优化能源需求响应的价格需要一个灵活的控制器，具有导航复杂环境的能力。我们提出了一种强化学习控制器，令人惊讶的是最小化其架构的修改。我们建议令人惊讶的最小化可用于提高学习速度，以利用人们在人民能源使用中的可预测性。我们的架构在模拟能源需求响应时表现良好。我们提出这种修改，以改善功能，并在大规模的实验中保存。

我们考虑了需求侧能源管理的问题，每个家庭都配备了能够在线安排家用电器的智能电表。目的是最大程度地减少实时定价计划下的整体成本。尽管以前的作品引入了集中式方法，在该方法中，调度算法具有完全可观察的性能，但我们提出了将智能网格环境作为马尔可夫游戏的表述。每个家庭都是具有部分可观察性的去中心化代理，可以在现实环境中进行可扩展性和隐私保护。电网操作员产生的价格信号随能量需求而变化。我们提出了从代理商的角度来解决部分可观察性和环境的局部可观察性的扩展，以解决部分可观察性。该算法学习了一位集中批评者，该批评者协调分散的代理商的培训。因此，我们的方法使用集中学习，但分散执行。仿真结果表明，我们的在线深入强化学习方法可以纯粹基于瞬时观察和价格信号来降低所有消耗的总能量的峰值与平均值和所有家庭的电力。

已经开发了增强学习（RL）技术来优化工业冷却系统，与传统的启发式政策相比，提供了可观的节能。工业控制中的一个主要挑战涉及由于机械限制而在现实世界中可行的学习行为。例如，某些操作只能每隔几个小时执行一次，而其他动作可以更频繁地采取。如果没有广泛的奖励工程和实验，RL代理可能无法学习机械的现实操作。为了解决这个问题，我们使用层次结构的增强学习与多种根据操作时间尺度控制动作子集的代理。我们的分层方法可以在现有基线上节省能源，同时在模拟的HVAC控制环境中保持在安全范围内的限制（例如操作冷却器）。

在本文中，多种子体增强学习用于控制混合能量存储系统，通过最大化可再生能源和交易的价值来降低微电网的能量成本。该代理商必须学习在波动需求，动态批发能源价格和不可预测的可再生能源中，控制三种不同类型的能量存储系统。考虑了两种案例研究：首先看能量存储系统如何在动态定价下更好地整合可再生能源发电，第二种与这些同一代理商如何与聚合剂一起使用，以向自私外部微电网销售能量的能量减少自己的能源票据。这项工作发现，具有分散执行的多代理深度确定性政策梯度的集中学习及其最先进的变体允许多种代理方法显着地比来自单个全局代理的控制更好。还发现，在多种子体方法中使用单独的奖励功能比使用单个控制剂更好。还发现能够与其他微电网交易，而不是卖回实用电网，也发现大大增加了网格的储蓄。

As an efficient way to integrate multiple distributed energy resources and the user side, a microgrid is mainly faced with the problems of small-scale volatility, uncertainty, intermittency and demand-side uncertainty of DERs. The traditional microgrid has a single form and cannot meet the flexible energy dispatch between the complex demand side and the microgrid. In response to this problem, the overall environment of wind power, thermostatically controlled loads, energy storage systems, price-responsive loads and the main grid is proposed. Secondly, the centralized control of the microgrid operation is convenient for the control of the reactive power and voltage of the distributed power supply and the adjustment of the grid frequency. However, there is a problem in that the flexible loads aggregate and generate peaks during the electricity price valley. The existing research takes into account the power constraints of the microgrid and fails to ensure a sufficient supply of electric energy for a single flexible load. This paper considers the response priority of each unit component of TCLs and ESSs on the basis of the overall environment operation of the microgrid so as to ensure the power supply of the flexible load of the microgrid and save the power input cost to the greatest extent. Finally, the simulation optimization of the environment can be expressed as a Markov decision process process. It combines two stages of offline and online operations in the training process. The addition of multiple threads with the lack of historical data learning leads to low learning efficiency. The asynchronous advantage actor-critic with the experience replay pool memory library is added to solve the data correlation and nonstatic distribution problems during training.

