机器人将在整个生命周期中都会经历非平稳环境动态：机器人动态可能会因磨损而改变，或者周围的环境可能会随着时间而改变。最终，机器人在遇到的所有环境变化中都应表现良好。同时，它仍然应该能够在新环境中快速学习。我们在这样的终身学习环境中确定了强化学习（RL）的两个挑战：首先，现有的现有非政策算法在保持旧环境中保持良好绩效和有效学习之间的权衡方面挣扎尽管将所有数据保留在重播缓冲区中，但新环境。我们提出了离线蒸馏管道，以通过将培训程序分离为在线互动阶段和离线蒸馏阶段来打破这一权衡。第二，我们发现，通过从一生中多个环境中的不平衡的非政策数据进行培训会产生重要性能下降。我们确定这种性能下降是由数据集中质量不平衡和大小的组合引起的，这些质量和大小加剧了Q功能的外推误差。在蒸馏阶段，我们通过使策略更接近生成数据的行为策略来应用一个简单的解决方案。在实验中，我们在各种环境变化中通过模拟的两足机器人步行任务证明了这两个挑战和拟议的解决方案。我们表明，离线蒸馏管线在所有遇到的环境中都能取得更好的性能，而不会影响数据收集。我们还提供了一项全面的实证研究，以支持我们对数据不平衡问题的假设。

我们开发了一种新的持续元学习方法，以解决连续多任务学习中的挑战。在此设置中，代理商的目标是快速通过任何任务序列实现高奖励。先前的Meta-Creenifiltive学习算法已经表现出有希望加速收购新任务的结果。但是，他们需要在培训期间访问所有任务。除了简单地将过去的经验转移到新任务，我们的目标是设计学习学习的持续加强学习算法，使用他们以前任务的经验更快地学习新任务。我们介绍了一种新的方法，连续的元策略搜索（Comps），通过以增量方式，在序列中的每个任务上，通过序列的每个任务来消除此限制，而无需重新访问先前的任务。 Comps持续重复两个子程序：使用RL学习新任务，并使用RL的经验完全离线Meta学习，为后续任务学习做好准备。我们发现，在若干挑战性连续控制任务的旧序列上，Comps优于持续的持续学习和非政策元增强方法。

离线强化学习在利用大型预采用的数据集进行政策学习方面表现出了巨大的希望，使代理商可以放弃经常廉价的在线数据收集。但是，迄今为止，离线强化学习的探索相对较小，并且缺乏对剩余挑战所在的何处的了解。在本文中，我们试图建立简单的基线以在视觉域中连续控制。我们表明，对两个基于最先进的在线增强学习算法，Dreamerv2和DRQ-V2进行了简单的修改，足以超越事先工作并建立竞争性的基准。我们在现有的离线数据集中对这些算法进行了严格的评估，以及从视觉观察结果中进行离线强化学习的新测试台，更好地代表现实世界中离线增强学习问题中存在的数据分布，并开放我们的代码和数据以促进此方面的进度重要领域。最后，我们介绍并分析了来自视觉观察的离线RL所独有的几个关键Desiderata，包括视觉分散注意力和动态视觉上可识别的变化。

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.

元强化学习（RL）方法可以使用比标准RL少的数据级的元培训策略，但元培训本身既昂贵又耗时。如果我们可以在离线数据上进行元训练，那么我们可以重复使用相同的静态数据集，该数据集将一次标记为不同任务的奖励，以在元测试时间适应各种新任务的元训练策略。尽管此功能将使Meta-RL成为现实使用的实用工具，但离线META-RL提出了除在线META-RL或标准离线RL设置之外的其他挑战。 Meta-RL学习了一种探索策略，该策略收集了用于适应的数据，并元培训策略迅速适应了新任务的数据。由于该策略是在固定的离线数据集上进行了元训练的，因此当适应学识渊博的勘探策略收集的数据时，它可能表现得不可预测，这与离线数据有系统地不同，从而导致分布变化。我们提出了一种混合脱机元元素算法，该算法使用带有奖励的脱机数据来进行自适应策略，然后收集其他无监督的在线数据，而无需任何奖励标签来桥接这一分配变化。通过不需要在线收集的奖励标签，此数据可以便宜得多。我们将我们的方法比较了在模拟机器人的运动和操纵任务上进行离线元rl的先前工作，并发现使用其他无监督的在线数据收集可以显着提高元训练政策的自适应能力，从而匹配完全在线的表现。在一系列具有挑战性的域上，需要对新任务进行概括。

