Reinforcement Learning in Single Robot Hose Transport Task_ A Physical Proof of Concept

合集下载

安全强化学习综述

安全强化学习综述王雪松 1王荣荣 1程玉虎1摘要强化学习(Reinforcement learning, RL)在围棋、视频游戏、导航、推荐系统等领域均取得了巨大成功. 然而, 许多强化学习算法仍然无法直接移植到真实物理环境中. 这是因为在模拟场景下智能体能以不断试错的方式与环境进行交互, 从而学习最优策略. 但考虑到安全因素, 很多现实世界的应用则要求限制智能体的随机探索行为. 因此, 安全问题成为强化学习从模拟到现实的一个重要挑战. 近年来, 许多研究致力于开发安全强化学习(Safe reinforcement learning, SRL)算法, 在确保系统性能的同时满足安全约束. 本文对现有的安全强化学习算法进行全面综述, 将其归为三类: 修改学习过程、修改学习目标、离线强化学习, 并介绍了5大基准测试平台: Safety Gym 、safe-control-gym 、SafeRL-Kit 、D4RL 、NeoRL.最后总结了安全强化学习在自动驾驶、机器人控制、工业过程控制、电力系统优化和医疗健康领域中的应用, 并给出结论与展望.关键词安全强化学习, 约束马尔科夫决策过程, 学习过程, 学习目标, 离线强化学习引用格式王雪松, 王荣荣, 程玉虎. 安全强化学习综述. 自动化学报, 2023, 49(9): 1813−1835DOI 10.16383/j.aas.c220631Safe Reinforcement Learning: A SurveyWANG Xue-Song 1 WANG Rong-Rong 1 CHENG Yu-Hu 1Abstract Reinforcement learning (RL) has proved a prominent success in the game of Go, video games, naviga-tion, recommendation systems and other fields. However, a large number of reinforcement learning algorithms can-not be directly transplanted to real physical environment. This is because in the simulation scenario, the agent is able to interact with the environment in a trial-and-error manner to learn the optimal policy. Considering the safety of systems, many real-world applications require the limitation of random exploration behavior of agents. Hence,safety has become an essential factor for reinforcement learning from simulation to reality. In recent years, many re-searches have been devoted to develope safe reinforcement learning (SRL) algorithms that satisfy safety constraints while ensuring system performance. This paper presents a comprehensive survey of existing SRL algorithms, which are divided into three categories: Modification of learning process, modification of learning objective, and offline re-inforcement learning. Furthermore, five experimental platforms are introduced, including Safety Gym, safe-control-gym, SafeRL-Kit, D4RL, and NeoRL. Lastly, the applications of SRL in the fields of autonomous driving, robot control, industrial process control, power system optimization, and healthcare are summarized, and the conclusion and perspective are briefly drawn.Key words Safe reinforcement learning (SRL), constrained Markov decision process (CMDP), learning process,learning objective, offline reinforcement learningCitation Wang Xue-Song, Wang Rong-Rong, Cheng Yu-Hu. Safe reinforcement learning: A survey. Acta Automat-ica Sinica , 2023, 49(9): 1813−1835作为一种重要的机器学习方法, 强化学习 (Re-inforcement learning, RL) 采用了人类和动物学习中 “试错法” 与 “奖惩回报” 的行为心理学机制, 强调智能体在与环境的交互中学习, 利用评价性的反馈信号实现决策的优化[1]. 早期的强化学习主要依赖于人工提取特征, 难以处理复杂高维状态和动作空间下的问题. 近年来, 随着计算机硬件设备性能的提升和神经网络学习算法的发展, 深度学习由于其强大的表征能力和泛化性能受到了众多研究人员的关注[2−3]. 于是, 将深度学习与强化学习相结合就成为了解决复杂环境下感知决策问题的一个可行方案. 2016年, Google 公司的研究团队DeepMind 创新性地将具有感知能力的深度学习与具有决策能收稿日期 2022-08-08 录用日期 2023-01-11Manuscript received August 8, 2022; accepted January 11,2023国家自然科学基金(62176259, 61976215), 江苏省重点研发计划项目(BE2022095)资助Supported by National Natural Science Foundation of China (62176259, 61976215) and Key Research and Development Pro-gram of Jiangsu Province (BE2022095)本文责任编委黎铭Recommended by Associate Editor LI Ming1. 中国矿业大学信息与控制工程学院徐州 2211161. School of Information and Control Engineering, China Uni-versity of Mining and Technology, Xuzhou 221116第 49 卷第 9 期自动化学报Vol. 49, No. 92023 年 9 月ACTA AUTOMATICA SINICASeptember, 2023力的强化学习相结合, 开发的人工智能机器人Al-phaGo 成功击败了世界围棋冠军李世石[4], 一举掀起了深度强化学习的研究热潮. 目前, 深度强化学习在视频游戏[5]、自动驾驶[6]、机器人控制[7]、电力系统优化[8]、医疗健康[9]等领域均得到了广泛的应用.近年来, 学术界与工业界开始逐步注重深度强化学习如何从理论研究迈向实际应用. 然而, 要实现这一阶段性的跨越还有很多工作需要完成, 其中尤为重要的一项任务就是保证决策的安全性. 安全对于许多应用至关重要, 一旦学习策略失败则可能会引发巨大灾难. 例如, 在医疗健康领域, 微创手术机器人辅助医生完成关于大脑或心脏等关键器官手术时, 必须做到精准无误, 一旦偏离原计划位置, 则将对病人造成致命危害. 再如, 自动驾驶领域, 如果智能驾驶车辆无法规避危险路障信息, 严重的话将造成车毁人亡. 因此, 不仅要关注期望回报最大化,同时也应注重学习的安全性.García 和Fernández [10]于2015年给出了安全强化学习 (Safe reinforcement learning, SRL) 的定义: 考虑安全或风险等概念的强化学习. 具体而言,所谓安全强化学习是指在学习或部署过程中, 在保证合理性能的同时满足一定安全约束的最大化长期回报的强化学习过程. 自2015年起, 基于此研究,学者们提出了大量安全强化学习算法. 为此, 本文对近年来的安全强化学习进行全面综述, 围绕智能体的安全性问题, 从修改学习过程、修改学习目标以及离线强化学习三方面进行总结, 并给出了用于安全强化学习的5大基准测试平台: Safety Gym 、safe-control-gym 、SafeRL-Kit 、D4RL 、NeoRL, 以及安全强化学习在自动驾驶、机器人控制、工业过程控制、电力系统优化以及医疗健康领域的应用.安全强化学习中所涉及的方法、基准测试平台以及应用领域之间的关系如图1所示.本文结构如下: 第1节对安全强化学习问题进行形式化描述; 第2节对近年来的安全强化学习方法进行分类与综述; 第3节介绍5种基准测试平台;第4节总结安全强化学习的实际应用场景; 第5节对未来研究方向进行探讨; 第6节对文章进行总结.1 问题描述M ∪C M =⟨S ,A ,T ,γ,r ⟩C ={c,d }S A T (s ′|s,a )γr :S ×A →R c :S ×A →R d π∗安全强化学习问题通常被定义为一个约束马尔科夫决策过程 (Constrained Markov decision pro-cess, CMDP) [11], 即在标准马尔科夫决策过程的基础上添加了关于成本函数的约束项 . 表示状态空间集, 表示动作空间集, 表示用于描述动力学模型的状态转移函数, 表示折扣因子, 表示奖励函数; 表示成本函数, 表示安全阈值. 这种情况下, 安全强化学习问题可以表述为在满足安全约束的情况下, 求解使期望回报最大化的最优可行策略J (π)=E τ∼π(∞t =0γtr (s t ,a t ))τ=(s 0,a 0,s 1,a 1,···)τ∼πτπΠc 其中, , 表示一条轨迹, 表示轨迹根据策略采样得到, 表示满足安全约束的安全策略集. 值得注意的是, 本文公式所描述的都是单成本约束的形式, 但不失一般性, 这些公式都可以拓展为多成本约束的形式. 对于不同类型的决策任务,安全策略集可以有不同的表达形式.Πc 对于安全性要求严格的决策任务, 例如自动驾驶[12−13]任务, 通常采用硬约束方式, 即在所有的时刻都需要强制满足单步约束. 这种情况下表示为环境知识人类知识无先验知识拉格朗日法信赖域法策略约束值约束预训练模型图 1 安全强化学习方法、基准测试平台与应用Fig. 1 Methods, benchmarking platforms, and applications of safe reinforcement learning1814自动化学报49 卷Π其中, 表示可行策略集. 但由于这种约束方式要求过于严格, 因此通常需要借助模型信息加以实现.Πc 在无模型情况下, 软约束方式有着更广泛的应用, 即对折扣累积成本的期望进行约束, 这种情况下表示为c :S ×A →{0,1}c (s t ,a t )=0c (s t ,a t )=1E τ∼π(∑∞t =0γtc (s t ,a t ))π这种约束方式可以很好地适用于机器人行走[14]、油泵安全控制[15]和电力系统优化[16]等任务, 但对于需要明确定义状态或动作是否安全的任务却难以处理. 为了使软约束方式更好地适用于不同类型的决策任务, 可以将成本函数修改为 ,利用成本函数对当前状态动作对进行安全性判断,若安全, 则 , 否则, , 并且在智能体与环境交互期间遇到不安全的状态动作对时终止当前回合. 这时, 约束项可以表示产生不安全状态动作对的概率, 因此经过这样修改后的软约束也被称为机会型约束. 机会型约束由于其良好的任务适应性, 已被成功应用于无模型的自动驾驶[17]和机械臂控制[18]等任务.M =⟨S ,A ,T ,γ,r ⟩π∗=arg max π∈ΠJ (π)B ={(s,a,r,s ′)}π∗另一方面, 离线强化学习[19−20]从一个静态的数据集中学习最优策略, 它避免了与环境的交互过程,可以保障训练过程中的安全性. 因此, 可以将离线强化学习作为安全强化学习的一种特殊形式. 离线强化学习考虑一个标准马尔科夫决策过程 , 它的目标是求解使期望回报最大化的最优可行策略 , 与在线方式不同的是, 智能体在训练过程中不再被允许与环境进行交互, 而是只能从一个静态数据集中进行学习. 尽管这种方式可以保障训练过程中的安全性, 但分布偏移问题 (目标策略与行为策略分布不同)[19−20]也给求解的过程带来了困难.因此, 现如今的离线强化学习方法大多关注于如何解决分布偏移问题. 离线强化学习在有先验离线数据集支持的情况下, 借助于其训练过程安全的优势,已被应用于微创手术机器人控制[21]和火力发电机组控制[22]等任务.2 方法分类求解安全强化学习问题的方法有很多, 受Gar-cía 和Fernández [10]启发, 本文从以下三方面进行综述:1) 修改学习过程. 通过约束智能体的探索范围, 采用在线交互反馈机制, 在强化学习的学习或探索过程中阻止其产生危险动作, 从而确保了训练时策略的安全性. 根据是否利用先验知识, 将此类方法划分为三类: 环境知识、人类知识、无先验知识.2) 修改学习目标. 同样采用在线交互反馈机制, 在强化学习的奖励函数或目标函数中引入风险相关因素, 将约束优化问题转化为无约束优化问题,如拉格朗日法、信赖域法.3) 离线强化学习. 仅在静态的离线数据集上训练而不与环境产生交互, 从而完全避免了探索, 但对部署时安全没有任何约束保证, 并未考虑风险相关因素. 因此大多数离线强化学习能实现训练时安全, 但无法做到部署时安全.三类安全强化学习方法的适用条件、优缺点以及应用领域对比如表1所示. 下面对安全强化学习的现有研究成果进行详细综述与总结.2.1 修改学习过程在强化学习领域, 智能体需要通过不断探索来减小外界环境不确定性对自身学习带来的影响. 因此, 鼓励智能体探索一直是强化学习领域非常重要的一个研究方向. 然而, 不加限制的自由探索很有可能使智能体陷入非常危险的境地, 甚至酿成重大安全事故. 为避免强化学习智能体出现意外和不可逆的后果, 有必要在训练或部署的过程中对其进行安全性评估并将其限制在 “安全” 的区域内进行探索, 将此类方法归结为修改学习过程. 根据智能体利用先验知识的类型将此类方法进一步细分为环境知识、人类知识以及无先验知识. 其中环境知识利用系统动力学先验知识实现安全探索; 人类知识借鉴人类经验来引导智能体进行安全探索; 无先验知识没有用到环境知识和人类知识, 而是利用安全约束结构将不安全的行为转换到安全状态空间中.2.1.1 环境知识基于模型的方法因其采样效率高而得以广泛研究. 该类方法利用了环境知识, 需要学习系统动力学模型, 并利用模型生成的轨迹来增强策略学习,其核心思想就是通过协调模型使用和约束策略搜索来提高安全探索的采样效率. 