Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings

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Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

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Energy-efficient AI-based Control of Semi-closed Greenhouses Leveraging Robust Optimization in Deep Reinforcement Learning

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MARLISA

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Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics

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MARCO - Multi-Agent Reinforcement learning based COntrol of building HVAC systems

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Multi-zone building control with thermal comfort constraints under disjunctive uncertainty using data-driven robust model predictive control

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Grid-Interactive Efficient Buildings Technical Report Series: Whole-Building Controls, Sensors, Modeling, and Analytics

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Real-Time Autonomous Residential Demand Response Management Based on Twin Delayed Deep Deterministic Policy Gradient Learning

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MARLA-SG: Multi-Agent Reinforcement Learning Algorithm for Efficient Demand Response in Smart Grid

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Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage

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Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks

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Deep reinforcement learning-based strategy for charging station participating in demand response

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Testbed implementation of reinforcement learning-based demand response energy management system

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A comprehensive and modular set of appliance operation MILP models for demand response optimization

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The citylearn challenge 2021

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Collaborative power tracking method of diversified thermal loads for optimal demand response: A MILP-Based decomposition algorithm

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The Rise of Agentic AI: A Review of Definitions, Frameworks, Architectures, Applications, Evaluation Metrics, and Challenges

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Safe reinforcement learning for power system control: A review

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Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning

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An overview of reinforcement learning-based approaches for smart home energy management systems with energy storages

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AgentAI: A comprehensive survey on autonomous agents in distributed AI for industry 4.0

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Attention Based Multi-Agent Reinforcement Learning for Demand Response in Grid-Responsive Buildings

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Demand Response in Microgrids with Attention-Based Deep Reinforcement Learning

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Enhancing Grid-Interactive Buildings Demand Response: Sequential Update-Based Multiagent Deep Reinforcement Learning Approach

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Optimizing grid-interactive buildings demand response: Sequence-based decision-making multi-agent policy decomposition deep reinforcement learning

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Demand Response through Price-setting Multi-agent Reinforcement Learning

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