A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments

• URL: http://arxiv.org/abs/2407.11489v1
• Date: Tue, 16 Jul 2024 08:23:20 GMT
• Title: A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
• Authors: Junlin Lu, Patrick Mannion, Karl Mason,
• Abstract summary: We extend state-of-the-art MORL algorithms with the meta-learning paradigm. We employ an auto-encoder (AE)-based unsupervised method to detect environment context changes. This study assesses the application of MORL in residential appliance scheduling and underscores the effectiveness of meta-learning in energy management.
• Score: 2.9845592719739127
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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