马尔可夫决策过程(MDP)的动态模型的离线估计是一个非琐碎的任务,这大大取决于学习阶段可用的数据。有时,模型的动态相对于当前状态和动作的一些转换是不变的。最近的作品表明,依赖于密度估计方法的专家引导的管道,因为基于深度神经网络的标准化流量有效地检测了确定性环境中的这种结构,包括分类和连续值。可以利用所获取的知识来增加原始数据集,最终导致True和学习模型之间的分布偏移的减少。在这项工作中,我们将范例扩展到解决非确定性MDP,特别是1)我们提出基于统计距离的分类环境中的检测阈值,2)我们在基于Wilcoxon签名的连续环境中引入分布换档的基准。等级统计测试和3)我们表明,在解决学习MDP时,前者的结果会导致性能改进,然后在真实环境中应用最佳政策。
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从固定批次的轨迹学习马尔可夫决策过程(MDP)是一个非琐碎的任务,其结果的质量取决于状态行动空间的采样区域的数量和多样性。然而,许多MDP是赋予了不变的奖励和转换函数,相对于当前状态和动作的一些转换。能够检测和利用这些结构不仅可以使用MDP的学习,而且可以利用其随后的最佳控制策略的计算。在这项工作中,我们提出了一种基于密度估计方法的范例,该方法旨在检测MDP动态是不变的状态动作空间的一些已经假设的状态的存在。我们在离散的环形电网环境中和Openai的健身房学习套件的两个臭名昭着的环境中测试了所提出的方法。结果表明,当数据集用通过使用检测到的对称获得的数据增强数据集时,允许更彻底和数据高效地学习转换功能时,模型分配移位减少。
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在这项工作中,我们继续建立最近有限马尔可夫进程的钢筋学习的进步。以前现有的算法中的一种共同方法,包括单个演员和分布式,都是剪辑奖励,也可以在Q函数上应用转换方法,以处理真正的折扣回报中的各种大小。理论上我们展示了如果我们有非确定性过程,最成功的方法可能不会产生最佳政策。作为一种解决方案,我们认为分布加强学习借给自己完全解决这种情况。通过引入共轭分布运营商,我们可以处理大量转换,以获得有保证的理论融合。我们提出了一种基于该操作员的近似单录像机算法,该操作员使用Cram \'ER距离给出的适当分布度量直接在不妨碍的奖励上培养代理。在使用粘性动作的35个Atari 2600游戏套件中培训代理的随机环境中的表现,与多巴胺框架中的其他众所周知的算法相比,获得最先进的绩效。
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我们介绍了一种改进政策改进的方法,该方法在基于价值的强化学习(RL)的贪婪方法与基于模型的RL的典型计划方法之间进行了插值。新方法建立在几何视野模型(GHM,也称为伽马模型)的概念上,该模型对给定策略的折现状态验证分布进行了建模。我们表明,我们可以通过仔细的基本策略GHM的仔细组成,而无需任何其他学习,可以评估任何非马尔科夫策略,以固定的概率在一组基本马尔可夫策略之间切换。然后,我们可以将广义政策改进(GPI)应用于此类非马尔科夫政策的收集,以获得新的马尔可夫政策,通常将其表现优于其先驱。我们对这种方法提供了彻底的理论分析,开发了转移和标准RL的应用,并在经验上证明了其对标准GPI的有效性,对充满挑战的深度RL连续控制任务。我们还提供了GHM培训方法的分析,证明了关于先前提出的方法的新型收敛结果,并显示了如何在深度RL设置中稳定训练这些模型。
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在现实世界中,通过弱势政策影响环境可能是昂贵的或非常危险的,因此妨碍了现实世界的加强学习应用。离线强化学习(RL)可以从给定数据集中学习策略,而不与环境进行交互。但是,数据集是脱机RL算法的唯一信息源,并确定学习策略的性能。我们仍然缺乏关于数据集特征如何影响不同离线RL算法的研究。因此,我们对数据集特性如何实现离散动作环境的离线RL算法的性能的全面实证分析。数据集的特点是两个度量:(1)通过轨迹质量(TQ)测量的平均数据集返回和(2)由状态 - 动作覆盖(SACO)测量的覆盖范围。我们发现,禁止政策深度Q网家族的变体需要具有高SACO的数据集来表现良好。将学习策略朝向给定数据集的算法对具有高TQ或SACO的数据集进行了良好。对于具有高TQ的数据集,行为克隆优先级或类似于最好的离线RL算法。
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离线强化学习利用大型数据集来训练政策而无需与环境进行互动。然后,可以在互动昂贵或危险的现实世界中部署学习的策略。当前算法过于拟合到训练数据集,并且在部署到环境外的分发概括时,因此表现不佳。我们的目标是通过学习Koopman潜在代表来解决这些限制,这使我们能够推断系统的潜在动态的对称性。然后利用后者在训练期间扩展其他静态离线数据集;这构成了一种新颖的数据增强框架,其反映了系统的动态,因此要被解释为对环境空间的探索。为了获得对称,我们采用Koopman理论,其中根据用于系统的测量功能空间的线性操作员表示非线性动力学,因此可以直接推断动力学的对称性。我们为对对称性的对称性的存在和性质提供了新的理论结果,这些控制系统如加强学习设置。此外,我们对我们的方法进行了多种基准脱机强化学习任务和数据集,包括D4RL,MetaWorld和RoboSuite,并通过使用我们的框架来始终如一地改善Q学习方法的最先进。
