A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
<|reference_start|>A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments: Effective residential appliance scheduling is crucial for sustainable living. While multi-objective reinforcement learning (MORL) has proven effective in balancing user preferences in appliance scheduling, traditional MORL struggles with limited data in non-stationary residential settings characterized by renewable generation variations. Significant context shifts that can invalidate previously learned policies. To address these challenges, we extend state-of-the-art MORL algorithms with the meta-learning paradigm, enabling rapid, few-shot adaptation to shifting contexts. Additionally, we employ an auto-encoder (AE)-based unsupervised method to detect environment context changes. We have also developed a residential energy environment to evaluate our method using real-world data from London residential settings. This study not only assesses the application of MORL in residential appliance scheduling but also underscores the effectiveness of meta-learning in energy management. Our top-performing method significantly surpasses the best baseline, while the trained model saves 3.28% on electricity bills, a 2.74% increase in user comfort, and a 5.9% improvement in expected utility. Additionally, it reduces the sparsity of solutions by 62.44%. Remarkably, these gains were accomplished using 96.71% less training data and 61.1% fewer training steps.<|reference_end|>
@article{lu2024a,
    title={A Meta-Learning Approach for Multi-Objective Reinforcement Learning in
  Sustainable Home Environments},
    author={Junlin Lu, Patrick Mannion, Karl Mason},
    journal={arXiv preprint arXiv:2407.11489},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11489},
    primaryClass={cs.LG cs.AI}
}