可以使用高斯过程对模型进行不确定性估计, 利用Shielding 修改策略动作从而生成满足约束的安全过滤器, 使用李雅普诺夫函数法或控制障碍函数法来限制智能体的动作选择, 亦或使用已学到的动力学模型预测失败并生成安全策略. 具体方法总结如下.高斯过程. 一种主流的修改学习过程方式是使用高斯过程对具有确定性转移函数和值函数的动力9 期王雪松等: 安全强化学习综述1815学建模, 以便能够估计约束和保证安全学习. Sui等[38]将 “安全” 定义为: 在智能体学习过程中, 选择的动作所收到的期望回报高于一个事先定义的阈值. 由于智能体只能观测到当前状态的安全函数值, 而无法获取相邻状态的信息, 因此需要对安全函数进行假设. 为此, 在假设回报函数满足正则性、Lipschitz 连续以及范数有界等条件的前提下, Sui等[38]利用高斯过程对带参数的回报函数进行建模, 提出一种基于高斯过程的安全探索方法SafeOpt. 在学习过程中, 结合概率生成模型, 通过贝叶斯推理即可求得高斯过程的后验分布, 即回报函数空间的后验.进一步, 利用回报函数置信区间来评估决策的安全性, 得到一个安全的参数区间并约束智能体只在这个安全区间内进行探索. 然而, SafeOpt仅适用于类似多臂老虎机这类的单步、低维决策问题, 很难推广至复杂决策问题. 为此, Turchetta等[39]利用马尔科夫决策过程的可达性, 在SafeOpt的基础上提出SafeMDP安全探索方法, 使其能够解决确定性有限马尔科夫决策过程问题. 在SafeOpt和SafeM-DP中, 回报函数均被视为是先验已知和时不变的,但在很多实际问题中, 回报函数通常是先验未知和时变的. 因此, 该方法并未在考虑安全的同时优化回报函数. 针对上述问题, Wachi等[40]把时间和空间信息融入核函数, 利用时−空高斯过程对带参数的回报函数进行建模, 提出一种新颖的安全探索方法: 时−空SafeMDP (Spatio-temporal SafeMDP, ST-SafeMDP), 能够依概率确保安全性并同时优化回报目标. 尽管上述方法是近似安全的, 但正则性、Lipschitz连续以及范数有界这些较为严格的假设条件限制了SafeOpt、SafeMDP和ST-SafeM-DP在实际中的应用, 而且, 此类方法存在理论保证与计算成本不一致的问题, 在高维空间中很难达到理论上保证的性能.Shielding. Alshiekh等[41]首次提出Shield-ing的概念来确保智能体在学习期间和学习后保持安全. 根据Shielding在强化学习环节中部署的位置, 将其分为两种类型: 前置Shielding和后置Shielding. 前置Shielding是指在训练过程中的每个时间步, Shielding仅向智能体提供安全的动作以供选择. 后置Shielding方式较为常用, 它主要影响智能体与环境的交互过程, 如果当前策略不安全则触发Shielding, 使用一个备用策略来覆盖当前策略以保证安全性. 可以看出, 后置Shielding方法的使用主要涉及两个方面的工作: 1) Shielding触发条件的设计. Zhang等[42]通过一个闭环动力学模型来估计当前策略下智能体未来的状态是否为可恢复状态, 如果不可恢复, 则需要采用备用策略将智能体还原到初始状态后再重新训练. 但如果智能体的状态不能还原, 则此方法就会失效. Jansen等[43]一方面采用形式化验证的方法来计算马尔科夫决策过程安全片段中关键决策的概率, 另一方面根据下一步状态的安全程度来估计决策的置信度. 当关键决策的概率及其置信度均较低时, 则启用备用策略. 但是, 在复杂的强化学习任务中, 从未知的环境中提取出安全片段并不是一件容易的事情. 2) 备用 (安全)策略的设计. Li和Bastani[44]提出了一种基于tube 的鲁棒非线性模型预测控制器并将其作为备用控制器, 其中tube为某策略下智能体多次运行轨迹组成的集合. Bastani[45]进一步将备用策略划分为不变策略和恢复策略, 其中不变策略使智能体在安全平衡点附近运动, 恢复策略使智能体运行到安全平衡点. Shielding根据智能体与安全平衡点的距离来表 1 安全强化学习方法对比Table 1 Comparison of safe reinforcement learning methods方法类别训练时安全部署时安全与环境实时交互优点缺点应用领域修改学习过程环境知识√√√采样效率高需获取环境的动力学模型、实现复杂自动驾驶[12−13, 23]、工业过程控制[24−25]、电力系统优化[26]、医疗健康[21]人类知识√√√加快学习过程人工监督成本高机器人控制[14, 27]、电力系统优化[28]、医疗健康[29]无先验知识√√√无需获取先验知识、可扩展性强收敛性差、训练不稳定自动驾驶[30]、机器人控制[31]、工业过程控制[32]、电力系统优化[33]、医疗健康[34]修改学习目标拉格朗日法×√√思路简单、易于实现拉格朗日乘子选取困难工业过程控制[15]、电力系统优化[16]信赖域法√√√收敛性好、训练稳定近似误差不可忽略、采样效率低机器人控制[35]离线强化学习策略约束√××收敛性好方差大、采样效率低医疗健康[36]值约束√××值函数估计方差小收敛性差工业过程控制[22]预训练模型√××加快学习过程、泛化性强实现复杂工业过程控制[37]1816自动化学报49 卷决定选用何种类型的备用策略, 从而进一步增强了智能体的安全性. 但是, 在复杂的学习问题中, 很难定义安全平衡点, 往往也无法直观地观测状态到平衡点的距离. 综上所述, 如果环境中不存在可恢复状态, Shielding即便判断出了危险, 也没有适合的备用策略可供使用. 此外, 在复杂的强化学习任务中, 很难提供充足的先验知识来搭建一个全面的Shielding以规避所有的危险.李雅普诺夫法. 李雅普诺夫稳定性理论对于控制理论学科的发展产生了深刻的影响, 是现代控制理论中一个非常重要的组成部分. 该方法已被广泛应用于控制工程中以设计出达到定性目标的控制器, 例如稳定系统或将系统状态维持在所需的工作范围内. 李雅普诺夫函数可以用来解决约束马尔科夫决策过程问题并保证学习过程中的安全性. Per-kins和Barto[46]率先提出了在强化学习中使用李雅普诺夫函数的思路, 通过定性控制技术设计一些基准控制器并使智能体在这些给定的基准控制器间切换, 用于保证智能体的闭环稳定性. 为了规避风险,要求强化学习方法具有从探索动作中安全恢复的能力, 也就是说, 希望智能体能够恢复到安全状态. 众所周知, 这种状态恢复的能力就是控制理论中的渐近稳定性. Berkenkamp等[47]使用李雅普诺夫函数对探索空间进行限制, 让智能体大概率地探索到稳定的策略, 从而能够确保基于模型的强化学习智能体可以在探索过程中被带回到 “吸引区域”. 所谓吸引区域是指: 状态空间的子集, 从该集合中任一状态出发的状态轨迹始终保持在其中并最终收敛到目标状态. 然而, 该方法只有在满足Lipschitz连续性假设条件下才能逐步探索安全状态区域, 这需要事先对具体系统有足够了解, 一般的神经网络可能并不具备Lipschitz连续. 上述方法是基于值函数的,因此将其应用于连续动作问题上仍然具有挑战性.相比之下, Chow等[48]更专注于策略梯度类方法,从原始CMDP安全约束中生成一组状态相关的李雅普诺夫约束, 提出一种基于李雅普诺夫函数的CMDP安全策略优化方法. 主要思路为: 使用深度确定性策略梯度和近端策略优化算法训练神经网络策略, 同时通过将策略参数或动作映射到由线性化李雅普诺夫约束诱导的可行解集上来确保每次策略更新时的约束满意度. 所提方法可扩展性强, 能够与任何同策略或异策略的方法相结合, 可以处理具有连续动作空间的问题, 并在训练和收敛过程中返回安全策略. 通过使用李雅普诺夫函数和Trans-former模型, Jeddi等[49]提出一种新的不确定性感知的安全强化学习算法. 该算法主要思路为: 利用具有理论安全保证的李雅普诺夫函数将基于轨迹的安全约束转换为一组基于状态的局部线性约束; 将安全强化学习模型与基于Transformer的编码器模型相结合, 通过自注意机制为智能体提供处理长时域范围内信息的记忆; 引入一个规避风险的动作选择方案, 通过估计违反约束的概率来识别风险规避的动作, 从而确保动作的安全性. 总而言之, 李雅普诺夫方法的主要特征是将基于轨迹的约束分解为一系列单步状态相关的约束. 因此, 当状态空间无穷大时, 可行性集就具有无穷维约束的特征, 此时直接将这些李雅普诺夫约束(相对于原始的基于轨迹的约束)强加到策略更新优化中实现成本高, 无法应用于真实场景, 而且, 此类方法仅适用于基于模型的强化学习且李雅普诺夫函数通常难以构造.障碍函数法. 障碍函数法是另一种保证控制系统安全的方法. 其基本思想为: 系统状态总是从内点出发, 并始终保持在可行安全域内搜索. 在原先的目标函数中加入障碍函数惩罚项, 相当于在可行安全域边界构筑起一道 “墙”. 当系统状态达到安全边界时, 所构造的障碍函数值就会趋于无穷, 从而避免状态处于安全边界, 而是被 “挡” 在安全域内.为保证强化学习算法在模型信息不确定的情况下的安全性, Cheng等[50]提出了一种将现有的无模型强化学习算法与控制障碍函数 (Control barrier func-tions, CBF) 相结合的框架RL-CBF. 该框架利用高斯过程来模拟系统动力学及其不确定性, 通过使用预先指定的障碍函数来指导策略探索, 提高了学习效率, 实现了非线性控制系统的端到端安全强化学习. 然而, 使用的离散时间CBF公式具有限制性, 因为它只能通过仿射CBF的二次规划进行实时控制综合. 例如, 在避免碰撞的情况下, 仿射CBF 只能编码多面体障碍物. 为了在学习过程中保持安全性, 系统状态必须始终保持在安全集内, 该框架前提假设已得到一个有效安全集, 但实际上学习安全集并非易事, 学习不好则可能出现不安全状态. Yang 等[51]采用障碍函数对系统进行变换, 将原问题转化为无约束优化问题的同时施加状态约束. 为减轻通信负担, 设计了静态和动态两类间歇性策略. 最后,基于actor-critic架构, 提出一种安全的强化学习算法, 采用经验回放技术, 利用历史数据和当前数据来共同学习约束问题的解, 在保证最优性、稳定性和安全性的同时以在线的方式寻求最优安全控制器. Marvi和Kiumarsi[52]提出了一种安全异策略强化学习方法, 以数据驱动的方式学习最优安全策略.该方法将CBF合并进安全最优控制成本目标中形成一个增广值函数, 通过对该增广值函数进行迭代近似并调节权衡因子, 从而实现安全性与最优性的平衡. 但在实际应用中, 权衡因子的选取需要事先9 期王雪松等: 安全强化学习综述1817人工设定, 选择不恰当则可能找不到最优解. 先前的工作集中在一类有限的障碍函数上, 并利用一个辅助神经网来考虑安全层的影响, 这本身就造成了一种近似. 为此, Emam等[53]将一个可微的鲁棒控制障碍函数 (Robust CBF, RCBF) 层合并进基于模型的强化学习框架中. 其中, RCBF可用于非仿射实时控制综合, 而且可以对动力学上的各种扰动进行编码. 同时, 使用高斯过程来学习扰动, 在安全层利用扰动生成模型轨迹. 实验表明, 所提方法能有效指导训练期间的安全探索, 提高样本效率和稳态性能. 障碍函数法能够确保系统安全, 但并未考虑系统的渐进稳定性, 与李雅普诺夫法类似, 在实际应用中障碍函数和权衡参数都需要精心设计与选择.引入惩罚项. 此类方法在原先目标函数的基础上添加惩罚项, 以此修正不安全状态. 由于传统的乐观探索方法可能会使智能体选择不安全的策略,导致违反安全约束, 为此, Bura等[54]提出一种基于模型的乐观−悲观安全强化学习算法 (Optimistic-pessimistic SRL, OPSRL). 该算法在不确定性乐观目标函数的基础上添加悲观约束成本函数惩罚项,对回报目标持乐观态度以便促进探索, 同时对成本函数持悲观态度以确保安全性. 在Media Control 环境下的仿真结果表明, OPSRL在没有违反安全约束的前提下能获得最优性能. 基于模型的方法有可能在安全违规行为发生之前就得以预测, 基于这一动机, Thomas等[55]提出了基于模型的安全策略优化算法 (Safe model-based policy optimization, SMBPO). 该算法通过预测未来几步的轨迹并修改奖励函数来训练安全策略, 对不安全的轨迹进行严厉惩罚, 从而避免不安全状态. 在MuJoCo机器人控制模拟环境下的仿真结果表明, SMBPO能够有效减少连续控制任务的安全违规次数. 但是, 需要有足够大的惩罚和精确的动力学模型才能避免违反安全. Ma等[56]提出了一种基于模型的安全强化学习方法, 称为保守与自适应惩罚 (Conservative and adaptive penalty, CAP). 该方法使用不确定性估计作为保守惩罚函数来避免到达不安全区域, 确保所有的中间策略都是安全的, 并在训练过程中使用环境的真实成本反馈适应性地调整这个惩罚项, 确保零安全违规. 相比于先前的安全强化学习算法, CAP具有高效的采样效率, 同时产生了较少的违规行为.2.1.2 人类知识为了获得更多的经验样本以充分训练深度网络, 有些深度强化学习方法甚至在学习过程中特意加入带有随机性质的探索性学习以增强智能体的探索能力. 一般来说, 这种自主探索仅适用于本质安全的系统或模拟器. 如果在现实世界的一些任务(例如智能交通、自动驾驶) 中直接应用常规的深度强化学习方法, 让智能体进行不受任何安全约束的“试错式” 探索学习, 所做出的决策就有可能使智能体陷入非常危险的境地, 甚至酿成重大安全事故.相较于通过随机探索得到的经验, 人类专家经验具备更强的安全性. 因此, 借鉴人类经验来引导智能体进行探索是一个可行的增强智能体安全性的措施. 常用的方法有中断机制、结构化语言约束、专家指导.中断机制. 此类方法借鉴了人类经验, 当智能体做出危险动作时能及时进行中断. 在将强化学习方法应用于实际问题时, 最理想的状况是智能体任何时候都不会做出危险动作. 由于限制条件太强,只能采取 “人在环中” 的人工介入方式, 即人工盯着智能体, 当出现危险动作时, 出手中断并改为安全的动作. 但是, 让人来持续不断地监督智能体进行训练是不现实的, 因此有必要将人工监督自动化.基于这个出发点, Saunders等[57]利用模仿学习技术来学习人类的干预行为, 提出一种人工干预安全强化学习 (SRL via human intervention, HIRL) 方法. 主要思路为: 首先, 在人工监督阶段, 收集每一个状态−动作对以及与之对应的 “是否实施人工中断” 的二值标签; 然后, 基于人工监督阶段收集的数据, 采用监督学习方式训练一个 “Blocker” 以模仿人类的中断操作. 需要指出的是, 直到 “Blocker”在剩余的训练数据集上表现良好, 人工监督阶段的操作方可停止. 采用4个Atari游戏来测试HIRL 的性能, 结果发现: HIRL的应用场景非常受限, 仅能处理一些较为简单的智能体安全事故且难以保证智能体完全不会做出危险动作; 当环境较为复杂的时候, 甚至需要一年以上的时间来实施人工监督,时间成本高昂. 为降低时间成本, Prakash等[58]将基于模型的方法与HIRL相结合, 提出一种混合安全强化学习框架, 主要包括三个模块: 基于模型的模块、自举模块、无模型模块. 首先, 基于模型的模块由一个动力学模型组成, 用以驱动模型预测控制器来防止危险动作发生; 然后, 自举模块采用由模型预测控制器生成的高质量示例来初始化无模型强化学习方法的策略; 最后, 无模型模块使用基于自举策略梯度的强化学习智能体在 “Blocker” 的监督下继续学习任务. 但是, 作者仅在小规模的4×4格子世界和Island Navigation仿真环境中验证了方法的有效性, 与HIRL一样, 该方法的应用场景仍1818自动化学报49 卷。