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具有很多玩家的非合作和合作游戏具有许多应用程序,但是当玩家数量增加时,通常仍然很棘手。由Lasry和Lions以及Huang,Caines和Malham \'E引入的,平均野外运动会(MFGS)依靠平均场外近似值,以使玩家数量可以成长为无穷大。解决这些游戏的传统方法通常依赖于以完全了解模型的了解来求解部分或随机微分方程。最近,增强学习(RL)似乎有望解决复杂问题。通过组合MFGS和RL,我们希望在人口规模和环境复杂性方面能够大规模解决游戏。在这项调查中,我们回顾了有关学习MFG中NASH均衡的最新文献。我们首先确定最常见的设置(静态,固定和进化)。然后,我们为经典迭代方法(基于最佳响应计算或策略评估)提供了一个通用框架,以确切的方式解决MFG。在这些算法和与马尔可夫决策过程的联系的基础上,我们解释了如何使用RL以无模型的方式学习MFG解决方案。最后,我们在基准问题上介绍了数值插图,并以某些视角得出结论。
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我们在马尔可夫决策过程的状态空间上提出了一种新的行为距离,并展示使用该距离作为塑造深度加强学习代理的学习言论的有效手段。虽然由于高计算成本和基于样本的算法缺乏缺乏样本的距离,但是,虽然现有的国家相似性通常难以在规模上学习,但我们的新距离解决了这两个问题。除了提供详细的理论分析外,我们还提供了学习该距离的经验证据,与价值函数产生的结构化和信息化表示,包括对街机学习环境基准的强劲结果。
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在训练数据的分布中评估时,学到的模型和政策可以有效地概括,但可以在分布输入输入的情况下产生不可预测且错误的输出。为了避免在部署基于学习的控制算法时分配变化,我们寻求一种机制将代理商限制为类似于受过训练的国家和行动的机制。在控制理论中,Lyapunov稳定性和控制不变的集合使我们能够保证稳定系统周围系统的控制器,而在机器学习中,密度模型使我们能够估算培训数据分布。我们可以将这两个概念结合起来,产生基于学习的控制算法,这些算法仅使用分配动作将系统限制为分布状态?在这项工作中,我们建议通过结合Lyapunov稳定性和密度估计的概念来做到这一点,引入Lyapunov密度模型:控制Lyapunov函数和密度模型的概括,这些函数和密度模型可以保证代理商在其整个轨迹上保持分布的能力。
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Offline reinforcement learning (RL) is suitable for safety-critical domains where online exploration is too costly or dangerous. In safety-critical settings, decision-making should take into consideration the risk of catastrophic outcomes. In other words, decision-making should be risk-sensitive. Previous works on risk in offline RL combine together offline RL techniques, to avoid distributional shift, with risk-sensitive RL algorithms, to achieve risk-sensitivity. In this work, we propose risk-sensitivity as a mechanism to jointly address both of these issues. Our model-based approach is risk-averse to both epistemic and aleatoric uncertainty. Risk-aversion to epistemic uncertainty prevents distributional shift, as areas not covered by the dataset have high epistemic uncertainty. Risk-aversion to aleatoric uncertainty discourages actions that may result in poor outcomes due to environment stochasticity. Our experiments show that our algorithm achieves competitive performance on deterministic benchmarks, and outperforms existing approaches for risk-sensitive objectives in stochastic domains.