The policy gradient method enjoys the simplicity of the objective where the agent optimizes the cumulative reward directly. Moreover, in the continuous action domain, parameterized distribution of action distribution allows easy control of exploration, resulting from the variance of the representing distribution. Entropy can play an essential role in policy optimization by selecting the stochastic policy, which eventually helps better explore the environment in reinforcement learning (RL). However, the stochasticity often reduces as the training progresses; thus, the policy becomes less exploratory. Additionally, certain parametric distributions might only work for some environments and require extensive hyperparameter tuning. This paper aims to mitigate these issues. In particular, we propose an algorithm called Robust Policy Optimization (RPO), which leverages a perturbed distribution. We hypothesize that our method encourages high-entropy actions and provides a way to represent the action space better. We further provide empirical evidence to verify our hypothesis. We evaluated our methods on various continuous control tasks from DeepMind Control, OpenAI Gym, Pybullet, and IsaacGym. We observed that in many settings, RPO increases the policy entropy early in training and then maintains a certain level of entropy throughout the training period. Eventually, our agent RPO shows consistently improved performance compared to PPO and other techniques: entropy regularization, different distributions, and data augmentation. Furthermore, in several settings, our method stays robust in performance, while other baseline mechanisms fail to improve and even worsen the performance.

我们提供了PelficGridWorld软件包，为用户提供轻量级，模块化和可定制的框架，用于创建专注的电源系统的多代理体育馆环境，该环境易于与强化学习（RL）的现有培训框架集成。虽然存在许多框架用于训练多代理RL（MARL）政策，但没有可以快速原型并发开发环境，尤其是在所需电流解决方案来定义网格的异构（复合式，多器件）电力系统的背景下 - 级别变量和成本。 PowerGridWorld是一个开源软件包，有助于填补此间隙。为了突出PowerGridWorld的关键功能，我们展示了两个案例研究，并使用Openai的多代理深度确定性政策梯度（MADDPG）和RLLIB的近端策略优化（PPO）算法来演示MARL政策。在这两种情况下，至少一些代理子集合在每次作为奖励（负成本）结构的一部分中的一部分中的功率流溶液的元件。

The decarbonization of buildings presents new challenges for the reliability of the electrical grid as a result of the intermittency of renewable energy sources and increase in grid load brought about by end-use electrification. To restore reliability, grid-interactive efficient buildings can provide flexibility services to the grid through demand response. Residential demand response programs are hindered by the need for manual intervention by customers. To maximize the energy flexibility potential of residential buildings, an advanced control architecture is needed. Reinforcement learning is well-suited for the control of flexible resources as it is able to adapt to unique building characteristics compared to expert systems. Yet, factors hindering the adoption of RL in real-world applications include its large data requirements for training, control security and generalizability. Here we address these challenges by proposing the MERLIN framework and using a digital twin of a real-world 17-building grid-interactive residential community in CityLearn. We show that 1) independent RL-controllers for batteries improve building and district level KPIs compared to a reference RBC by tailoring their policies to individual buildings, 2) despite unique occupant behaviours, transferring the RL policy of any one of the buildings to other buildings provides comparable performance while reducing the cost of training, 3) training RL-controllers on limited temporal data that does not capture full seasonality in occupant behaviour has little effect on performance. Although, the zero-net-energy (ZNE) condition of the buildings could be maintained or worsened as a result of controlled batteries, KPIs that are typically improved by ZNE condition (electricity price and carbon emissions) are further improved when the batteries are managed by an advanced controller.

