mlsanityData to 14 Oct 2026

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

arXiv 2506.00898 ↗First posted 1 Jun 2025eess.SY cs.SYcited by 0

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 2 of its 8 references that are in the map. First posted on arXiv 1 Jun 2025.

Builds on

2 of 8 references.

References · 2

  1. 2019Deep Reinforcement Learning for Smart Home Energy Managementcited by 30
  2. 2021Physically Consistent Neural Networks for building thermal modeling: theory and analysiscited by 17

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