人工智能专用名词

专用名词专用名词1.（Artificial Intelligence，简称）是指计算机科学的一个分支，旨在开发具备类似人类智能的机器或系统。

的主要目标是使计算机能够执行类似于人类智能的任务，如学习、推理、理解、感知和语言处理等。

2.机器学习（Machine Learning）是的一个重要分支，它是通过让计算机从数据中学习和改进性能，而无需明确编程指令的技术。

机器学习可以分为监督学习、无监督学习、强化学习等不同类型。

2.1 监督学习（Supervised Learning）：在监督学习中，计算机从有标签的训练数据中学习，并通过学习到的模型对未知数据进行预测。

2.2 无监督学习（Unsupervised Learning）：在无监督学习中，计算机从没有标签的训练数据中学习，并通过发现数据之间的潜在关系进行聚类或降维等任务。

2.3 强化学习（Reinforcement Learning）：在强化学习中，计算机通过与环境的交互学习，并根据反馈信号来调整行为，从而逐步提升性能。

3.深度学习（Deep Learning）是机器学习的一种特殊形式，其基本单位是人工神经网络（Artificial Neural Network）。

深度学习通过多层神经网络模拟人脑神经元之间的连接，实现对复杂数据的学习和理解。

4.自然语言处理（Natural Language Processing，简称NLP）是一门使用技术处理和分析自然语言（如英语、汉语等）的学科。

NLP的主要任务包括语言理解、语言、机器翻译等。

5.计算机视觉（Computer Vision）是一种使用技术使计算机能够“看”的学科。

计算机视觉的应用包括图像识别、物体检测、人脸识别等。

6.自动驾驶（Autonomous Driving）是将技术应用于汽车领域，使汽车能够自主、智能地行驶。

自动驾驶技术需要借助传感器、计算机视觉、机器学习等技术来实现。

7.（Robot）是利用技术设计和制造的具有自主智能行动能力的物理实体。

基于深度强化学习的机器人控制算法研究

基于深度强化学习的机器人控制算法研究在当今科技发展日新月异的时代，机器人被广泛应用于各个领域，从工业生产到医疗保健，甚至到家庭助理。

作为一种智能化的装置，机器人控制算法的研究显得尤为重要。

本文将介绍一种新兴的机器人控制算法——基于深度强化学习的机器人控制算法，并探讨其在机器人控制领域中的应用。

深度强化学习（Deep Reinforcement Learning，DRL）作为一种结合了深度学习和强化学习的技术，近年来得到了广泛的关注。

深度学习通过多层次的神经网络将非线性函数逼近与优化相结合，能够对大量、高维度的数据进行处理和分析。

而强化学习则通过智能体与环境的交互，通过试错的过程寻找最优的决策策略。

在机器人领域，DRL算法的出现使得机器人的控制能力得到了极大的提升。

一种典型的基于DRL的机器人控制算法是深度Q网络（Deep Q Network，DQN）。

DQN通过将环境状态作为输入，输出对应于每种动作的Q值。

在训练过程中，通过不断迭代更新神经网络的权重，从而使得网络能够逐渐收敛到真实的Q 值函数。

在实际应用中，DQN可以通过经验回放和目标网络来增强学习的稳定性和效果。

除了DQN，还有其他一些基于DRL的机器人控制算法，如深度策略网络（Deep Policy Network，DPN），DDPG（Deep Deterministic Policy Gradient），PPO（Proximal Policy Optimization）等。

这些算法在不同的机器人控制场景中有着各自的优势和适用性。

机器人控制算法的研究旨在实现机器人的智能化行为，使其能够自主地与环境交互并根据情境作出相应的决策。

例如，在物流领域，机器人需要在仓库中自主地寻找货物、拣选、包装和移动。

在医疗领域，机器人需要能够根据患者的情况做出适当的医疗决策。

而在家庭助理领域，机器人需要具备感知环境、识别人脸、语音识别等能力，从而能够为用户提供个性化的服务。

基于深度强化学习的自主机器人运动控制研究

基于深度强化学习的自主机器人运动控制研究深度强化学习（Deep Reinforcement Learning, DRL）作为一种基于智能体与环境交互学习的方法，近年来在自主机器人运动控制方面取得了显著的突破。

本文将探讨基于深度强化学习的自主机器人运动控制研究的现状、挑战以及未来发展方向。

自主机器人的运动控制是一个复杂而多样的问题，要求机器人能够在不确定的环境中做出适应性的决策，并实现精准的运动控制。

传统的控制方法常常需要手动设计复杂的运动模型和控制策略，然而，随着机器人运动场景的多样化和任务复杂度的提高，传统方法在应对这些问题时显得力不从心。

深度强化学习的出现为解决自主机器人运动控制问题带来了新的机遇。

它结合了深度学习和强化学习的优势，能够从海量的数据中自动学习适应性的运动控制策略。

深度强化学习的核心思想是通过智能体与环境的交互，不断试错、学习和优化，最终实现最优的动作选择策略。

在基于深度强化学习的自主机器人运动控制研究中，一个重要的挑战是如何建立合适的状态表示。

状态表示是深度强化学习的基础，它将环境的信息抽象为一个向量或者矩阵，作为输入传输给深度神经网络。

合适的状态表示需要包含足够的信息，同时去除冗余和无关的信息，以降低学习的复杂度。

研究人员可以通过观察机器人的传感器数据，选择合适的特征进行表示，或者采用无监督学习的方法自动学习最优的状态表示。

另外一个挑战是如何设计适应性的奖励函数。

奖励函数是深度强化学习中的重要组成部分，用于评估智能体每个动作的好坏程度。

在自主机器人运动控制领域，设计奖励函数需要考虑多个方面，如运动目标的完成度、轨迹的平滑度、碰撞的避免等因素。

一个好的奖励函数应该能够正确引导深度强化学习算法学习到合理的运动策略，避免过度优化或者陷入局部最优。

此外，面对复杂多变的环境，如何提高深度强化学习算法的学习效率和稳定性也是一个重要的研究方向。

深度强化学习算法通常需要大量的样本来进行训练，但在实际应用中，获取大量的真实样本往往非常困难和昂贵。

人工智能术语

人工智能术语人工智能术语人工智能（Artificial Intelligence，简称AI）是一种模拟人类智能行为的技术和方法。

它通过模拟人类的思维能力，使机器能够像人一样进行学习、推理、决策和解决问题。

以下是一些常见的人工智能术语。

1. 机器学习（Machine Learning）：机器学习是一种基于数据和模型的算法，通过分析和处理大量的数据来训练机器，使其能够自动地识别模式和规律，并做出相应的决策和预测。

2. 深度学习（Deep Learning）：深度学习是机器学习的一种特殊形式，其模型由多个神经网络层组成。

深度学习通过多层次的非线性变换，能够对复杂的数据进行更准确的建模和分析。

3. 神经网络（Neural Network）：神经网络是一种模拟人脑神经元结构和功能的数学模型。

它由多个节点和连接组成，通过输入数据和权重的计算，进行信息传递和处理。

4. 自然语言处理（Natural Language Processing，简称NLP）：自然语言处理是研究人类语言的一门学科，旨在使计算机能够理解、分析和生成自然语言。

NLP在机器翻译、语义分析等领域有广泛应用。

5. 计算机视觉（Computer Vision）：计算机视觉是使计算机能够理解和解释图像和视频的技术。

它包括图像识别、目标检测、图像生成等任务，广泛应用于人脸识别、无人驾驶等领域。

6. 强化学习（Reinforcement Learning）：强化学习是一种通过试错和反馈来训练智能体的学习方法。

智能体根据环境的反馈，不断调整自己的行为，以达到最优的目标。

7. 数据挖掘（Data Mining）：数据挖掘是从大量数据中发现模式和知识的过程。

通过机器学习和统计分析等技术，数据挖掘可以帮助人们发现隐藏在数据中的规律和趋势。

8. 自动驾驶（Autonomous Driving）：自动驾驶是利用人工智能技术使汽车能够在没有人类驾驶的情况下自动行驶的技术。

reinforcement learning的例子

reinforcement learning的例子Reinforcement learning是一种机器学习方法，它通过尝试与周围环境进行交互，通过正反馈不断地调整自己的学习效果，最终达到使某一目标函数最大化的目标。

Reinforcement learning的应用非常广泛，例如在游戏玩家的智能控制、机器人导航等领域中得到了广泛的应用。

在游戏智能控制领域，reinforcement learning被广泛应用。

比如，在象棋中，使用reinforcement learning可以让模型自动学习最佳的下棋策略。

为此，我们可以使用强化学习方法来训练模型，在不同的棋局中，根据模型预测的结果与实际结果的差异进行学习，不断调整模型参数。

这样的强化学习模型可以通过无数的自我对弈而不断提升自己的水平，最终学习到最佳的下棋策略。

另外，reinforcement learning也可以被应用在机器人导航的领域。

比如，在一个复杂的环境中，我们可以用reinforcement learning来训练机器人，让它通过自身的运动与环境进行交互，不断地调整自己的行动策略，最终找到一个最优的路径，完成任务的目标。

通过这种方式，我们可以让机器人在一个陌生的环境中自主探索，获得更准确和高效的导航能力。

除了上述两个领域，reinforcement learning还可以被应用在其他很多领域。

例如，在自然语言处理领域中，我们可以通过reinforcement learning来训练一个机器翻译模型，使其不断地修正自己的预测，并输出更准确的翻译结果。

在金融领域，我们可以使用reinforcement learning来优化投资组合，以获得更好的投资回报。

还有一个非常重要的领域，就是在医学方面的应用。

我们可以利用reinforcement learning来帮助医生诊断疾病或者选择最佳的治疗方式，从而提高诊断和治疗的准确性和效率。

总之，reinforcement learning在应用上非常广泛，可以被用于各种研究领域。

reinforcement learning中文版

reinforcement learning中文版
强化学习（Reinforcement Learning）是一种机器学习方法，它通过智能体与环境之间的交互来学习如何做出最优的决策。

在强化学习中，智能体通过观察环境的状态，采取相应的行动，并根据环境给予的奖励或惩罚来调整自己的策略，以获得更大的累积奖励。

强化学习的核心概念包括：
1. 状态（State）：描述智能体和环境的特征或条件。

2. 行动（Action）：智能体在某个状态下可以选择的动作。

3. 奖励（Reward）：环境根据智能体的行动给予的反馈，用于评估行动的好坏。

4. 策略（Policy）：智能体在特定状态下选择行动的规则或策略。

5. 值函数（Value Function）：评估智能体在某个状态下采取行动的价值，用于指导决策。

6. Q值函数（Q-Value Function）：评估智能体在某个状态下选择某个行动的价值。

强化学习的核心思想是通过不断尝试和反馈来优化策略，使智能体能够在环境中学习并逐渐提高性能。

常见的强化学习算法包括Q-Learning、Deep Q-Network（DQN）、Policy Gradient等。

强化学习在许多领域都有广泛的应用，如智能游戏玩家、机器人控制、金融交易等。

通过强化学习，智能体能够从与环境的交互中不断优化策略，实现自主学习和决策的能力。

深度强化学习在人工智能领域前沿

深度强化学习在人工智能领域前沿人工智能（Artificial Intelligence，简称AI）是目前科技领域最炙手可热的话题之一。

随着技术的快速发展，AI的应用范围也越来越广泛，其中深度强化学习（Deep Reinforcement Learning，简称DRL）作为AI领域的前沿技术，正逐渐成为实现人工智能智能化的重要手段。

深度强化学习是指通过模拟智能体与环境的交互行为，使其通过自我学习、积累经验，通过奖励反馈机制不断优化提高自己的行为策略。

与传统的机器学习算法相比，深度强化学习不仅可以通过大量真实样本进行训练，而且可以根据环境和目标的不同进行自我调整和适应，从而实现更为智能化的决策。

随着深度强化学习的发展，它在人工智能领域特别是视觉感知、自然语言处理、机器人等方面已经取得了显著的成果。

以视觉感知为例，深度强化学习可以通过大规模训练数据集和强化学习算法来实现对图像、视频等视觉信息的有效识别和理解，从而大大提高了计算机对于视觉感知能力。

在自然语言处理领域，深度强化学习可以有效地利用大规模的文本数据进行学习，通过建立语言模型和语义模型，实现对语言的理解和生成。

这使得机器可以更好地处理语音识别、机器翻译、问答系统等自然语言处理任务，为人们提供更加便利的交互方式。

此外，在机器人领域，深度强化学习可以帮助机器人通过感知和控制实现更复杂的人机交互。

通过模拟现实情境，机器人可以通过深度强化学习来学习和改善自己的运动策略，使得机器人在复杂和多样的环境中更加灵活和智能地行动。

虽然深度强化学习在人工智能领域取得了许多突破，但也面临着一些挑战。

首先，由于深度强化学习需要大量的训练数据和计算资源，对于一些特定任务和场景的应用仍存在一定的局限性。

此外，深度强化学习的训练过程可能会面临长时间的迭代和尝试，并需要进行针对性的优化和调整，使得算法的开发周期较长。

为了进一步推动深度强化学习在人工智能领域的应用和发展，我们需要加强对该领域的研究与合作。

《人工智能基础》名词术语

《人工智能基础》名词术语人工智能基础一、引言人工智能（Artificial Intelligence，简称AI）是近年来发展迅猛的前沿科学领域之一。

随着大数据和计算能力的快速增长，人工智能正在逐渐渗透到我们的日常生活和各个行业中。

本文将介绍人工智能基础的一些重要名词术语，帮助读者理解和应用人工智能技术。

二、机器学习机器学习（Machine Learning）指机器通过数据和算法自动进行学习和优化，从而不断改进性能。

监督学习（Supervised Learning）是一种常见的机器学习方法，它通过给机器提供带有标签的训练数据，让机器学习到输入数据和输出标签之间的关系。

无监督学习（Unsupervised Learning）则不需要标签，机器可以自主学习数据中的模式和结构。

三、深度学习深度学习（Deep Learning）是一种基于神经网络的机器学习方法，它模拟了人脑神经元之间的连接方式，通过多层的神经网络结构来进行学习和特征提取。