建筑物中的加热和冷却系统占全球能源使用的31 \％，其中大部分受基于规则的控制器（RBC）调节，这些控制器（RBC）既不通过与电网进行最佳交互来最大化能源效率或最小化排放。通过强化学习（RL）的控制已显示可显着提高建筑能源效率，但是现有的解决方案需要访问世界上每栋建筑物都无法期望的特定建筑模拟器或数据。作为回应，我们表明可以在没有这样的知识的情况下获得减少排放的政策，这是我们称为零射击建筑物控制的范式。我们结合了系统识别和基于模型的RL的想法，以创建PEARL（概率避免发射的增强学习），并表明建立表现模型所需的短期积极探索是所需的。在三个不同的建筑能源模拟的实验中，我们显示珍珠在所有情况下都优于现有的RBC，并且在所有情况下，流行的RL基线，在维持热舒适度的同时，将建筑物排放量减少了31 \％。我们的源代码可通过https://enjeener.io/projects/pearl在线获得。

建筑物中的供暖和冷却系统占全球能源使用的31％，其中大部分受基于规则的控制器（RBC）调节，这些控制器（RBC）既不通过与网格最佳交互来最大程度地提高能源效率或最小化排放。通过增强学习（RL）的控制已显示可显着提高建筑能源效率，但是现有的解决方案需要在模拟器中进行预训练，这些模拟器对世界上每栋建筑物的获得非常昂贵。作为回应，我们表明可以通过结合系统识别和基于模型的RL的想法来对建筑物进行安全，零射击的控制。我们称这种组合珍珠（概率避免施加加固的增强学习），并表明它可以减少排放而无需预先培训，只需要三个小时的调试期。在三个不同的建筑能源模拟的实验中，我们显示珍珠在所有情况下都胜过现有的RBC，并且在所有情况下，流行的RL基线，在维持热舒适度的同时，将建筑物排放量降低了31％。

实际经济体可以被视为一种顺序不完美信息游戏，具有许多异质，互动的各种代理类型的战略代理，例如消费者，公司和政府。动态一般均衡模型是在此类系统中建模经济活动，交互和结果的普通经济工具。然而，当所有代理商是战略和互动时，现有的分析和计算方法努力寻找明确的均衡，而联合学习是不稳定的并且具有挑战性。在其他人中，一个重要的原因是，一个经济代理人的行动可能会改变另一名代理人的奖励职能，例如，当公司更改价格或政府更改税收时，消费者的消费者的消费收入变化。我们表明，多代理深度加强学习（RL）可以发现稳定的解决方案，即通过使用结构的学习课程和高效的GPU，在经济模拟中，在经济仿真中，在经济模拟中，可以发现普遍存器类型的稳定解决方案。仿真和培训。概念上，我们的方法更加灵活，不需要不切实际的假设，例如市场清算，通常用于分析途径。我们的GPU实施使得能够在合理的时间范围内具有大量代理的经济体，例如，在一天内完成培训。我们展示了我们在实际商业周期模型中的方法，这是一个代表性的DGE模型系列，100名工人消费者，10家公司和政府税收和重新分配。我们通过近似最佳响应分析验证了学习的Meta-Game epsilon-Nash均衡，表明RL政策与经济直觉保持一致，我们的方法是建设性的，例如，通过明确地学习Meta-Game epsilon-Nash ePhilia的频谱打开RBC型号。

Driven by the global decarbonization effort, the rapid integration of renewable energy into the conventional electricity grid presents new challenges and opportunities for the battery energy storage system (BESS) participating in the energy market. Energy arbitrage can be a significant source of revenue for the BESS due to the increasing price volatility in the spot market caused by the mismatch between renewable generation and electricity demand. In addition, the Frequency Control Ancillary Services (FCAS) markets established to stabilize the grid can offer higher returns for the BESS due to their capability to respond within milliseconds. Therefore, it is crucial for the BESS to carefully decide how much capacity to assign to each market to maximize the total profit under uncertain market conditions. This paper formulates the bidding problem of the BESS as a Markov Decision Process, which enables the BESS to participate in both the spot market and the FCAS market to maximize profit. Then, Proximal Policy Optimization, a model-free deep reinforcement learning algorithm, is employed to learn the optimal bidding strategy from the dynamic environment of the energy market under a continuous bidding scale. The proposed model is trained and validated using real-world historical data of the Australian National Electricity Market. The results demonstrate that our developed joint bidding strategy in both markets is significantly profitable compared to individual markets.