Deep reinforcement learning algorithms have succeeded in several challenging domains. Classic Online RL job schedulers can learn efficient scheduling strategies but often takes thousands of timesteps to explore the environment and adapt from a randomly initialized DNN policy. Existing RL schedulers overlook the importance of learning from historical data and improving upon custom heuristic policies. Offline reinforcement learning presents the prospect of policy optimization from pre-recorded datasets without online environment interaction. Following the recent success of data-driven learning, we explore two RL methods: 1) Behaviour Cloning and 2) Offline RL, which aim to learn policies from logged data without interacting with the environment. These methods address the challenges concerning the cost of data collection and safety, particularly pertinent to real-world applications of RL. Although the data-driven RL methods generate good results, we show that the performance is highly dependent on the quality of the historical datasets. Finally, we demonstrate that by effectively incorporating prior expert demonstrations to pre-train the agent, we short-circuit the random exploration phase to learn a reasonable policy with online training. We utilize Offline RL as a \textbf{launchpad} to learn effective scheduling policies from prior experience collected using Oracle or heuristic policies. Such a framework is effective for pre-training from historical datasets and well suited to continuous improvement with online data collection.

We study the use of model-based reinforcement learning methods, in particular, world models for continual reinforcement learning. In continual reinforcement learning, an agent is required to solve one task and then another sequentially while retaining performance and preventing forgetting on past tasks. World models offer a task-agnostic solution: they do not require knowledge of task changes. World models are a straight-forward baseline for continual reinforcement learning for three main reasons. Firstly, forgetting in the world model is prevented by persisting existing experience replay buffers across tasks, experience from previous tasks is replayed for learning the world model. Secondly, they are sample efficient. Thirdly and finally, they offer a task-agnostic exploration strategy through the uncertainty in the trajectories generated by the world model. We show that world models are a simple and effective continual reinforcement learning baseline. We study their effectiveness on Minigrid and Minihack continual reinforcement learning benchmarks and show that it outperforms state of the art task-agnostic continual reinforcement learning methods.

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

Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package. The benchmarks we provide are designed to evaluate different aspects of the continual RL challenge, such as catastrophic forgetting, plasticity, ability to generalize, and sample-efficient learning. Three of the benchmarks utilize video game environments (Atari, Procgen, NetHack). The fourth benchmark, CHORES, consists of four different task sequences in a visually realistic home simulator, drawn from a diverse set of task and scene parameters. To compare continual RL methods on these benchmarks, we prepare three metrics in CORA: Continual Evaluation, Isolated Forgetting, and Zero-Shot Forward Transfer. Finally, CORA includes a set of performant, open-source baselines of existing algorithms for researchers to use and expand on. We release CORA and hope that the continual RL community can benefit from our contributions, to accelerate the development of new continual RL algorithms.

最近的工作表明，离线增强学习（RL）可以作为序列建模问题（Chen等，2021; Janner等，2021）配制，并通过类似于大规模语言建模的方法解决。但是，RL的任何实际实例化也涉及一个在线组件，在线组件中，通过与环境的任务规定相互作用对被动离线数据集进行了预测的策略。我们建议在线决策变压器（ODT），这是一种基于序列建模的RL算法，该算法将离线预处理与统一框架中的在线填充融为一体。我们的框架将序列级熵正规仪与自回归建模目标结合使用，用于样品效率探索和填充。从经验上讲，我们表明ODT在D4RL基准上的绝对性能中与最先进的表现具有竞争力，但在填充过程中显示出更大的收益。

安全探索对于使用风险敏感环境中的强化学习（RL）至关重要。最近的工作了解衡量违反限制概率的风险措施，然后可以使用安全性来实现安全性。然而，学习这种风险措施需要与环境的重大互动，从而在学习期间违反违规程度过多。此外，这些措施不易转移到新环境。我们将安全探索作为离线Meta RL问题，目的是利用一系列环境中的安全和不安全行为的例子，以快速将学习风险措施与以前看不见的动态的新环境。然后，我们向安全适应（MESA）提出元学习，这是一个荟萃学习安全RL的风险措施的方法。跨5个连续控制域的仿真实验表明，MESA可以从一系列不同的环境中利用脱机数据，以减少未经调整环境中的约束违规，同时保持任务性能。有关代码和补充材料，请参阅https://tinyurl.com/safe-meta-rl。