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在动态编程(DP)和强化学习(RL)中,代理商学会在通过由Markov决策过程(MDP)建模的环境中顺序交互来实现预期的长期返回。更一般地在分布加强学习(DRL)中,重点是返回的整体分布,而不仅仅是其期望。虽然基于DRL的方法在RL中产生了最先进的性能,但它们涉及尚未充分理解的额外数量(与非分布设置相比)。作为第一个贡献,我们介绍了一类新的分类运营商,以及一个实用的DP算法,用于策略评估,具有强大的MDP解释。实际上,我们的方法通过增强的状态空间重新重新重新重新重新重新格式化,其中每个状态被分成最坏情况的子变量,并且最佳的子变电站,其值分别通过安全和危险的策略最大化。最后,我们派生了分配运营商和DP算法解决了一个新的控制任务:如何区分安全性的最佳动作,以便在最佳政策空间中打破联系?
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Model-Based Reinforcement Learning (RL) is widely believed to have the potential to improve sample efficiency by allowing an agent to synthesize large amounts of imagined experience. Experience Replay (ER) can be considered a simple kind of model, which has proved extremely effective at improving the stability and efficiency of deep RL. In principle, a learned parametric model could improve on ER by generalizing from real experience to augment the dataset with additional plausible experience. However, owing to the many design choices involved in empirically successful algorithms, it can be very hard to establish where the benefits are actually coming from. Here, we provide theoretical and empirical insight into when, and how, we can expect data generated by a learned model to be useful. First, we provide a general theorem motivating how learning a model as an intermediate step can narrow down the set of possible value functions more than learning a value function directly from data using the Bellman equation. Second, we provide an illustrative example showing empirically how a similar effect occurs in a more concrete setting with neural network function approximation. Finally, we provide extensive experiments showing the benefit of model-based learning for online RL in environments with combinatorial complexity, but factored structure that allows a learned model to generalize. In these experiments, we take care to control for other factors in order to isolate, insofar as possible, the benefit of using experience generated by a learned model relative to ER alone.
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Behavioural cloning (BC) is a commonly used imitation learning method to infer a sequential decision-making policy from expert demonstrations. However, when the quality of the data is not optimal, the resulting behavioural policy also performs sub-optimally once deployed. Recently, there has been a surge in offline reinforcement learning methods that hold the promise to extract high-quality policies from sub-optimal historical data. A common approach is to perform regularisation during training, encouraging updates during policy evaluation and/or policy improvement to stay close to the underlying data. In this work, we investigate whether an offline approach to improving the quality of the existing data can lead to improved behavioural policies without any changes in the BC algorithm. The proposed data improvement approach - Trajectory Stitching (TS) - generates new trajectories (sequences of states and actions) by `stitching' pairs of states that were disconnected in the original data and generating their connecting new action. By construction, these new transitions are guaranteed to be highly plausible according to probabilistic models of the environment, and to improve a state-value function. We demonstrate that the iterative process of replacing old trajectories with new ones incrementally improves the underlying behavioural policy. Extensive experimental results show that significant performance gains can be achieved using TS over BC policies extracted from the original data. Furthermore, using the D4RL benchmarking suite, we demonstrate that state-of-the-art results are obtained by combining TS with two existing offline learning methodologies reliant on BC, model-based offline planning (MBOP) and policy constraint (TD3+BC).
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安全政策改进(SPI)是在安全关键应用中脱机加强学习的重要技术,因为它以很高的可能性改善了行为政策。我们根据如何利用国家行动对的不确定性将各种SPI方法分为两组。为了关注软SPIBB(通过软基线自举的安全政策改进)算法,我们表明他们对被证明安全的主张不坚持。基于这一发现,我们开发了适应性,Adv-Soft SpibB算法,并证明它们是可以安全的。在两个基准上进行的广泛实验中,启发式适应性较低的SPOBB在所有SPIBB算法中都能表现出最佳性能。我们还检查了可证明的安全算法的安全保证,并表明有大量数据是必要的,以使安全界限在实践中变得有用。
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脱机强化学习 - 从一批数据中学习策略 - 是难以努力的:如果没有制造强烈的假设,它很容易构建实体算法失败的校长。在这项工作中,我们考虑了某些现实世界问题的财产,其中离线强化学习应该有效:行动仅对一部分产生有限的行动。我们正规化并介绍此动作影响规律(AIR)财产。我们进一步提出了一种算法,该算法假定和利用AIR属性,并在MDP满足空气时绑定输出策略的子优相。最后,我们展示了我们的算法在定期保留的两个模拟环境中跨越不同的数据收集策略占据了现有的离线强度学习算法。
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Advances in reinforcement learning have led to its successful application in complex tasks with continuous state and action spaces. Despite these advances in practice, most theoretical work pertains to finite state and action spaces. We propose building a theoretical understanding of continuous state and action spaces by employing a geometric lens. Central to our work is the idea that the transition dynamics induce a low dimensional manifold of reachable states embedded in the high-dimensional nominal state space. We prove that, under certain conditions, the dimensionality of this manifold is at most the dimensionality of the action space plus one. This is the first result of its kind, linking the geometry of the state space to the dimensionality of the action space. We empirically corroborate this upper bound for four MuJoCo environments. We further demonstrate the applicability of our result by learning a policy in this low dimensional representation. To do so we introduce an algorithm that learns a mapping to a low dimensional representation, as a narrow hidden layer of a deep neural network, in tandem with the policy using DDPG. Our experiments show that a policy learnt this way perform on par or better for four MuJoCo control suite tasks.