We present temporally layered architecture (TLA), a biologically inspired system for temporally adaptive distributed control. TLA layers a fast and a slow controller together to achieve temporal abstraction that allows each layer to focus on a different time-scale. Our design is biologically inspired and draws on the architecture of the human brain which executes actions at different timescales depending on the environment's demands. Such distributed control design is widespread across biological systems because it increases survivability and accuracy in certain and uncertain environments. We demonstrate that TLA can provide many advantages over existing approaches, including persistent exploration, adaptive control, explainable temporal behavior, compute efficiency and distributed control. We present two different algorithms for training TLA: (a) Closed-loop control, where the fast controller is trained over a pre-trained slow controller, allowing better exploration for the fast controller and closed-loop control where the fast controller decides whether to "act-or-not" at each timestep; and (b) Partially open loop control, where the slow controller is trained over a pre-trained fast controller, allowing for open loop-control where the slow controller picks a temporally extended action or defers the next n-actions to the fast controller. We evaluated our method on a suite of continuous control tasks and demonstrate the advantages of TLA over several strong baselines.

With the growing need to reduce energy consumption and greenhouse gas emissions, Eco-driving strategies provide a significant opportunity for additional fuel savings on top of other technological solutions being pursued in the transportation sector. In this paper, a model-free deep reinforcement learning (RL) control agent is proposed for active Eco-driving assistance that trades-off fuel consumption against other driver-accommodation objectives, and learns optimal traction torque and transmission shifting policies from experience. The training scheme for the proposed RL agent uses an off-policy actor-critic architecture that iteratively does policy evaluation with a multi-step return and policy improvement with the maximum posteriori policy optimization algorithm for hybrid action spaces. The proposed Eco-driving RL agent is implemented on a commercial vehicle in car following traffic. It shows superior performance in minimizing fuel consumption compared to a baseline controller that has full knowledge of fuel-efficiency tables.

Heating in private households is a major contributor to the emissions generated today. Heat pumps are a promising alternative for heat generation and are a key technology in achieving our goals of the German energy transformation and to become less dependent on fossil fuels. Today, the majority of heat pumps in the field are controlled by a simple heating curve, which is a naive mapping of the current outdoor temperature to a control action. A more advanced control approach is model predictive control (MPC) which was applied in multiple research works to heat pump control. However, MPC is heavily dependent on the building model, which has several disadvantages. Motivated by this and by recent breakthroughs in the field, this work applies deep reinforcement learning (DRL) to heat pump control in a simulated environment. Through a comparison to MPC, it could be shown that it is possible to apply DRL in a model-free manner to achieve MPC-like performance. This work extends other works which have already applied DRL to building heating operation by performing an in-depth analysis of the learned control strategies and by giving a detailed comparison of the two state-of-the-art control methods.

This paper presents a multi-agent Deep Reinforcement Learning (DRL) framework for autonomous control and integration of renewable energy resources into smart power grid systems. In particular, the proposed framework jointly considers demand response (DR) and distributed energy management (DEM) for residential end-users. DR has a widely recognized potential for improving power grid stability and reliability, while at the same time reducing end-users energy bills. However, the conventional DR techniques come with several shortcomings, such as the inability to handle operational uncertainties while incurring end-user disutility, which prevents widespread adoption in real-world applications. The proposed framework addresses these shortcomings by implementing DR and DEM based on real-time pricing strategy that is achieved using deep reinforcement learning. Furthermore, this framework enables the power grid service provider to leverage distributed energy resources (i.e., PV rooftop panels and battery storage) as dispatchable assets to support the smart grid during peak hours, thus achieving management of distributed energy resources. Simulation results based on the Deep Q-Network (DQN) demonstrate significant improvements of the 24-hour accumulative profit for both prosumers and the power grid service provider, as well as major reductions in the utilization of the power grid reserve generators.