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

Interacting with a complex world involves continual learning, in which tasks and data distributions change over time. A continual learning system should demonstrate both plasticity (acquisition of new knowledge) and stability (preservation of old knowledge). Catastrophic forgetting is the failure of stability, in which new experience overwrites previous experience. In the brain, replay of past experience is widely believed to reduce forgetting, yet it has been largely overlooked as a solution to forgetting in deep reinforcement learning. Here, we introduce CLEAR, a replay-based method that greatly reduces catastrophic forgetting in multi-task reinforcement learning. CLEAR leverages off-policy learning and behavioral cloning from replay to enhance stability, as well as on-policy learning to preserve plasticity. We show that CLEAR performs better than state-of-the-art deep learning techniques for mitigating forgetting, despite being significantly less complicated and not requiring any knowledge of the individual tasks being learned.

离线强化学习用于在实时访问环境昂贵或不可能的情况下培训策略。作为这些恶劣条件的自然后果，在采取行动之前，代理商可能缺乏完全遵守在线环境的资源。我们配备了这种情况资源受限的设置。这导致脱机数据集（可用于培训）的情况可以包含完全处理的功能（使用功能强大的语言模型，图像模型，复杂传感器等）在实际在线时不可用。此断开连接导致离线RL中的有趣和未开发的问题：是否可以使用丰富地处理的脱机数据集来培训可访问在线环境中的更少功能的策略？在这项工作中，我们介绍并正式化这一新颖的资源受限的问题设置。我们突出了使用有限功能培训的完整脱机数据集和策略培训的策略之间的性能差距。我们通过策略传输算法解决了这种性能缺口，该策略传输算法首先使用功能完全可用的脱机数据集列举教师代理，然后将此知识传输到仅使用资源约束功能的学生代理。为了更好地捕获此设置的挑战，我们提出了一个数据收集过程：RL（RC-D4RL）的资源受限数据集。我们在RC-D4RL和流行的D4RL基准测试中评估传输算法，并观察到基线上的一致性改进（无需传输）。实验的代码在https://github.com/jayanthrr /rc-offlinerl上获得。

深度神经网络的强大学习能力使强化学习者能够直接从连续环境中学习有效的控制政策。从理论上讲，为了实现稳定的性能，神经网络假设I.I.D.不幸的是，在训练数据在时间上相关且非平稳的一般强化学习范式中，输入不存在。这个问题可能导致“灾难性干扰”和性能崩溃的现象。在本文中，我们提出智商，即干涉意识深度Q学习，以减轻单任务深度加固学习中的灾难性干扰。具体来说，我们求助于在线聚类，以实现在线上下文部门，以及一个多头网络和一个知识蒸馏正规化术语，用于保留学习上下文的政策。与现有方法相比，智商基于深Q网络，始终如一地提高稳定性和性能，并通过对经典控制和ATARI任务进行了广泛的实验。该代码可在以下网址公开获取：https：//github.com/sweety-dm/interference-aware-ware-deep-q-learning。

A long-standing challenge in artificial intelligence is lifelong learning. In lifelong learning, many tasks are presented in sequence and learners must efficiently transfer knowledge between tasks while avoiding catastrophic forgetting over long lifetimes. On these problems, policy reuse and other multi-policy reinforcement learning techniques can learn many tasks. However, they can generate many temporary or permanent policies, resulting in memory issues. Consequently, there is a need for lifetime-scalable methods that continually refine a policy library of a pre-defined size. This paper presents a first approach to lifetime-scalable policy reuse. To pre-select the number of policies, a notion of task capacity, the maximal number of tasks that a policy can accurately solve, is proposed. To evaluate lifetime policy reuse using this method, two state-of-the-art single-actor base-learners are compared: 1) a value-based reinforcement learner, Deep Q-Network (DQN) or Deep Recurrent Q-Network (DRQN); and 2) an actor-critic reinforcement learner, Proximal Policy Optimisation (PPO) with or without Long Short-Term Memory layer. By selecting the number of policies based on task capacity, D(R)QN achieves near-optimal performance with 6 policies in a 27-task MDP domain and 9 policies in an 18-task POMDP domain; with fewer policies, catastrophic forgetting and negative transfer are observed. Due to slow, monotonic improvement, PPO requires fewer policies, 1 policy for the 27-task domain and 4 policies for the 18-task domain, but it learns the tasks with lower accuracy than D(R)QN. These findings validate lifetime-scalable policy reuse and suggest using D(R)QN for larger and PPO for smaller library sizes.

Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications. This paper studies offline RL using the DQN Replay Dataset comprising the entire replay experience of a DQN agent on 60 Atari 2600 games. We demonstrate that recent off-policy deep RL algorithms, even when trained solely on this fixed dataset, outperform the fully-trained DQN agent. To enhance generalization in the offline setting, we present Random Ensemble Mixture (REM), a robust Q-learning algorithm that enforces optimal Bellman consistency on random convex combinations of multiple Q-value estimates. Offline REM trained on the DQN Replay Dataset surpasses strong RL baselines. Ablation studies highlight the role of offline dataset size and diversity as well as the algorithm choice in our positive results. Overall, the results here present an optimistic view that robust RL algorithms used on sufficiently large and diverse offline datasets can lead to high quality policies. To provide a testbed for offline RL and reproduce our results, the DQN Replay Dataset is released at offline-rl.github.io.

We introduce a conceptually simple and scalable framework for continual learning domains where tasks are learned sequentially. Our method is constant in the number of parameters and is designed to preserve performance on previously encountered tasks while accelerating learning progress on subsequent problems. This is achieved by training a network with two components: A knowledge base, capable of solving previously encountered problems, which is connected to an active column that is employed to efficiently learn the current task. After learning a new task, the active column is distilled into the knowledge base, taking care to protect any previously acquired skills. This cycle of active learning (progression) followed by consolidation (compression) requires no architecture growth, no access to or storing of previous data or tasks, and no task-specific parameters. We demonstrate the progress & compress approach on sequential classification of handwritten alphabets as well as two reinforcement learning domains: Atari games and 3D maze navigation.

应对深层终身强化学习（LRL）挑战的一种方法是仔细管理代理商的学习经验，以学习（不忘记）并建立内部元模型（任务，环境，代理商和世界）。生成重播（GR）是一种以生物学启发的重播机制，可以通过从内部生成模型中绘制的自标记示例来增强学习经验，该模型随着时间的推移而更新。在本文中，我们提出了一个满足两个Desiderata的GR版本：（a）使用深RL学习的策略的潜在策略的内省密度建模，以及（b）无模型的端到端学习。在这项工作中，我们研究了三个无模型GR的深度学习体系结构。我们在三种不同的情况下评估了我们提出的算法，其中包括来自Starcraft2和Minigrid域的任务。我们报告了几个关键发现，显示了设计选择对定量指标的影响，包括转移学习，对看不见的任务的概括，任务更改后的快速适应，与任务专家相当的绩效以及最小化灾难性遗忘。我们观察到我们的GR可以防止从深层批评剂的潜在矢量空间中的特征映射中漂移。我们还显示了既定的终身学习指标的改进。我们发现，当与重播缓冲液和生成的重播缓冲液结合使用时，需要引入一个小的随机重放缓冲液，以显着提高训练的稳定性。总体而言，我们发现“隐藏的重播”（一种众所周知的班级入学分类体系结构）是最有前途的方法，它推动了LRL的GR中最新的方法。

Lack of performance when it comes to continual learning over non-stationary distributions of data remains a major challenge in scaling neural network learning to more human realistic settings. In this work we propose a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples. We then propose a new algorithm, Meta-Experience Replay (MER), that directly exploits this view by combining experience replay with optimization based meta-learning. This method learns parameters that make interference based on future gradients less likely and transfer based on future gradients more likely. 1 We conduct experiments across continual lifelong supervised learning benchmarks and non-stationary reinforcement learning environments demonstrating that our approach consistently outperforms recently proposed baselines for continual learning. Our experiments show that the gap between the performance of MER and baseline algorithms grows both as the environment gets more non-stationary and as the fraction of the total experiences stored gets smaller.