我们考虑在以$ s $状态的地平线$ h $和$ a $ ACTIVE的偶发性，有限的，依赖于阶段的马尔可夫决策过程的环境中进行强化学习。代理商的性能是在与环境互动以$ t $插件互动后的遗憾来衡量的。我们提出了一种乐观的后验抽样算法（OPSRL），这是一种简单的后验抽样变体，仅需要许多后样品对数，$ h $，$ s $，$ a $和$ t $ a $ h $ s $ s $ a $ a $和$ t $一对。对于OPSRL，我们保证最多可容纳订单的高概率遗憾，$ \ wideTilde {\ mathcal {o}}}（\ sqrt {h^3sat}）$忽略$ \ text {poly} \ log（hsat）$项。新型的新型技术成分是线性形式的新型抗浓缩不等式，可能具有独立感兴趣。具体而言，我们将Alfers and Dinges [1984]的Beta分布的基于正常近似的下限扩展到Dirichlet分布。我们的界限匹配订单$ \ omega（\ sqrt {h^3sat}）$的下限，从而回答了Agrawal和Jia [2017b]在情节环境中提出的空旷问题。

我们介绍了DeepNash，这是一种能够学习从头开始播放不完美的信息游戏策略的自主代理，直到人类的专家级别。 Stratego是人工智能（AI）尚未掌握的少数标志性棋盘游戏之一。这个受欢迎的游戏具有$ 10^{535} $节点的巨大游戏树，即，$ 10^{175} $倍的$倍于GO。它具有在不完美的信息下需要决策的其他复杂性，类似于德克萨斯州Hold'em扑克，该扑克的游戏树较小（以$ 10^{164} $节点为单位）。 Stratego中的决策是在许多离散的动作上做出的，而动作与结果之间没有明显的联系。情节很长，在球员获胜之前经常有数百次动作，而Stratego中的情况则不能像扑克中那样轻松地分解成管理大小的子问题。由于这些原因，Stratego几十年来一直是AI领域的巨大挑战，现有的AI方法几乎没有达到业余比赛水平。 Deepnash使用游戏理论，无模型的深钢筋学习方法，而无需搜索，该方法学会通过自我播放来掌握Stratego。 DeepNash的关键组成部分的正则化NASH Dynamics（R-NAD）算法通过直接修改基础多项式学习动力学来收敛到近似NASH平衡，而不是围绕它“循环”。 Deepnash在Stratego中击败了现有的最先进的AI方法，并在Gravon Games平台上获得了年度（2022年）和历史前3名，并与人类专家竞争。

In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters. A key challenge is to handle the increased amount of data and extended training time. We have developed a new distributed agent IMPALA (Importance Weighted Actor-Learner Architecture) that not only uses resources more efficiently in singlemachine training but also scales to thousands of machines without sacrificing data efficiency or resource utilisation. We achieve stable learning at high throughput by combining decoupled acting and learning with a novel off-policy correction method called V-trace. We demonstrate the effectiveness of IMPALA for multi-task reinforcement learning on DMLab-30 (a set of 30 tasks from the DeepMind Lab environment (Beattie et al., 2016)) and Atari-57 (all available Atari games in Arcade Learning Environment (Bellemare et al., 2013a)). Our results show that IMPALA is able to achieve better performance than previous agents with less data, and crucially exhibits positive transfer between tasks as a result of its multi-task approach. The source code is publicly available at github.com/deepmind/scalable agent.

We consider an agent's uncertainty about its environment and the problem of generalizing this uncertainty across states. Specifically, we focus on the problem of exploration in non-tabular reinforcement learning. Drawing inspiration from the intrinsic motivation literature, we use density models to measure uncertainty, and propose a novel algorithm for deriving a pseudo-count from an arbitrary density model. This technique enables us to generalize count-based exploration algorithms to the non-tabular case. We apply our ideas to Atari 2600 games, providing sensible pseudo-counts from raw pixels. We transform these pseudo-counts into exploration bonuses and obtain significantly improved exploration in a number of hard games, including the infamously difficult MONTEZUMA'S REVENGE.