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几乎可以肯定(或使用概率)满足安全限制对于在现实生活中的增强学习(RL)的部署至关重要。例如,理想情况下,平面降落和起飞应以概率为单位发生。我们通过引入安全增强(SAUTE)马尔可夫决策过程(MDP)来解决该问题,在该过程中,通过将其扩大到州空间并重塑目标来消除安全限制。我们表明,Saute MDP满足了Bellman方程,并使我们更加接近解决安全的RL,几乎可以肯定地满足。我们认为,Saute MDP允许从不同的角度查看安全的RL问题,从而实现新功能。例如,我们的方法具有插件的性质,即任何RL算法都可以“炒”。此外,国家扩展允许跨安全限制进行政策概括。我们最终表明,当约束满意度非常重要时,SAUTE RL算法的表现可以胜过其最先进的对应物。
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交通信号控制是城市移动性的重要问题,具有经济和环境影响的显着潜力。虽然对交通管制的加固学习(RL)越来越兴趣,但到目前为止的工作侧重于学习的互动,在实践中是昂贵的。相反,有关流量的真实体验数据可用,可以以最少的成本利用。最近在离线或批处理RL中的进展已启用。基于模型的离线RL方法,特别是已经显示到比其他体验数据更好。我们构建基于模型的学习框架A-DAC,它在数据集中浏览了Markov决策过程(MDP),其中包括悲观成本,以处理数据不确定性。通过MDP中的奖励的自适应整形来建模成本,其与先前的相关工作相比提供了更好的数据正则化。使用大小和批量收集策略的多个数据集在复杂的信号化环形交叉路口上评估A-DAC。评估结果表明,可以使用简单的批量收集策略以数据有效的方式构建高性能控制策略。
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Safety is still one of the major research challenges in reinforcement learning (RL). In this paper, we address the problem of how to avoid safety violations of RL agents during exploration in probabilistic and partially unknown environments. Our approach combines automata learning for Markov Decision Processes (MDPs) and shield synthesis in an iterative approach. Initially, the MDP representing the environment is unknown. The agent starts exploring the environment and collects traces. From the collected traces, we passively learn MDPs that abstractly represent the safety-relevant aspects of the environment. Given a learned MDP and a safety specification, we construct a shield. For each state-action pair within a learned MDP, the shield computes exact probabilities on how likely it is that executing the action results in violating the specification from the current state within the next $k$ steps. After the shield is constructed, the shield is used during runtime and blocks any actions that induce a too large risk from the agent. The shielded agent continues to explore the environment and collects new data on the environment. Iteratively, we use the collected data to learn new MDPs with higher accuracy, resulting in turn in shields able to prevent more safety violations. We implemented our approach and present a detailed case study of a Q-learning agent exploring slippery Gridworlds. In our experiments, we show that as the agent explores more and more of the environment during training, the improved learned models lead to shields that are able to prevent many safety violations.
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Classical reinforcement learning (RL) techniques are generally concerned with the design of decision-making policies driven by the maximisation of the expected outcome. Nevertheless, this approach does not take into consideration the potential risk associated with the actions taken, which may be critical in certain applications. To address that issue, the present research work introduces a novel methodology based on distributional RL to derive sequential decision-making policies that are sensitive to the risk, the latter being modelled by the tail of the return probability distribution. The core idea is to replace the $Q$ function generally standing at the core of learning schemes in RL by another function taking into account both the expected return and the risk. Named the risk-based utility function $U$, it can be extracted from the random return distribution $Z$ naturally learnt by any distributional RL algorithm. This enables to span the complete potential trade-off between risk minimisation and expected return maximisation, in contrast to fully risk-averse methodologies. Fundamentally, this research yields a truly practical and accessible solution for learning risk-sensitive policies with minimal modification to the distributional RL algorithm, and with an emphasis on the interpretability of the resulting decision-making process.
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