标题： 基于HMPC辅助的对抗性逆强化学习的智能家庭能源管理 显示英文标题

标题： HMPC-assisted Adversarial Inverse Reinforcement Learning for Smart Home Energy Management

摘要： 本文提出了一种基于对抗逆强化学习（AIRL）的智能家庭能量管理方法，并结合了隐式的热动态模型。在所提出的方法中，首先使用神经网络辅助的分层模型预测控制（HMPC）框架生成历史最优决策。然后，这些决策被用作AIRL模块中的专家演示，该模块旨在训练判别器以区分专家演示和由强化学习代理策略生成的转换，同时更新能够生成混淆判别器的转换的代理策略。所提出的HMPC-AIRL方法消除了显式热动力学模型、不确定参数的先验或预测知识以及人工设计的奖励函数的需求。基于真实数据轨迹的仿真结果证明了所提出方法的有效性和数据效率。 显示英文摘要

摘要： This letter proposes an Adversarial Inverse Reinforcement Learning (AIRL)-based energy management method for a smart home, which incorporates an implicit thermal dynamics model. In the proposed method, historical optimal decisions are first generated using a neural network-assisted Hierarchical Model Predictive Control (HMPC) framework. These decisions are then used as expert demonstrations in the AIRL module, which aims to train a discriminator to distinguish expert demonstrations from transitions generated by a reinforcement learning agent policy, while simultaneously updating the agent policy that can produce transitions to confuse the discriminator. The proposed HMPC-AIRL method eliminates the need for explicit thermal dynamics models, prior or predictive knowledge of uncertain parameters, or manually designed reward functions. Simulation results based on real-world traces demonstrate the effectiveness and data efficiency of the proposed method.