卷积神经网络（Convolutional Neural Network，简称CNN）是一种常用的深度学习结构，广泛应用于图像识别和计算机视觉领域。

四、自然语言处理自然语言处理（Natural Language Processing，简称NLP）是研究如何使机器能够理解和处理人类语言的一门技术。

文本分类（Text Classification）是NLP中的一项重要任务，它通过对文本进行分类或标记，实现对大规模文本数据的自动处理和分析。

情感分析（Sentiment Analysis）则是一种常见的文本分类应用，它可以判断文本中蕴含的情绪倾向。

五、强化学习强化学习（Reinforcement Learning）是一种通过试错学习来优化机器行为的方法，它通过与环境的交互，根据反馈信号对机器的行动进行调整和优化。

Q学习（Q-Learning）是强化学习中的一种常用算法，通过学习和更新动作值函数来实现智能体的决策策略的优化。

双足机器人平衡控制及步态规划研究

摘要摘要驱动技术，人工智能，高性能计算机等最新技术已经使双足机器人有了粗略模拟人体运动的灵巧性，能够进行舞蹈展示，乐器演奏，与人交谈等。

然而这与投入实际应用所需求的能力还有不小差距。

主要体现在缺乏与人类相近的平衡能力和步伐协调能力，对工作环境要求高，在非结构化环境中适应能力差。

因此，本文以自主研制的双足机器人为研究对象，重点研究了双足机器人的平衡控制，阻抗控制以及步态规划等内容。

本文首先简要介绍了自主研制的双足机器人的软硬件构架，建立了ADAMS 和Gazebo仿真来协助对控制算法性能预测和优化并减少对物理机器人的危险操作。

接着分析了双足机器人的正逆运动学并引入运动学库KDL来简化运动学运算。

稳定的平衡控制对于双足机器人而言在目前还是个不小的挑战。

本文就此研究了两种处理平衡的阻抗调节方案。

一种是基于LQR的固定阻抗模型，这种方案简单有效，但存在易产生振动的问题，本文结合滤波改善了平衡控制效果。

另一种是基于增强学习的自适应阻抗模型。

该方法可以在不知道系统内部动态信息的情况下利用迭代策略在线得到最优解，是对前述LQR方法的进一步优化。

随后本文通过仿真和实验进行了验证并分析了优缺点。

步态规划是机器人运动控制中最基础的一环。

本文从五连杆平面机器人入手对其运动控制进行了研究。

首先采用基于ZMP的多项式拟合法实现了机器人平地行走的步态规划。

然后分析其动力学模型并利用PD控制器进行运动仿真，就仿真中出现双腿支撑阶段跟踪误差较大的问题提出了PD与径向基神经网络混合控制的新策略。

再次通过仿真证实该方案能够减小跟踪误差。

最后，本文利用前述多项式拟合法对实验平台的物理机器人进行静态行走和上楼梯的步态规划。

针对上楼梯的步态规划的特殊性，本文提出了分段拟合来实现各关节的协同规划，并引入了躯干前倾角来辅助身体平衡。

由于时间所限，本文实现了双足机器人的稳定步行实验，上楼梯实验还尚缺稳健性，这将作为下一步的工作。

关键词：双足机器人，平衡控制，步态规划，ADAMS仿真，增强学习IABSTRACTDriving technology, artificial intelligence, high-performance computers and other latest technology has enable bipedal robots to roughly emulate the motor dexterity of humans, able to dance show, musical instruments, and talking. However, this ability still have big gap between putting into practical application. Mainly reflected in the lack of the ability of balance, and the coordination of walking. High demands on the working environment, poor adaptability in unstructured environments. In this paper, the self-developed bipedal humanoid robot is researched, and the balance control, impedance control and gait planning are mainly studied.This paper first introduces the hardware and software architecture of the biped robot, and establishes the ADAMS and Gazebo simulation to assist in the prediction and optimization of the performance of the control algorithm, so as to reduce the risk operation of the physical robot and avoiding the potential risks. Then the forward kinematics and inverse kinematics of the biped robot are analyzed and the kinematic library KDL is introduced to simplify the kinematic operation.Stable balance control is still a challenge for biped robots. In this paper, we present two schemes for impedance adjustment when dealing with the balance. One is the fixed impedance model, which is simple and effective, but there is a problem of vibration, a filter is combined in this paper to improve the balance control effect. The other is an adaptive impedance model based on integral reinforcement learning. This method can obtain the optimal solution online by using the policy iteration without knowing the dynamic information of the system. It is a further optimization of the LQR method. Then the scheme is simulated and experimented, and the advantages and disadvantages are analyzed.Gait planning is the most basic part of robot motion control. First, a simplified five-link planar robot model is established to facilitate the study. Then, the ZMP-based polynomial fitting method is used to realize the gait planning of the robot's horizontal walking. Then the dynamic model is analyzed and the PD controller is used to simulate the motion. A new strategy of PD and RBF neural network hybrid control is proposed to reduce the tracking error during DSP. Again, the simulation results show that the scheme can reduce the tracking error.IIFinally, this paper applies the polynomial fitting method to carry on the static walking and the stairway gait planning of the physical robot of the experimental platform. In view of the particularity of the gait planning of the stairs, this paper proposes a partition fitting to realize the cooperative planning of each joint and introduces the trunk leaning forward to assist the body balance. Due to time constraints, this paper has achieved a stable walking experiment of bipedal robots, and the stair experiment is still lacking in robustness, which will be the next step of the work.Keywords: biped robot, balance control, gait planning, ADAMS simulation, reinforcement learningIII目录第一章绪论 (1)1.1 研究工作的背景与意义 (1)1.2 国内外研究历史和发展态势 (2)1.2.1双足机器人的发展现状 (2)1.2.2双足机器人平衡控制概况 (6)1.2.3机器人阻抗控制概况 (7)1.2.4双足机器人步态规划及运动控制概况 (8)1.3 本文的主要工作 (9)1.4 本论文的结构安排 (10)第二章双足机器人控制系统架构与仿真平台设计 (11)2.1 双足机器人机体结构 (11)2.2 双足机器人控制系统框架设计 (13)2.2.1硬件系统设计 (13)2.2.2控制软件设计 (15)2.3 双足机器人仿真平台的设计 (16)2.3.1机器人系统常用仿真软件 (16)2.3.2ADAMS虚拟样机建模 (17)2.3.3G AZEBO模型建立 (18)2.4 本章小结 (19)第三章双足机器人运动学建模分析 (20)3.1 双足机器人位姿的描述 (20)3.2 正向运动学求解 (21)3.3 逆运动学求解 (22)3.4 五连杆平面机器人的运动仿真 (26)3.4.1开源运动学和动力学库KDL (26)3.4.2基于KDL的双足机器人运动学仿真 (26)3.5 本章小结 (27)第四章双足机器人站姿下的平衡控制 (28)4.1 双足机器人的平衡控制策略 (28)4.2 双足机器人的踝关节平衡策略 (30)IV4.2.1基于倒立摆的固定阻抗模型 (31)4.2.2基于增强学习的自适应阻抗模型 (33)4.3 仿真结果 (38)4.3.1固定阻抗与自适应阻抗仿真结果及对比 (38)4.3.2仿真算法的进一步优化 (41)4.4 实验结果 (43)4.4.1实验设计 (43)4.4.2实验结果与分析 (44)4.5 本章小结 (47)第五章五连杆双足机器人行走步态规划及控制 (48)5.1 步态规划依据和方法 (48)5.1.1步态规划的依据 (48)5.1.2离线步态规划的方法 (49)5.2 五连杆平面机器人模型的建立 (49)5.2.1五连杆模型简介 (50)5.2.2五连杆的运动学与动力学模型 (51)5.3 五连杆机器人的步态规划 (53)5.3.1摆动腿的轨迹规划 (53)5.3.2髋关节的轨迹规划 (55)5.3.3轨迹规划展示 (56)5.4 基于PD控制器的五连杆运动控制 (57)5.4.1PD控制器设计 (58)5.4.2仿真实验结果及分析 (59)5.5 基于RBFNN的五连杆运动控制 (61)5.5.1基于动力学模型的控制分析 (61)5.5.2RBF神经网络控制器设计 (62)5.5.3仿真实验结果及分析 (64)5.6 本章小结 (65)第六章双足机器人步态规划与实验 (66)6.1 双足机器人步态规划的约束 (66)6.2 双足机器人静态行走的步态规划 (66)6.2.1步行准备阶段运动规划 (67)6.2.2周期步行阶段运动规划 (69)V6.2.3步态仿真验证 (71)6.2.4双足机器人步行实验 (73)6.3 双足机器人上楼梯的步态规划 (73)6.3.1起步阶段运动规划 (73)6.3.2上楼梯双腿支撑阶段运动规划 (74)6.3.3跨两层台阶运动规划 (75)6.3.4双足机器人上楼梯仿真及实验 (76)6.4 本章小结 (78)第七章全文总结与展望 (79)7.1 全文总结 (79)7.2 后续工作展望 (80)致谢 (81)参考文献 (82)攻读硕士学位期间取得的成果 (87)VI第一章绪论第一章绪论1.1 研究工作的背景与意义上世纪60年代初，工业机器人和自主移动机器人成为现实，为实现大规模自动化生产，降低制造成本提升产品质量做出了巨大贡献。

深度强化学习原理及其在机器人运动控制中的运用

深度强化学习原理及其在机器人运动控制中的运用深度强化学习（Deep Reinforcement Learning）是机器学习的一个分支，结合了深度学习和强化学习的技术，用于解决具有高度复杂性和无监督信息的环境中的决策问题。

在深度强化学习中，智能体通过试错的方式与环境进行交互，从而学习到最优行为策略。

深度学习是一种强大的机器学习技术，可以通过神经网络模型对复杂的非线性关系进行建模和学习。

在深度强化学习中，深度学习模型被用于估计智能体在不同状态下采取不同行动的价值函数。

价值函数表示了在给定状态下采取不同行动的预期回报值，智能体通过最大化价值函数来选择最优的行动策略。

强化学习是一种无监督学习的方法，通过智能体与环境的交互来优化策略。

在深度强化学习中，智能体通过观察环境的当前状态，选择行动并观察环境给出的奖励信号，来更新策略。

通过不断与环境交互和反馈，智能体可以学习到最优的行动策略。

在机器人运动控制中，深度强化学习可以用于解决复杂的动作决策问题。

传统的机器人控制方法通常需要手动设计特征和规则，这在面对高度复杂的环境和任务时变得十分困难。

而深度强化学习可以通过与环境的交互学习到最优行动策略，无需手动设计特征和规则。

深度强化学习在机器人运动控制中的应用可以分为两个方面。

第一个方面是在无模型控制中的应用，也称为模型自由控制。

在无模型控制中，智能体通过与环境的交互学习到最优的行动策略，根据当前状态直接选择行动，而无需对环境的动力学模型进行建模和预测。

这种方法可以用于解决机器人在复杂环境中自主导航、物体抓取等任务。

第二个方面是在有模型控制中的应用，也称为模型引导控制。

在有模型控制中，智能体不仅学习最优行动策略，还建立了对环境的动力学模型。

这个模型可以预测给定动作下环境的状态转移和奖励，通过模型的引导，智能体可以在规划阶段预测不同行动的后果，并选择最佳路径来实现目标。

这种方法可以用于高精度的机械臂控制、运动规划和路径规划等任务。

强化学习简介ppt

•强化学习简介
• Reinforcement Learning
什么是机器学习（ Machine Learning）？
机器学习是一门多领域交叉学科，涉及概率论、统计学、逼近论、凸分析、算法复杂度理论等多门学科。专门研究计算机怎样模拟或实现人类的学习行为，以获取新的知识或技能，重新组织已有的知识结构使之不断改善自身的性能。
28
当智能体采用策略π时，累积回报服从一个分布，累积回报在状态s处的期望值定义为状态值函数：
29
例
30
例
31
例
32
例
33
贝尔曼方程状态值函数可以分为两部分： •瞬时奖励 •后继状态值函数的折扣值
34
35
36
马尔可夫决策过程
马尔可夫决策过程是一种带有决策作用的马尔科夫奖励过程，由元组（S，A，P， R, γ ）来表示 •S为有限的状态集 •A为有限的动作集 •P为状态转移概率
9
10
11
12
强化学习基本要素
强化学习基本要素及其关系
13
• 策略定义了agent在给定时间内的行为方式，一个策略就是从环境感知的状态到在这些状态中可采取动作的一个映射。
• 可能是一个查找表，也可能是一个函数 • 确定性策略：a = π(s) • 随机策略： π(a ∣ s) = P[At = a ∣ St = s]
3
强化学习（reinforcement learning）与监督学习、非监督学习的区别
没有监督者，只有奖励信号反馈是延迟的，不是顺时的时序性强，不适用于独立分布的数据自治智能体（agent)的行为会影响后续信息的
接收
4
思考：
• 五子棋：棋手通过数学公式计算，发现位置 1比位置2价值大，这是强化学习吗？