We introduce a machine-learning (ML)-based weather simulator--called "GraphCast"--which outperforms the most accurate deterministic operational medium-range weather forecasting system in the world, as well as all previous ML baselines. GraphCast is an autoregressive model, based on graph neural networks and a novel high-resolution multi-scale mesh representation, which we trained on historical weather data from the European Centre for Medium-Range Weather Forecasts (ECMWF)'s ERA5 reanalysis archive. It can make 10-day forecasts, at 6-hour time intervals, of five surface variables and six atmospheric variables, each at 37 vertical pressure levels, on a 0.25-degree latitude-longitude grid, which corresponds to roughly 25 x 25 kilometer resolution at the equator. Our results show GraphCast is more accurate than ECMWF's deterministic operational forecasting system, HRES, on 90.0% of the 2760 variable and lead time combinations we evaluated. GraphCast also outperforms the most accurate previous ML-based weather forecasting model on 99.2% of the 252 targets it reported. GraphCast can generate a 10-day forecast (35 gigabytes of data) in under 60 seconds on Cloud TPU v4 hardware. Unlike traditional forecasting methods, ML-based forecasting scales well with data: by training on bigger, higher quality, and more recent data, the skill of the forecasts can improve. Together these results represent a key step forward in complementing and improving weather modeling with ML, open new opportunities for fast, accurate forecasting, and help realize the promise of ML-based simulation in the physical sciences.

Imperfect information games (IIG) are games in which each player only partially observes the current game state. We study how to learn $\epsilon$-optimal strategies in a zero-sum IIG through self-play with trajectory feedback. We give a problem-independent lower bound $\mathcal{O}(H(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ on the required number of realizations to learn these strategies with high probability, where $H$ is the length of the game, $A_{\mathcal{X}}$ and $B_{\mathcal{Y}}$ are the total number of actions for the two players. We also propose two Follow the Regularize leader (FTRL) algorithms for this setting: Balanced-FTRL which matches this lower bound, but requires the knowledge of the information set structure beforehand to define the regularization; and Adaptive-FTRL which needs $\mathcal{O}(H^2(A_{\mathcal{X}}+B_{\mathcal{Y}})/\epsilon^2)$ plays without this requirement by progressively adapting the regularization to the observations.

We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their own future latent representations. Despite its recent empirical success, such algorithms have an apparent defect: trivial representations (such as constants) minimize the prediction error, yet it is obviously undesirable to converge to such solutions. Our central insight is that careful designs of the optimization dynamics are critical to learning meaningful representations. We identify that a faster paced optimization of the predictor and semi-gradient updates on the representation, are crucial to preventing the representation collapse. Then in an idealized setup, we show self-predictive learning dynamics carries out spectral decomposition on the state transition matrix, effectively capturing information of the transition dynamics. Building on the theoretical insights, we propose bidirectional self-predictive learning, a novel self-predictive algorithm that learns two representations simultaneously. We examine the robustness of our theoretical insights with a number of small-scale experiments and showcase the promise of the novel representation learning algorithm with large-scale experiments.

神经网络越来越依赖于复杂安全系统（例如自动驾驶汽车）的组成部分。对在更大的验证周期中嵌入神经网络验证的工具和方法的需求很高。但是，由于关注的广泛验证属性，很难进行神经网络验证，通常每个验证属性仅适用于专用求解器中的验证。在本文中，我们展示了最初设计用于验证，验证和仿真金融基础架构的功能编程语言的Imandra如何为神经网络验证提供整体基础架构。我们开发了一个新颖的图书馆Checkinn，该图书馆在Imandra的神经网络上形式化，并涵盖了神经网络验证的不同重要方面。

当同时部署大量传感器和执行器的多个服务时，设计智能家庭服务是一项复杂的任务。它可能依赖于基于知识或数据驱动的方法。前者可以使用基于规则的方法静态设计服务，后者可以使用学习方法动态地发现居民的偏好。但是，这些方法都不完全令人满意，因为规则不能涵盖所有可能改变的可能情况，而学习方法可能会做出有时对居民无法理解的决定。在本文中，提出了PBRE（基于教学的规则提取器），以从学习方法中提取规则，以实现智能家庭系统的动态规则生成。预期的优势是采用了基于规则的方法的解释性和学习方法的动态性。我们将PBRE与现有规则提取方法进行比较，结果显示PBRE的性能更好。我们还应用PBRE从NRL（基于神经网络的强化学习）代表的智能家庭服务中提取规则。结果表明，PBRE可以帮助NRL模拟的服务向居民提出可理解的建议。

我们研究了分销RL的多步非政策学习方法。尽管基于价值的RL和分布RL之间的相似性明显相似，但我们的研究揭示了多步环境中两种情况之间的有趣和根本差异。我们确定了依赖路径依赖性分布TD误差的新颖概念，这对于原则上的多步分布RL是必不可少的。基于价值的情况的区别对诸如后视算法等概念的重要含义具有重要意义。我们的工作提供了多步非政策分布RL算法的第一个理论保证，包括适用于多步分配RL现有方法的结果。此外，我们得出了一种新颖的算法，即分位数回归 - 逆转录，该算法导致了深度RL QR QR-DQN-RETRACE，显示出对Atari-57基准上QR-DQN的经验改进。总的来说，我们阐明了多步分布RL中如何在理论和实践中解决多个独特的挑战。