驶，而且还可以推广到未知轨道。

随着自动驾驶行业的发展，自动驾驶汽车群体的潜在相互作用也随之增长。结合人工智能和模拟的进步，可以模拟此类组，并且可以学习控制内部汽车的安全模型。这项研究将强化学习应用于多代理停车场的问题，在那里，汽车旨在有效地停车，同时保持安全和理性。利用强大的工具和机器学习框架，我们以马尔可夫决策过程的形式与独立学习者一起设计和实施灵活的停车环境，从而利用多代理通信。我们实施了一套工具来进行大规模执行实验，从而取得了超过98.1％成功率的高达7辆汽车的模型，从而超过了现有的单代机构模型。我们还获得了与汽车在我们环境中表现出的竞争性和协作行为有关的几个结果，这些行为的密度和沟通水平各不相同。值得注意的是，我们发现了一种没有竞争的合作形式，以及一种“泄漏”的合作形式，在没有足够状态的情况下，代理商进行了协作。这种工作在自动驾驶和车队管理行业中具有许多潜在的应用，并为将强化学习应用于多机构停车场提供了几种有用的技术和基准。

Reinforcement learning (RL) gained considerable attention by creating decision-making agents that maximize rewards received from fully observable environments. However, many real-world problems are partially or noisily observable by nature, where agents do not receive the true and complete state of the environment. Such problems are formulated as partially observable Markov decision processes (POMDPs). Some studies applied RL to POMDPs by recalling previous decisions and observations or inferring the true state of the environment from received observations. Nevertheless, aggregating observations and decisions over time is impractical for environments with high-dimensional continuous state and action spaces. Moreover, so-called inference-based RL approaches require large number of samples to perform well since agents eschew uncertainty in the inferred state for the decision-making. Active inference is a framework that is naturally formulated in POMDPs and directs agents to select decisions by minimising expected free energy (EFE). This supplies reward-maximising (exploitative) behaviour in RL, with an information-seeking (exploratory) behaviour. Despite this exploratory behaviour of active inference, its usage is limited to discrete state and action spaces due to the computational difficulty of the EFE. We propose a unified principle for joint information-seeking and reward maximization that clarifies a theoretical connection between active inference and RL, unifies active inference and RL, and overcomes their aforementioned limitations. Our findings are supported by strong theoretical analysis. The proposed framework's superior exploration property is also validated by experimental results on partial observable tasks with high-dimensional continuous state and action spaces. Moreover, the results show that our model solves reward-free problems, making task reward design optional.

在包装交付，交通监控，搜索和救援操作以及军事战斗订婚等不同应用中，对使用无人驾驶汽车（UAV）（无人机）的需求越来越不断增加。在所有这些应用程序中，无人机用于自动导航环境 - 没有人类互动，执行特定任务并避免障碍。自主无人机导航通常是使用强化学习（RL）来完成的，在该学习中，代理在域中充当专家在避免障碍的同时导航环境。了解导航环境和算法限制在选择适当的RL算法以有效解决导航问题方面起着至关重要的作用。因此，本研究首先确定了无人机导航任务，并讨论导航框架和仿真软件。接下来，根据环境，算法特征，能力和不同无人机导航问题的应用程序对RL算法进行分类和讨论，这将帮助从业人员和研究人员为其无人机导航使用情况选择适当的RL算法。此外，确定的差距和机会将推动无人机导航研究。

在自主驾驶场中，人类知识融合到深增强学习（DRL）通常基于在模拟环境中记录的人类示范。这限制了在现实世界交通中的概率和可行性。我们提出了一种两级DRL方法，从真实的人类驾驶中学习，实现优于纯DRL代理的性能。培训DRL代理商是在Carla的框架内完成了机器人操作系统（ROS）。对于评估，我们设计了不同的真实驾驶场景，可以将提出的两级DRL代理与纯DRL代理进行比较。在从人驾驶员中提取“良好”行为之后，例如在信号交叉口中的预期，该代理变得更有效，并且驱动更安全，这使得这种自主代理更适应人体机器人交互（HRI）流量。

Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged effect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favorable qualitative behavior in our policy analysis.

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.

In order to avoid conventional controlling methods which created obstacles due to the complexity of systems and intense demand on data density, developing modern and more efficient control methods are required. In this way, reinforcement learning off-policy and model-free algorithms help to avoid working with complex models. In terms of speed and accuracy, they become prominent methods because the algorithms use their past experience to learn the optimal policies. In this study, three reinforcement learning algorithms; DDPG, TD3 and SAC have been used to train Fetch robotic manipulator for four different tasks in MuJoCo simulation environment. All of these algorithms are off-policy and able to achieve their desired target by optimizing both policy and value functions. In the current study, the efficiency and the speed of these three algorithms are analyzed in a controlled environment.