rlhf方法

RLHF方法1. 引言强化学习（Reinforcement Learning, RL）是一种机器学习方法，用于训练智能体在与环境交互的过程中自主学习最优策略。

在强化学习中，一个智能体通过观察环境的状态，采取动作，并根据环境的反馈来调整自己的行为。

近年来，随着深度学习的兴起，结合深度神经网络和强化学习的方法取得了显著的进展。

RLHF（Reinforcement Learning with Human Feedback）方法是一种结合人类反馈和强化学习的方法。

传统的强化学习方法通常需要大量的交互次数才能达到较好的性能，而RLHF方法通过利用人类专家提供的反馈信息，可以加速智能体在环境中学习到最优策略。

2. RLHF方法原理RLHF方法基于以下假设：人类专家可以提供有关最优行为或者性能评估方面宝贵信息。

因此，在训练过程中将人类专家作为辅助来指导智能体的决策。

2.1 数据采集阶段在数据采集阶段，首先由人类专家来执行任务，并记录下其在每个状态下采取的动作。

这些数据被称为专家演示（expert demonstration）数据。

可以通过让专家玩游戏、开车等方式获得这些数据。

2.2 强化学习阶段在强化学习阶段，使用专家演示数据和强化学习算法来训练智能体。

传统的强化学习算法通常是基于奖励信号进行优化，而RLHF方法则引入了人类反馈。

在每个状态下，智能体会根据当前策略选择一个动作，并将该动作发送给人类专家。

人类专家会对该动作进行评估，然后给出一个反馈信号，表示该动作的好坏程度。

这个反馈信号可以是一个简单的二进制标签（如好或坏），也可以是一个连续的分数。

智能体会将专家反馈信息与奖励信号结合起来进行策略优化。

具体来说，可以通过最大化奖励信号和最小化与专家反馈之间的差异来调整策略参数。

2.3 交互迭代阶段在交互迭代阶段，智能体会不断地与环境交互，通过与环境的交互来进一步改进策略。

在每次与环境交互后，智能体会将新的经验数据与专家演示数据结合起来进行策略更新。

Reinforcement learning in robotics A survey

ArticleReinforcement learning in robotics: A survey The International Journal of Robotics Research32(11)1238–1274©The Author(s)2013Reprints and permissions: /journalsPermissions.nav DOI:10.1177/0278364913495721 Jens Kober1,2,J.Andrew Bagnell3and Jan Peters4,5AbstractReinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors.Conversely,the challenges of robotic problems provide both inspiration,impact,and validation for develop-ments in reinforcement learning.The relationship between disciplines has sufﬁcient promise to be likened to that between physics and mathematics.In this article,we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots.W e highlight both key chal-lenges in robot reinforcement learning as well as notable successes.W e discuss how contributions tamed the complexity of the domain and study the role of algorithms,representations,and prior knowledge in achieving these successes.As a result,a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods.By analyzing a simple problem in some detail we demonstrate how rein-forcement learning approaches may be proﬁtably applied,and we note throughout open questions and the tremendous potential for future research.KeywordsReinforcement learning,learning control,robot,survey1.IntroductionA remarkable variety of problems in robotics may be naturally phrased as problems of reinforcement learning. Reinforcement learning enables a robot to autonomously discover an optimal behavior through trial-and-error inter-actions with its environment.Instead of explicitly detail-ing the solution to a problem,in reinforcement learning the designer of a control task provides feedback in terms of a scalar objective function that measures the one-step performance of the robot.Figure1illustrates the diverse set of robots that have learned tasks using reinforcement learning.Consider,for example,attempting to train a robot to return a table tennis ball over the net(Muelling et al., 2012).In this case,the robot might make an observations of dynamic variables specifying ball position and velocity and the internal dynamics of the joint position and veloc-ity.This might in fact capture well the state s of the sys-tem,providing a complete statistic for predicting future observations.The actions a available to the robot might be the torque sent to motors or the desired accelerations sent to an inverse dynamics control system.A functionπthat generates the motor commands(i.e.the actions)based on the incoming ball and current internal arm observations (i.e.the state)would be called the policy.A reinforcement learning problem is toﬁnd a policy that optimizes the long-term sum of rewards R(s,a);a reinforcement learning algo-rithm is one designed toﬁnd such a(near-)optimal policy. The reward function in this example could be based on the success of the hits as well as secondary criteria such as energy consumption.1.1.Reinforcement learning in the context ofmachine learningIn the problem of reinforcement learning,an agent explores the space of possible strategies and receives feedback on 1Bielefeld University,CoR-Lab Research Institute for Cognition and Robotics,Bielefeld,Germany2Honda Research Institute Europe,Offenbach/Main,Germany3Carnegie Mellon University,Robotics Institute,Pittsburgh,PA,USA4Max Planck Institute for Intelligent Systems,Department of Empirical Inference,Tübingen,Germany5Technische Universität Darmstadt,FB Informatik,FG Intelligent Autonomous Systems,Darmstadt,GermanyCorresponding author:Jens Kober,Bielefeld University,CoR-Lab Research Institute for Cogni-tion and Robotics,Universitätsstraße25,33615Bielefeld,Germany. Email:jkober@cor-lab.uni-bielefeld.deKober et al.1239(a)(b)(c)(d)Fig.1.A small sample of robots with behaviors that were rein-forcement learned.These cover the whole range of aerial vehi-cles,robotic arms,autonomous vehicles,and humanoid robots.(a)The OBELIX robot is a wheeled mobile robot that learned to push boxes (Mahadevan and Connell,1992)with a value-function-based approach.(Reprinted with permission from Srid-har Mahadevan.)(b)A Zebra Zero robot arm learned a peg-in-hole insertion task (Gullapalli et al.,1994)with a model-free policy gradient approach.(Reprinted with permission from Rod Gru-pen.)(c)Carnegie Mellon’s autonomous helicopter leveraged a model-based policy-search approach to learn a robust ﬂight con-troller (Bagnell and Schneider,2001).(d)The Sarcos humanoid DB learned a pole-balancing task (Schaal,1996)using forward models.(Reprinted with permission from Stefan Schaal.)the outcome of the choices made.From this informa-tion,a “good”,or ideally optimal,policy (i.e.strategy or controller)must be deduced.Reinforcement learning may be understood by contrast-ing the problem with other areas of study in machine learn-ing.In supervised learning (Langford and Zadrozny,2005),an agent is directly presented a sequence of independent examples of correct predictions to make in different circum-stances.In imitation learning,an agent is provided demon-strations of actions of a good strategy to follow in given situations (Argall et al.,2009;Schaal,1999).To aid in understanding the reinforcement learning prob-lem and its relation with techniques widely used within robotics,Figure 2provides a schematic illustration of two axes of problem variability:the complexity of sequential interaction and the complexity of reward structure.This hierarchy of problems,and the relations between them,is a complex one,varying in manifold attributes and difﬁcult to condense to something like a simple linear ordering on problems.Much recent work in the machine learning com-munity has focused on understanding the diversity and the inter-relations between problem classes.The ﬁgure should be understood in this light as providing a crude picture of the relationship between areas of machine learning research important forrobotics.Fig.2.An illustration of the inter-relations between well-studied learning problems in the literature along axes that attempt to cap-ture both the information and complexity available in reward sig-nals and the complexity of sequential interaction between learner and environment.Each problem subsumes those to the left and below;reduction techniques provide methods whereby harder problems (above and right)may be addressed using repeated appli-cation of algorithms built for simpler problems (Langford and Zadrozny,2005).Each problem subsumes those that are both below and to the left in the sense that one may always frame the sim-pler problem in terms of the more complex one;note that some problems are not linearly ordered.In this sense,rein-forcement learning subsumes much of the scope of classical machine learning as well as contextual bandit and imi-tation learning problems.Reduction algorithms (Langford and Zadrozny,2005)are used to convert effective solutions for one class of problems into effective solutions for others,and have proven to be a key technique in machine learning.At lower left,we ﬁnd the paradigmatic problem of super-vised learning,which plays a crucial role in applications as diverse as face detection and spam ﬁltering.In these problems (including binary classiﬁcation and regression),a learner’s goal is to map observations (typically known as features or covariates)to actions which are usually a dis-crete set of classes or a real value.These problems possess no interactive component:the design and analysis of algo-rithms to address these problems rely on training and testing instances as independent and identical distributed random variables.This rules out any notion that a decision made by the learner will impact future observations:supervised learning algorithms are built to operate in a world in which every decision has no effect on the future examples consid-ered.Further,within supervised learning scenarios,during a training phase the “correct”or preferred answer is pro-vided to the learner,so there is no ambiguity about action choices.More complex reward structures are also often studied:one example is known as cost-sensitive learning,where each training example and each action or prediction is anno-tated with a cost for making such a prediction.Learning techniques exist that reduce such problems to the sim-pler classiﬁcation problem,and active research directly addresses such problems as they are crucial in practical learning applications.1240The International Journal of Robotics Research32(11)Contextual bandit or associative reinforcement learning problems begin to address the fundamental problem of exploration versus exploitation,as information is provided only about a chosen action and not what might have been. Theseﬁnd widespread application in problems as diverse as pharmaceutical drug discovery to ad placement on the web, and are one of the most active research areas in theﬁeld. Problems of imitation learning and structured prediction may be seen to vary from supervised learning on the alter-nate dimension of sequential interaction.Structured pre-diction,a key technique used within computer vision and robotics,where many predictions are made in concert by leveraging inter-relations between them,may be seen as a simpliﬁed variant of imitation learning(Dauméet al.,2009; Ross et al.,2011a).In imitation learning,we assume that an expert(for example,a human pilot)that we wish to mimic provides demonstrations of a task.While“correct answers”are provided to the learner,complexity arises because any mistake by the learner modiﬁes the future observations from what would have been seen had the expert chosen the controls.Such problems provably lead to compound-ing errors and violate the basic assumption of independent examples required for successful supervised learning.In fact,in sharp contrast with supervised learning problems where only a single data set needs to be collected,repeated interaction between learner and teacher appears to both nec-essary and sufﬁcient(Ross et al.,2011b)to provide perfor-mance guarantees in both theory and practice in imitation learning problems.Reinforcement learning embraces the full complexity of these problems by requiring both interactive,sequential pre-diction as in imitation learning as well as complex reward structures with only“bandit”style feedback on the actions actually chosen.It is this combination that enables so many problems of relevance to robotics to be framed in these terms;it is this same combination that makes the problem both information-theoretically and computationally hard. We note here brieﬂy the problem termed“baseline dis-tribution reinforcement learning”:this is the standard rein-forcement learning problem with the additional beneﬁt for the learner that it may draw initial states from a distri-bution provided by an expert instead of simply an initial state chosen by the problem.As we describe further in Sec-tion5.1,this additional information of which states matter dramatically affects the complexity of learning.1.2.Reinforcement learning in the context ofoptimal controlReinforcement learning is very closely related to the theory of classical optimal control,as well as dynamic program-ming,stochastic programming,simulation-optimization, stochastic search,and optimal stopping(Powell,2012). Both reinforcement learning and optimal control address the problem ofﬁnding an optimal policy(often also called the controller or control policy)that optimizes an objec-tive function(i.e.the accumulated cost or reward),and both rely on the notion of a system being described by an underlying set of states,controls,and a plant or model that describes transitions between states.However,optimal con-trol assumes perfect knowledge of the system’s description in the form of a model(i.e.a function T that describes what the next state of the robot will be given the current state and action).For such models,optimal control ensures strong guarantees which,nevertheless,often break down due to model and computational approximations.In contrast,rein-forcement learning operates directly on measured data and rewards from interaction with the environment.Reinforce-ment learning research has placed great focus on addressing cases which are analytically intractable using approxima-tions and data-driven techniques.One of the most important approaches to reinforcement learning within robotics cen-ters on the use of classical optimal control techniques(e.g. linear-quadratic regulation and differential dynamic pro-gramming(DDP))to system models learned via repeated interaction with the environment(Atkeson,1998;Bagnell and Schneider,2001;Coates et al.,2009).A concise discus-sion of viewing reinforcement learning as“adaptive optimal control”is presented by Sutton et al.(1991).1.3.Reinforcement learning in the context ofroboticsRobotics as a reinforcement learning domain differs con-siderably from most well-studied reinforcement learning benchmark problems.In this article,we highlight the challenges faced in tackling these problems.Problems in robotics are often best represented with high-dimensional, continuous states and actions(note that the10–30dimen-sional continuous actions common in robot reinforcement learning are considered large(Powell,2012)).In robotics, it is often unrealistic to assume that the true state is com-pletely observable and noise-free.The learning system will not be able to know precisely in which state it is and even vastly different states might look very similar.Thus, robotics reinforcement learning are often modeled as par-tially observed,a point we take up in detail in our formal model description below.The learning system must hence useﬁlters to estimate the true state.It is often essential to maintain the information state of the environment that not only contains the raw observations but also a notion of uncertainty on its estimates(e.g.both the mean and the vari-ance of a Kalmanﬁlter tracking the ball in the robot table tennis example).Experience on a real physical system is tedious to obtain, expensive and often hard to reproduce.Even getting to the same initial state is impossible for the robot table tennis sys-tem.Every single trial run,also called a roll-out,is costly and,as a result,such applications force us to focus on difﬁculties that do not arise as frequently in classical rein-forcement learning benchmark examples.In order to learnKober et al.1241within a reasonable time frame,suitable approximations of state,policy,value function,and/or system dynamics need to be introduced.However,while real-world experience is costly,it usually cannot be replaced by learning in simula-tions alone.In analytical or learned models of the system even small modeling errors can accumulate to a substan-tially different behavior,at least for highly dynamic tasks. Hence,algorithms need to be robust with respect to models that do not capture all the details of the real system,also referred to as under-modeling,and to model uncertainty. Another challenge commonly faced in robot reinforcement learning is the generation of appropriate reward functions. Rewards that guide the learning system quickly to success are needed to cope with the cost of real-world experience. This problem is called reward shaping(Laud,2004)and represents a substantial manual contribution.Specifying good reward functions in robotics requires a fair amount of domain knowledge and may often be hard in practice. Not every reinforcement learning method is equally suitable for the robotics domain.In fact,many of the methods thus far demonstrated on difﬁcult problems have been model-based(Atkeson et al.,1997;Abbeel et al., 2007;Deisenroth and Rasmussen,2011)and robot learn-ing systems often employ policy-search methods rather than value-function-based approaches(Gullapalli et al.,1994; Miyamoto et al.,1996;Bagnell and Schneider,2001;Kohl and Stone,2004;Tedrake et al.,2005;Kober and Peters, 2009;Peters and Schaal,2008a,b;Deisenroth et al.,2011). Such design choices stand in contrast to possibly the bulk of the early