在各种控制任务域中，现有控制器提供了基线的性能水平，虽然可能是次优的 - 应维护。依赖于国家和行动空间的广泛探索的强化学习（RL）算法可用于优化控制策略。但是，完全探索性的RL算法可能会在训练过程中降低低于基线水平的性能。在本文中，我们解决了控制政策的在线优化问题，同时最大程度地减少了遗憾的W.R.T基线政策绩效。我们提出了一个共同的仿制学习框架，表示乔尔。 JIRL中的学习过程假设了基线策略的可用性，并设计了两个目标\ textbf {（a）}利用基线的在线演示，以最大程度地减少培训期间的遗憾W.R.T的基线策略，\ textbf {（b） }最终超过了基线性能。 JIRL通过最初学习模仿基线策略并逐渐将控制从基线转移到RL代理来解决这些目标。实验结果表明，JIRR有效地实现了几个连续的动作空间域中的上述目标。结果表明，JIRL在最终性能中与最先进的算法相当，同时在所有提出的域中训练期间都会降低基线后悔。此外，结果表明，对于最先进的基线遗憾最小化方法，其基线后悔的减少因素最高为21美元。

尽管在许多具有挑战性的问题中取得了成功，但增强学习（RL）仍然面临样本效率低下，可以通过将先验知识引入代理人来缓解。但是，在加强学习方面的许多转移技术使教师是专家的局限性假设。在本文中，我们将增强学习中的行动作为推理框架 - 即，在每个状态下的行动分布，类似于教师政策，而不是贝叶斯的先验 - 恢复最先进的策略蒸馏技术。然后，我们提出了一类自适应方法，这些方法可以通过结合奖励成型和辅助正则化损失来鲁sumply动作先验。与先前的工作相反，我们开发了利用次优的动作先验的算法，这些算法可能仍然传授有价值的知识 - 我们称之为软动作先验。拟议的算法通过根据教师在每个州的有用性的估计来调整教师反馈的强度来适应。我们执行表格实验，这表明所提出的方法达到了最先进的性能，在从次优先的先验中学习时超过了它。最后，我们证明了自适应算法在连续动作中的鲁棒性深度RL问题，与现有的策略蒸馏方法相比，自适应算法显着提高了稳定性。

我向已知的数学问题提出了一个深入的加强学习（RL）解决方案，称为新闻温丹主模型，这旨在考虑到概率的需求分布。为了反映更现实和复杂的情况，需求分布可以改变本周不同的日子，从而改变了最佳行为。我使用了一个双延迟的深度确定性政策梯度代理（写为完全原始代码）与演员和批评网络来解决这个问题。该代理能够学习与问题的分析解决方案一致的最佳行为，并且可以识别本周不同日期的单独概率分布并相应地行事。

智能能源网络提供了一种有效的手段，可容纳可变可再生能源（例如太阳能和风能）的高渗透率，这是能源生产深度脱碳的关键。但是，鉴于可再生能源以及能源需求的可变性，必须制定有效的控制和能源存储方案来管理可变的能源产生并实现所需的系统经济学和环境目标。在本文中，我们引入了由电池和氢能存储组成的混合储能系统，以处理与电价，可再生能源生产和消费有关的不确定性。我们旨在提高可再生能源利用率，并最大程度地减少能源成本和碳排放，同时确保网络内的能源可靠性和稳定性。为了实现这一目标，我们提出了一种多代理的深层确定性政策梯度方法，这是一种基于强化的基于强化学习的控制策略，可实时优化混合能源存储系统和能源需求的调度。提出的方法是无模型的，不需要明确的知识和智能能源网络环境的严格数学模型。基于现实世界数据的仿真结果表明：（i）混合储能系统和能源需求的集成和优化操作可将碳排放量减少78.69％，将成本节省的成本储蓄提高23.5％，可续订的能源利用率比13.2％以上。其他基线模型和（ii）所提出的算法优于最先进的自学习算法，例如Deep-Q网络。