research in the machine learning community (Kaelbling et al.,1996;Sutton and Barto,1998).We attempt to give a fairly complete overview on real robot reinforce-ment learning citing most original papers while grouping them based on the key insights employed to make the robot reinforcement learning problem tractable.We isolate key insights such as choosing an appropriate representation for a value function or policy,incorporating prior knowledge, and transfer knowledge from simulations.This paper surveys a wide variety of tasks where reinforcement learning has been successfully applied to robotics.If a task can be phrased as an optimization prob-lem and exhibits temporal structure,reinforcement learning can often be proﬁtably applied to both phrase and solve that problem.The goal of this paper is twofold.On the one hand,we hope that this paper can provide indications for the robotics community which type of problems can be tackled by reinforcement learning and provide pointers to approaches that are promising.On the other hand,for the reinforcement learning community,this paper can point out novel real-world test beds and remarkable opportunities for research on open questions.We focus mainly on results that were obtained on physical robots with tasks going beyond typical reinforcement learning benchmarks.We concisely present reinforcement learning techniques in the context of robotics in Section2.The challenges in applying reinforcement learning in robotics are discussed in Section3.Different approaches to making reinforcement learning tractable are treated in Sections4–6.In Section7,the example of a ball in a cup is employed to highlightwhich of the various approaches discussed in the paperhave been particularly helpful to make such a complextask tractable.Finally,in Section8,we summarize the spe-ciﬁc problems and beneﬁts of reinforcement learning inrobotics and provide concluding thoughts on the problemsand promise of reinforcement learning in robotics.2.A concise introduction to reinforcementlearningIn reinforcement learning,an agent tries to maximize theaccumulated reward over its lifetime.In an episodic setting,where the task is restarted after each end of an episode,theobjective is to maximize the total reward per episode.If thetask is on-going without a clear beginning and end,eitherthe average reward over the whole lifetime or a discountedreturn(i.e.a weighted average where distant rewards haveless inﬂuence)can be optimized.In such reinforcementlearning problems,the agent and its environment may bemodeled being in a state s∈S and can perform actionsa∈A,each of which may be members of either discreteor continuous sets and can be multi-dimensional.A state scontains all relevant information about the current situationto predict future states(or observables);an example wouldbe the current position of a robot in a navigation task.1Anaction a is used to control(or change)the state of the sys-tem.For example,in the navigation task we could have theactions corresponding to torques applied to the wheels.Forevery step,the agent also gets a reward R,which is a scalarvalue and assumed to be a function of the state and obser-vation.(It may equally be modeled as a random variablethat depends on only these variables.)In the navigation task,a possible reward could be designed based on the energycosts for taken actions and rewards for reaching targets.The goal of reinforcement learning is toﬁnd a mappingfrom states to actions,called policyπ,that picks actionsa in given states s maximizing the cumulative expectedreward.The policyπis either deterministic or probabilistic.The former always uses the exact same action for a givenstate in the form a=π(s),the later draws a sample froma distribution over actions when it encounters a state,i.e.a∼π(s,a)=P(a|s).The reinforcement learning agentneeds to discover the relations between states,actions,andrewards.Hence,exploration is required which can either bedirectly embedded in the policy or performed separately andonly as part of the learning process.Classical reinforcement learning approaches are basedon the assumption that we have a Markov decision pro-cess(MDP)consisting of the set of states S,set of actionsA,the rewards R and transition probabilities T that capturethe dynamics of a system.Transition probabilities(or den-sities in the continuous state case)T(s ,a,s)=P(s |s,a) describe the effects of the actions on the state.Transitionprobabilities generalize the notion of deterministic dynam-ics to allow for modeling outcomes are uncertain even given1242The International Journal of Robotics Research 32(11)full state.The Markov property requires that the next state s and the reward only depend on the previous state s and action a (Sutton and Barto,1998),and not on additional information about the past states or actions.In a sense,the Markov property recapitulates the idea of state:a state is a sufﬁcient statistic for predicting the future,rendering previ-ous observations irrelevant.In general in robotics,we may only be able to ﬁnd some approximate notion of state.Different types of reward functions are commonly used,including rewards depending only on the current state R =R (s ),rewards depending on the current state and action R =R (s ,a ),and rewards including the transitions R =R (s ,a ,s ).Most of the theoretical guarantees only hold if the problem adheres to a Markov structure,how-ever in practice,many approaches work very well for many problems that do not fulﬁll this requirement.2.1.Goals of reinforcement learningThe goal of reinforcement learning is to discover an optimal policy π∗that maps states (or observations)to actions so as to maximize the expected return J ,which corresponds to the cumulative expected reward.There are different models of optimal behavior (Kaelbling et al.,1996)which result in different deﬁnitions of the expected return.A ﬁnite-horizon model only attempts to maximize the expected reward for the horizon H ,i.e.the next H (time)steps hJ =EHh =0R h .This setting can also be applied to model problems where itis known how many steps are remaining.Alternatively,future rewards can be discounted by a discount factor γ(with 0≤γ<1)J =E∞h =0γh R h .This is the setting most frequently discussed in classicalreinforcement learning texts.The parameter γaffects how much the future is taken into account and needs to be tuned manually.As illustrated by Kaelbling et al.(1996),this parameter often qualitatively changes the form of the opti-mal solution.Policies designed by optimizing with small γare myopic and greedy,and may lead to poor perfor-mance if we actually care about longer-term rewards.It is straightforward to show that the optimal control law can be unstable if the discount factor is too low (e.g.it is not difﬁcult to show this destabilization even for discounted linear quadratic regulation problems).Hence,discounted formulations are frequently inadmissible in robot control.In the limit when γapproaches 1,the metric approaches what is known as the average-reward criterion (Bertsekas,1995),J =lim H →∞E1H H h =0R h .This setting has the problem that it cannot distinguishbetween policies that initially gain a transient of large rewards and those that do not.This transient phase,also called preﬁx,is dominated by the rewards obtained in the long run.If a policy accomplishes both an optimal pre-ﬁx as well as an optimal long-term behavior,it is called bias optimal (Lewis and Puterman,2001).An example in robotics would be the transient phase during the start of a rhythmic movement,where many policies will accomplish the same long-term reward but differ substantially in the transient (e.g.there are many ways of starting the same gait in dynamic legged locomotion)allowing for room for improvement in practical application.In real-world domains,the shortcomings of the dis-counted formulation are often more critical than those of the average reward setting as stable behavior is often more important than a good transient (Peters et al.,2004).We also often encounter an episodic control task,where the task runs only for H time steps and then reset (potentially by human intervention)and started over.This horizon,H ,may be arbitrarily large,as long as the expected reward over the episode can be guaranteed to converge.As such episodic tasks are probably the most frequent ones,ﬁnite-horizon models are often the most relevant.T wo natural goals arise for the learner.In the ﬁrst,we attempt to ﬁnd an optimal strategy at the end of a phase of training or interaction.In the second,the goal is to maxi-mize the reward over the whole time the robot is interacting with the world.In contrast to supervised learning,the learner must ﬁrst discover its environment and is not told the optimal action it needs to take.To gain information about the rewards and the behavior of the system,the agent needs to explore by con-sidering previously unused actions or actions it is uncertain about.It needs to decide whether to play it safe and stick to well-known actions with (moderately)high rewards or to dare trying new things in order to discover new strate-gies with an even higher reward.This problem is commonly known as the exploration–exploitation trade-off .In principle,reinforcement learning algorithms for MDPs with performance guarantees are known (Brafman and Tennenholtz,2002;Kearns and Singh,2002;Kakade,2003)with polynomial scaling in the size of the state and action spaces,an additive error term,as well as in the hori-zon length (or a suitable substitute including the discount factor or “mixing time”(Kearns and Singh,2002)).How-ever,state-spaces in robotics problems are often tremen-dously large as they scale exponentially in the number of state variables and often are continuous.This chal-lenge of exponential growth is often referred to as the curse of dimensionality (Bellman,1957)(also discussed in Section 3.1).Off-policy methods learn independent of the employed policy,i.e.an explorative strategy that is different from the desired ﬁnal policy can be employed during the learn-ing process.On-policy methods collect sample informa-tion about the environment using the current policy.As aKober et al.1243result,exploration must be built into the policy and deter-mines the speed of the policy improvements.Such explo-ration and the performance of the policy can result in an exploration–exploitation trade-off between long-and short-term improvement of the policy.Modeling exploration models with probability distributions has surprising impli-cations,e.g.stochastic policies have been shown to be the optimal stationary policies for selected problems (Jaakkola et al.,1993;Sutton et al.,1999)and can even break the curse of dimensionality (Rust,1997).Furthermore,stochas-tic policies often allow the derivation of new policy update steps with surprising ease.The agent needs to determine a correlation between actions and reward signals.An action taken does not have to have an immediate effect on the reward but can also inﬂuence a reward in the distant future.The difﬁculty in assigning credit for rewards is directly related to the hori-zon or mixing time of the problem.It also increases with the dimensionality of the actions as not all parts of the action may contribute equally.The classical reinforcement learning setup is a MDP where additionally to the states S ,actions A ,and rewards R we also have transition probabilities T (s ,a ,s ).Here,the reward is modeled as a reward function R (s ,a ).If both the transition probabilities and reward function are known,this can be seen as an optimal control problem (Powell,2012).2.2.Reinforcement learning in the averagereward settingWe focus on the average-reward model in this section.Sim-ilar derivations exist for the ﬁnite horizon and discounted reward cases.In many instances,the average-reward case is often more suitable in a robotic setting as we do not have to choose a discount factor and we do not have to explicitly consider time in the derivation.To make a policy able to be optimized by continuous optimization techniques,we write a policy as a conditional probability distribution π(s ,a )=P (a |s ).Below,we con-sider restricted policies that are parametrized by a vector θ.In reinforcement learning,the policy is usually considered to be stationary and memoryless.Reinforcement learning and optimal control aim at ﬁnding the optimal policy π∗or equivalent policy parameters θ∗which maximize the aver-age return J (π)=s ,a μπ(s )π(s ,a )R (s ,a )where μπis the stationary state distribution generated by policy πacting in the environment,i.e.the MDP .It can be shown (Puter-man,1994)that such policies that map states (even deter-ministically)to actions are sufﬁcient to ensure optimality in this setting:a policy needs neither to remember previous states visited,actions taken,or the particular time step.For simplicity and to ease exposition,we assume that this dis-tribution is unique.MDPs where this fails (i.e.non-ergodic processes)require more care in analysis,but similar results exist (Puterman,1994).The transitions between states s caused by actions a are modeled as T (s ,a ,s )=P (s |s ,a ).We can then frame the control problem as an optimization ofmax πJ (π)=s ,a μπ(s )π(s ,a )R (s ,a ),(1)s.t.μπ(s )=s ,a μπ(s )π(s ,a )T (s ,a ,s ),∀s ∈S ,(2)1=s ,a μπ(s )π(s ,a )(3)π(s ,a )≥0,∀s ∈S ,a ∈A .Here,Equation (2)deﬁnes stationarity of the state distribu-tions μπ(i.e.it ensures that it is well deﬁned)and Equation(3)ensures a proper state–action probability distribution.This optimization problem can be tackled in two substan-tially different ways (Bellman,1967,1971).We can search the optimal solution directly in this original,primal prob-lem or we can optimize in the Lagrange dual formulation.Optimizing in the primal formulation is known as policy search in reinforcement learning while searching in the dual formulation is known as a value-function-based approach .2.2.1.V alue-function approaches Much of the reinforce-ment learning literature has focused on solving the opti-mization problem in Equations (1)–(3)in its dual form (Puterman,1994;Gordon,1999).2Using Lagrange multi-pliers V π sand ¯R,we can express the Lagrangian of the problem byL = s ,aμπ(s )π(s ,a )R (s ,a )+sV π(s )s ,aμπ(s )π(s ,a )T (s ,a ,s )−μπ(s )+¯R1−s ,aμπ(s )π(s ,a )=s ,aμπ(s )π(s ,a ) R (s ,a )+sV π(s )T (s ,a ,s )−¯R−sV π(s )μπ(s )aπ(s ,a )=1+¯R .Using the property s ,a V (s )μπ(s )π(s ,a)= s ,aV (s )μπ(s )π(s ,a ),we can obtain the Karush–Kuhn–Tucker conditions (Kuhn and Tucker,1950)by differen-tiating with respect to μπ(s )π(s ,a )which yields extrema at∂μππL =R (s ,a )+sV π(s )T (s ,a ,s )−¯R−V π(s )=0.This statement implies that there are as many equations asthe number of states multiplied by the number of actions.For each state there can be one or several optimal actions a ∗that result in the same maximal value and,hence,canbe written in terms of the optimal action a ∗as V π∗(s )=R (s ,a ∗)−¯R+ s V π∗(s)T (s ,a ∗,s ).As a ∗is generated by。

对机器人进行示教时,英语作文

对机器人进行示教时,英语作文英文回答：Teaching robots is a complex and challenging task that requires a combination of technical expertise, patience, and creativity. The process of teaching a robot involves programming it to perform specific tasks or behaviors and then testing and refining the program until the desired results are achieved.One of the most common methods of teaching robots is through the use of physical demonstrations. In this approach, the human teacher demonstrates the desired task or behavior to the robot, which then attempts to imitate the actions. This method can be effective for teaching simple tasks, such as picking up objects or moving around. However, it can be more difficult for teaching complex tasks, such as navigating through a cluttered environment or interacting with humans.Another method of teaching robots is through the use of reinforcement learning. In this approach, the robot is given a set of goals and then allowed to explore its environment through trial and error. The robot receives positive reinforcement for achieving its goals and negative reinforcement for making mistakes. Over time, the robot learns to associate certain actions with positive outcomes and other actions with negative outcomes, and it adapts its behavior accordingly.Reinforcement learning can be effective for teaching robots complex tasks, such as driving a car or playing a game. However, it can be a slow and inefficient process, and it can be difficult to design the right reward system for the robot.In addition to physical demonstrations and reinforcement learning, there are a number of other methods that can be used to teach robots. These methods include supervised learning, unsupervised learning, and imitation learning. The best method for teaching a robot a particular task will depend on the specific task and the capabilitiesof the robot.中文回答：机器人教学。

强化学习算法中的反向动力学方法详解(五)

强化学习（Reinforcement Learning，RL）作为一种机器学习方法，在近年来受到了广泛的关注。

其中，反向动力学方法是强化学习算法中的一种重要方法，它通过对环境的建模和状态-动作的映射来实现智能体的学习和决策。

本文将对强化学习算法中的反向动力学方法进行详细的介绍和分析。

一、强化学习概述强化学习是一种基于奖励机制的学习方法，其目标是让智能体在与环境的交互中通过试错来学习最优的决策策略。

在强化学习中，智能体通过与环境的交互不断地尝试各种动作，并根据环境的反馈来调整自己的决策策略。

这种试错学习的过程，正是强化学习与传统监督学习和无监督学习的最大区别之一。

二、反向动力学方法的基本原理反向动力学方法是强化学习中一种常用的学习方法，其基本原理是通过对环境的建模和状态-动作的映射来实现智能体的学习和决策。

在反向动力学方法中，智能体首先需要对环境进行建模，即学习环境的状态空间和动作空间。

然后，智能体通过与环境的交互不断地尝试各种动作，并根据环境的反馈来调整自己的决策策略。

最终，智能体通过不断地试错学习来找到最优的策略。

三、反向动力学方法的应用反向动力学方法在强化学习中有着广泛的应用。

其中，基于模型的反向动力学方法是一种常用的方法，它通过对环境的建模和状态-动作的映射来实现智能体的学习和决策。

在基于模型的反向动力学方法中，智能体首先需要对环境进行建模，即学习环境的状态空间和动作空间。

然后，智能体通过与环境的交互不断地尝试各种动作，并根据环境的反馈来调整自己的决策策略。

最终，智能体通过不断地试错学习来找到最优的策略。

另外，基于价值函数的反向动力学方法也是强化学习中常用的一种方法。

在基于价值函数的反向动力学方法中，智能体通过学习状态的价值函数来实现决策。

智能体首先需要对环境进行建模，并学习状态的价值函数。

然后，智能体根据状态的价值来做决策，从而实现学习和决策的过程。

四、反向动力学方法的优势和局限反向动力学方法作为强化学习中的一种重要学习方法，其具有许多优势。

基于深度强化学习的无人驾驶技术研究

基于深度强化学习的无人驾驶技术研究一、引言在科技与工业的快速发展下，无人驾驶汽车（自动驾驶系统）已经逐渐被广泛关注与研究。

随着国家和行业对无人驾驶技术的重视，采用深度强化学习进行无人驾驶技术研究已成为主要研究方向。

本文旨在综述当前基于深度强化学习的无人驾驶技术研究所取得的成果。

二、深度强化学习基础深度强化学习（Deep Reinforcement Learning，DRL）是深度学习（Deep Learning，DL）与强化学习（Reinforcement Learning，RL）的结合。

强化学习是通过对环境与奖励进行交互学习，从而使系统得到最优解决方案的一种学习方法。

深度学习是通过构建深层神经网络，并利用大量数据进行训练，从而解决复杂的模式识别和决策问题。

DRL通过多层神经网络学会对环境进行预测，同时利用奖励信号进行加强，最终实现决策动作。

DRL在图像识别、自然语言处理等领域已经广泛运用。

三、无人驾驶基础无人驾驶汽车（自动驾驶系统）是一种无需驾驶员干预的自动化驾驶工具。

其本身可以感知周围环境，并据此做出决策并进行相应的控制。

无人驾驶汽车的核心技术包括高精度地图、激光雷达、摄像头、超声波雷达、毫米波雷达等传感设备，控制决策单元和执行单元。

无人驾驶汽车将传感器采集的环境信息输入到决策单元，再由执行单元执行相应控制。

无人驾驶汽车的发展已走过了从自动驾驶辅助到全自动驾驶阶段。

四、基于深度强化学习的无人驾驶技术研究成果1.自动转弯基于深度强化学习的自动转弯技术是指无人驾驶汽车在行驶过程中通过学习实现自动转弯的一种技术。

该技术可以大幅度提高无人驾驶汽车的驾驶水平。

在深度强化学习的框架下，研究者可以利用大量模拟数据来训练无人驾驶汽车。

在实际道路驾驶中，无人驾驶汽车能够优雅地完成复杂的转弯任务。

与传统的机器学习方法相比，基于深度强化学习的自动转弯技术大幅提高了模型训练的效率和准确性。

2.自动泊车基于深度强化学习的自动泊车技术是指无人驾驶汽车在泊车时通过学习实现自动泊车的一种技术。

【RL系列】马尔可夫决策过程——状态价值评价与动作价值评价

【RL 系列】马尔可夫决策过程——状态价值评价与动作价值评价请先阅读上两篇⽂章：状态价值函数，顾名思义，就是⽤于状态价值评价（SVE ）的。

典型的问题有“格⼦世界（GridWorld ）”游戏（什么是格⼦世界？可以参考：），⾼尔夫游戏，这类问题的本质还是求解最优路径，共性是在学习过程中每⼀步都会由⼀个动作产⽣⼀个特定的状态，⽽到达该状态所获得的奖励是固定的，与如何到达，也就是之前的动作是⽆关的，并且这类问题都有⼀个或多个固定的⽬标。

相⽐较⽽⾔，虽然Multi-Armed Bandit(MAB)问题也可以⽤状态价值评价的⽅法进⾏policy 的求解，不过这个问题本质上还是对动作价值的评价。

因为在MAB 问题中，⼀个动作只能产⽣固定的状态，且⼀个状态只能由⼀个固定的动作产⽣，这种⼀对⼀的关系决定了其对动作的评价可以直接转化为状态评价。

⼀个典型的SVE 问题在转变为动作价值评价（AVE ）问题时（前提是这种转变可⾏），往往奖励机制会发⽣变化，从对状态奖励转变为对当前状态的某⼀动作奖励，因为MAB 问题的动作状态等价，所以这种变化并不明显，本篇也就不再将MAB 问题作为讨论的例⼦了。

本篇将着重分析⼀个典型的SVE 问题和⼀个典型的AVE 问题，从⽽引出SVE 与AVE 在马尔可夫决策过程下的统⼀形式。

这⾥需要强调⼀点的是，bellman ⽅程实质上是由AVE 的思想引出，与之前我在⽂章中所给出的状态价值评价的推导逻辑还是有些许不同的，所以bellman ⽅程并不适合统⼀这两种评价体系。

如果想要详细了解bellman ⽅程，我认为看书(Reinforcement Learning: An Introduction )和阅读这篇⽂章都是不错的选择。

GridWorld简单介绍⼀下“格⼦世界”游戏。

这是⼀个⾮常简单的寻找最优路径的问题，也是⼀个典型的SVE 问题。

给定⼀个有N*N 个⽹格的地图，在这些⽹格中有⼀个或⼏个⽬的地，找出地图上任意⼀个⽹格到达最近⽬的地的最短路径。

fundamental of reinforcement learning -回复

fundamental of reinforcement learning -回复介绍什么是强化学习，强化学习的原理和基本概念以及它在现实生活中的应用。

强化学习是机器学习中一种重要的方法，它旨在通过智能体与环境间的互动来实现自主学习和决策。

强化学习通过试错的方式，通过与环境的不断交互来学习最佳的动作选择策略，以最大化长期累积奖励。

强化学习的核心原理是"奖励驱动的决策"。

智能体与环境进行交互，智能体基于当前的状态，采取动作，并从环境中获得奖励或惩罚。

智能体的目标是通过试错来学习，并通过最大化累计奖励来选择最佳的动作。

在强化学习中，有一些重要的基本概念。

首先是状态（State），状态是指智能体在交互过程中所处的环境的某个瞬时状态。

然后是动作（Action），动作是智能体基于当前状态所采取的行为。

接下来是奖励（Reward），奖励是智能体从环境中获得的反馈信号，它可以是正值、负值或零。

最后是策略（Policy），策略是智能体采取动作的方法或规则。

强化学习的目标是找到最佳策略，使得累积奖励最大化。

强化学习在现实生活中有广泛的应用。

其中一个典型的应用是在机器人领域。

例如，可以训练机器人通过与环境的不断交互来学习最佳的动作选择，以完成特定的任务。

强化学习还可以应用于自动驾驶汽车，通过学习来优化驾驶决策，提高行驶安全性。

此外，强化学习还可以应用于资源分配问题，如电力市场中的负荷调度和风电场中的风机控制等。

在强化学习中，有许多重要的算法和技术被开发出来。

其中一个重要的算法是Q学习（Q-Learning），它是一种无模型的学习方法，通过更新一个状态-动作对的价值函数，来学习最佳的动作选择策略。

另一个重要的算法是深度强化学习（Deep Reinforcement Learning），它结合了深度神经网络和强化学习，可以处理更复杂的问题，并取得了一些重要的突破，如AlphaGo击败人类围棋冠军。

总结起来，强化学习是一种通过试错的方式来学习最佳动作选择策略的机器学习方法。

基于深度强化学习的机械臂自适应阀门旋拧方法研究

(4)
(5)
其中，单位为毫米。
2.2 阀门旋拧过程中的模型训练
本文基于DDPG实现对阀门旋拧策略模型的学习训练。基于DDPG的训练目标为寻找最优网络参数θ，使得阀门旋拧策略μ最优，如图3所示。
该算法在状态st时，根据策略μ选取动作at式(6)，并在动作执行后，返回新的状态和奖励（rt，st+1）。策略网络μ会将（st，at，rt，st+1）存入记忆池（Replay Memory，RM），作为训练行为网络的数据集。记忆池的使用，可以减少算法的不稳定性。
(6)
其中，μ为策略函数，θ为策略参数，s为当前状态。即状态为s时，相同策略的动作是唯一确定的。
网络训练时，会从RM中随机采样N个数据，作为行为策略网络μ、行为价值网络Q的一个mini-batch训练数据，mini-batch中的单个数据记为（si，ai，ri，si+1）。
首先依据式(7)计算行为价值网络Q的梯度，并更新该网络。
基于直径为200mm，400mm，500mm的阀门手轮的测试结果如表2所示。虽然所提出算法是基于单一尺寸规格的阀门手轮进行训练，但在阀门手轮尺寸未知的情况下仍然可以旋拧其他尺寸规格的阀门手轮，表明所提出算法具有较好的适应性。
表2 实验仿真测试结果
直径/mm
200
300
400
500
平均径向误差绝对值
参考文献：
[1] OECD (2014), R&D and innovation needs for decommissioning nuclear facilities,radioactive waste management,OECD publishing, Paris.

1、下载文档前请自行甄别文档内容的完整性，平台不提供额外的编辑、内容补充、找答案等附加服务。
2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
3、如文档侵犯您的权益，请联系客服反馈,我们会尽快为您处理(人工客服工作时间：9:00-18:30)。

Reinforcement Learning in Single RobotHose Transport Task:A PhysicalProof of ConceptJose Manuel Lopez-Guede,Julián Estévez and Manuel GrañaAbstract In this paper we address the physical realization of proof of concept exper-iments demonstrating the suitability of the controllers learned by means of Rein-forcement Learning(RL)techniques to accomplish tasks involving Linked Multi-Component Robotic System(LMCRS).In this paper,we deal with the task of trans-porting a hose by a single robot as a prototypical example of LMCRS,which can be extended to much more complex tasks.We describe how the complete system has been designed and built,explaining its diﬀerent main components:the RL controller, the communications,andﬁnally,the monitoring system.A previously learned RL controller has been tested solving a concrete problem with a determined state space modeling and discretization step.This physical realization validates our previous published works carried out through computer simulations,giving a strong argu-ment in favor of the suitability of RL techniques to deal with real LMCRS systems. Keywords Reinforcement learning⋅Linked multicomponent robotic systems⋅LMCRS⋅Hose transport⋅Proof of concept1IntroductionLinked Multi-Component Robotic Systems(LMCRS)[1]are composed of a col-lection of autonomous robots linked by aﬂexible one-dimensional link introducing additional non-linearities and uncertainties when designing the control of the robots to accomplish a given task.A paradigmatic task example is the transportation of a hose-like object by the robots(or only one robot in its simplest form).There are no relevant references in the literature about this line of research apart from the work done by our group.Theﬁrst attempts to deal with this problem,modeled the task as a cooperative control problem[2],but this resulted in a too low level approach lacking the intended autonomous learning.The work reported in[3,4]representedJ.M.Lopez-Guede(✉)⋅J.Estévez⋅M.GrañaComputational Intelligence Group of the BasqueCountry University(UPV/EHU),San Sebastian,Spaine-mail:jm.lopez@ehu.es297©Springer International Publishing Switzerland2015Á.Herrero et al.(eds.),10th International Conference on Soft Computing Modelsin Industrial and Environmental Applications,Advances in Intelligent Systemsand Computing368,DOI10.1007/978-3-319-19719-7_26298J.M.Lopez-Guede et al.a great breakthrough:a powerful tool was developed based on Geometrically Exact Dynamic Splines(GEDS)[5–7]to execute accurate simulations of LMCRS,allow-ing to assess the dynamical eﬀects of the linking element of the LMCRS[8].Using that tool,the work focused on the autonomous learning of optimal policies by Rein-forcement Learning(RL)[9]reformulating the task as a Markov Decision Process (MDP)[9–11].Within the RL paradigm,the Q-Learning algorithm[9,12,13]was implemented because it allows the agent to learn from its experience with the envi-ronment,without previous knowledge.Several works have been reported[14–19] using that algorithm improving the optimality of the reached results.Following the philosophical approach of using RL techniques that learn only from the experience, the TRQ-Learning algorithm was introduced in[20]reaching better results with boosted convergence.However,these results were always the fruit of computer sim-ulations.The main objective of the paper is to execute a proof of concept physical exper-iment of the task in the simplest instance of a LMCRS(with only one robot)to demonstrate that the computational simulation results are transferable to real physi-cal world systems.To achieve this objectiveﬁrst it is necessary to build a complete physical system composed of the hose to transport,the robot which transports the hose,the RL controller that controls the execution of the task,the communications interface connecting the RL controller and the robot and,ﬁnally,a perception system to monitor the evolution of the task execution.The paper is structured as follows.Section2details the design of the diﬀerent parts of the system and their construction to carry out the proof of concept exper-iments.The concrete experimental design is given in Sect.3.Section4discusses the results obtained in the experimental realization.Finally,Sect.5summarizes the obtained results and addresses further work.2System Design and Construction2.1Global Scheme ControlFigure1shows the main speciﬁc modules of which the system is composed in this proof of concept,and whose boxes are highlighted with a thicker trace:Fig.1Speciﬁc closed loop of the general systemReinforcement Learning in Single Robot Hose Transport (299)–A control module,built according to the optimal policy learned by means of a RL algorithm.–A communications module in charge of the transmission of the action to be exe-cuted by the robot by means of a wireless interface built for the occasion.–The actuator that exerts the action on the environment,i.e.,the robot.–The perception module sensing and monitoring the environment after the actions has been executed to build the new system’s state representation.2.2RL Control ModuleThis module is the responsible for determining the optimal action to be carried out by the agent to reach the goal state(s goal)given an actual state(s′).We have to clarify that the RL controller has been previously learned by means a RL algorithm,and at this point it is executed without any retraining.Therefore,at this stage the controller is in the exploitation phase,so that it is neither able to learn new knowledge nor to improve its performance.The previous required learning phase has been carried out using TRQ-Learning algorithm,reaching a performance of92%successful goals. Anyway,that training phase is carefully described in previous papers[14–20]and it is beyond the scope of this paper.2.3Robot ManagerThe actions that the agent can execute in the environment are movements of the SR1 robot.That robot is quite simple,so we have adapted it to our purpose.We have divided the software functions which we have built into two groups according to their abstraction level.Low level functions This set of low abstraction level functions include all the settings and functions that are necessary to carry out our purpose,but that are not in the interface that the robot oﬀers at RL level.Theﬁrst operation that we had to do was the calibration of the two servo motors to determine the width of pulses necessary to reach a given velocity.This operation was performed only once,but there are other supporting functions executed on demand by the high abstraction level functions.High level functions This set of high abstraction level functions includes the functions that the interface of the robot oﬀers at RL level.The functions that the robot interface oﬀers to the controller are the following:–move_straight(time,velocity):it is used to move straight the SR1robot at spec-iﬁed velocity(between−200mm/s and+200mm/s)during the speciﬁed time (between0and9999s).The product of these magnitudes(ignoring the sign)gives us the absolute distance of the movement.–turn(angle):it turns the SR1robot the speciﬁed angle atﬁxed angular velocity.300J.M.Lopez-Guede et al.–move(time,velocity,angle):itﬁrst calls the turn(.)function to turn the robot in a desired orientation,and then calls the move_straight(.)function to move the robot straight during the speciﬁed time at the desired velocity.2.4Communications ModuleWireless communications The platform that has been used in this implementation does not have wireless capabilities even in its most complete embodiment.It can only receive commands through a wired RS-232interface,but it is not suitable to the environment of our proof of concept because it would introduce a disturbing element (another wire)in the problem which was not considered when the learning agent was trained.So,we have extended the basic SR1platform with wireless capabilities with the19200baud Easy-Radio ER400TRS Data Transceiver at433MHz.This is a transparent radio modem that allows us sending and receiving data frames up to 192bytes,limitation that we take into account while designing the communications interface.On the host PC side,the complementary wireless device is the Devantech RF04radio module,which allows transparent plug and play connection to the host PC.This device also includes another Easy-Radio ER400TRS Data radio modem that can send and receive data of192bytes,but the USB-serial data converter which includes has a buﬀer of only128bytes.Communication protocol A complete speciﬁcation of the communication pro-tocol design is given by means of the syntax of the messages,their semantics and the procedure used to coordinate them.In this case,the protocol provides a connec-tionless service.–SyntaxSyntax speciﬁes the form of the messages and the size and type of each of the ﬁelds of which they are composed.In this case there are only two types of messages. Theﬁrst is the command,sent from the RL controller located in the computer to the SR1robot.The second is the response sent from the SR1robot to the computer.It is not speciﬁed in more detail due to space issues.–SemanticsSemantics speciﬁes the meaning of the messages andﬁelds of which they are com-posed.1.The meaning of the command message is to allow the RL controller to send to theSR1robot the speciﬁcation of the action to be executed.By means the move(time, velocity,angle)interface,the RL controller tells the robot to turn angle degrees, and to move straight during time seconds at velocity m/s.2.The meaning of the response message is to inform the RL controller by the SR1robot manager about the reception of the command message.Each time that a command message is correctly received,the SR1robot manager sends an‘OK’message to indicate that reception.Reinforcement Learning in Single Robot Hose Transport (301)–ProceduresThe procedures specify the order in which the diﬀerent messages are sent between the two interlocutors.In this case,the procedures of the protocol are quite simple because it is a connectionless protocol:ﬁrst the RL controller send to the SR1robot manager the action(movement)to carry out through the command message,and the SR1robot manager answers with a response message if the command message has been correctly received.If this does not happen,the RL controller will not receive that‘OK’response message and a timeout will be triggered.In that circumstance, the RL controller will be able to decide if the same command message must be sent again or not if it suspects that a permanent problem is happening(i.e.,the same command message has been sent several times and the response message does not come back).2.5Visual Perception ModuleIn our proof of concept implementation the sensing is carried out by means of a visual perception module which monitors the relevant objects of the system,i.e.,the robot that performs the task and the hose to transport,and translates the situation of the system into the state space model used in the experiment.The complete description and design of the visual perception system and several experiments are reported in[21].3Experimental Design3.1Transportation Task SpeciﬁcationThe transportation task can be described as follows:given aﬂexible hose with one end attached to aﬁxed position(which could be a source of someﬂuid or electrical energy),transport its tip from a given position P r to a goal position P g by means of a mobile robot attached to it.The robot is able to execute a reduced set of actions (movements)to achieve that objective,avoiding collision with the hose itself which may interfere with reaching the goal position while ensuring that there is not any segment of the hose going outside of the predeﬁned working area.3.2Experimental SettingsWorking area and hose The concrete environment in which the previously described task is carried out is a perfect square of2m of side.The hose length at rest is1m, and one end of it isﬁxed in the center of the square,which will be the center of the coordinate reference system.302J.M.Lopez-Guede et al.Visual perception The complete system monitoring is done through an overhead web cam,placed just in the center of the working area,3.5m over the coordinate reference and deliberately misaligned with the working area.The ground on which the experiments have been performed has been chosen on purpose because it is real-istic enough for the diﬃculty of the task of identifying objects in the images.It is not speciﬁed in more detail due to space issues.Actions,state models,spatial discretization steps Several elements regarding the Markovian formulation of the problem must be formalized.First,the set of actions (movements)that the SR1robot can execute is speciﬁed in Eq.1:A ={North ,South ,East ,West }(1)On the other hand,we have to deﬁne the state modeling,i.e.,the environment per-ception by the agent and the discretization step,because the visual perception system must describe the real working environment according to them.The experiments has used a spatial discretization step of 0,5m with the state model X ={P r ,P g ,i ,c },where P r and P g are the discrete two-dimensional coordinates of the robot and the goal position respectively,i is set to 1if the line intersects the hose and c is set to 1if the box with corners and intersects the hose4Physical Realization ResultsFigure 2shows the realization of the system.In the left column there is the evolution of the system according to the simulator,while in the right column there are the steps of the real world physical realization.The episode of the experiment starts from the initial situation of the system shown in Fig.2(a,b),with the SR1robot and the tip of the hose in the position P r .The goal position P g is indicated through the bold cross that is in the 4-th quadrant.Along the ﬁve necessary steps,all intermediate states of the system are shown in Figs.2(c,e,g,i,k)for the simulated system and in Figs.2(d,f,h,j,l)for the real world physical realization until the goal position P g is reached.Each of these steps is of a length of 0.5m.We can notice that the path that the SR1has followed is optimal,because it is impossible to reach the goal position P g with a lower number of intermediate steps starting from the position P r .Comparing the simulator implementation versus the real physical world implemen-tation realizations,we may notice that the shape of the hose (the linking element)diﬀers slightly in some steps because the linking element implemented in the simula-tor is an ideal hose,where ideal twisting and bending forces have been implemented.However,the real world hose that has been used in the experiment is conditioned by folds,biases and ﬂaws derived from the position in which it has been placed from the manufacturing moment.These minimal diﬀerences are not an issue for the realization of the proof of con-cept experiment which can be considered successful because the real world real-ization shows that the linking element and the global system follow the behaviourReinforcement Learning in Single Robot Hose Transport (303)Fig.2A realization of the system with state model X={P r,P g,i,c}and spatial discretizationstep=0.5m304J.M.Lopez-Guede et al.Fig.2(continued)Reinforcement Learning in Single Robot Hose Transport (305)predicted by the simulated system.We can conclude that the real physical implemen-tation validates our previous simulations,and that the Q matrix learned previously in an autonomous way by means of RL techniques to solve this is concrete system is adequate.5ConclusionsThis paper carries out theﬁrst proof of concept experiment of a single-robot hose transport task driven by a RL controller,which serves as a prototypical LMCRS task. We started referencing several of our previous work where the task was approached from a computational point of view,since there we used tools as a simulator based on GEDS to allow the agent to learn autonomously from its experience with the environment.Once the learner agent has reached a speciﬁed level of knowledge and could exploit it,we demonstrated through simulations that the single-robot case of hose transport task was successfully done.However,proof of concept experiments were still necessary to demonstrate that these results are transferable to real physical world.In this paper we have reported a successfully real world proof of concept experiment,validating our computational tool(simulator)and demonstrating that RL techniques are well suited to deal with real LMCRS tasks.Acknowledgments The research was supported by the Computational Intelligence Group of the Basque Country University(UPV/EHU)through Grant IT874-13of Research Groups Call2013-2017(Basque Country Government).References1.Duro R,Graña M,de Lope J(2010)On the potential contributions of hybrid intelligentapproaches to multicomponen robotic system development.Inf Sci180(14):2635–26482.Lopez-Guede JM,Graña M,Zulueta E(2008)On distributed cooperative control for the manip-ulation of a hose by a multirobot system.In:Corchado E,Abraham A,Pedrycz W(eds)Hybrid artiﬁcial intelligence systems.Lecture notes in artiﬁcial intelligence,vol5271,pp673–679.3rd international workshop on hybrid artiﬁcial intelligence systems,pp24–26.University of Burgos,Burgos,Spain3.Echegoyen Z(2009)Contributions to visual servoing for legged and linked multicomponentrobots.Ph.D.dissertation,UPV/EHU4.Echegoyen Z,Villaverde I,Moreno R,Graña M,d’Anjou A(2010)Linked multi-componentmobile robots:modeling,simulation and control.Rob Auton Syst58(12,SI):1292–13055.Boor CD(1994)A practical guide to splines.Springer6.Rubin M(2000)Cosserat theories:shells.Kluwer,Rods and Points7.Theetten A,Grisoni L,Andriot C,Barsky B(2008)Geometrically exact dynamic -put Aided Des40(1):35–488.Fernandez-Gauna B,Lopez-Guede J,Zulueta E(2010)Linked multicomponent robotic sys-tems:basic assessment of linking element dynamical eﬀect.In:Manuel Grana Romay MGS, Corchado ES(eds.)Hybrid artiﬁcial intelligence systems,Part I,vol6076.Springer,pp73–79 9.Sutton R,Barto A(1998)Reinforcement learning:an introduction.MIT Press306J.M.Lopez-Guede et al.10.Bellman R(1957)A markovian decision process.Indiana Univ Math J6:679–68411.Tijms HC(2004)Discrete-time Markov decision processes.John Wiley&Sons Ltd,pp233–277./10.1002/047001363X.ch612.Watkins C(1989)Learning from delayed rewards.Ph.D.dissertation,University of Cambridge,England13.Watkins C,Dayan P(1992)Technical note:Q-learning.Mach Learn8:279–292.doi:10.1023/A:1022676722315./10.1023/A:102267672231514.Fernandez-Gauna B,Lopez-Guede J,Zulueta E,Graña M(2010)Learning hose transport con-trol with q-learning.Neural Netw World20(7):913–92315.Graña M,Fernandez-Gauna B,Lopez-Guede J(2011)Cooperative multi-agent reinforcementlearning for multi-component robotic systems:guidelines for future research.Paladyn.J Behav Rob2:71–81.doi:10.2478/s13230-011-0017-5./10.2478/s13230-011-0017-5 16.Fernandez-Gauna B,Lopez-Guede JM,Zulueta E,Echegoyen Z,Graña M(2011)Basic resultsand experiments on robotic multi-agent system for hose deployment and transportation.Int J Artif Intell6(S11):183–20217.Fernandez-Gauna B,Lopez-Guede J,Graña M(2011)Towards concurrent q-learning onlinked multi-component robotic systems.In:Corchado E,Kurzynski M,Wozniak M(eds) Hybrid artiﬁcial intelligent systems.Lecture notes in computer science,vol6679.Springer, Berlin/Heidelberg,pp463–47018.Fernandez-Gauna B,Lopez-Guede J,Graña M(2011)Concurrent modular q-learning withlocal rewards on linked multi-component robotic systems.In:Ferrández J,Alvarez Sánchez J, de la Paz F,Toledo F(eds)Foundations on natural and artiﬁcial computation.Lecture notes in computer science,vol6686.Springer,Berlin/Heidelberg,pp148–15519.Lopez-Guede JM,Fernandez-Gauna B,Graña M,Zulueta E(2011)Empirical study of q-learning based elemental hose transport control.In:Corchado E,Kurzynski M,Wozniak M(eds)Hybrid artiﬁcial intelligent systems.Lecture notes in computer science,vol6679.Springer,Berlin/Heidelberg,pp455–46220.Lopez-Guede J,Fernandez-Gauna B,Graña M,Zulueta E(2012)Improving the control ofsingle robot hose transport.Cybern Syst43(4):261–27521.Lopez-Guede JM,Fernandez-Gauna B,Moreno R,Graña M(2012)Robotic vision:technolo-gies for machine learning and vision applications.In:JoséGarcía-Rodríguez MC(ed.)IGI Global。