Summary of A Meta-learning Approach For Multi-objective Reinforcement Learning in Sustainable Home Environments, by Junlin Lu et al.
A Meta-Learning Approach for Multi-Objective Reinforcement Learning in Sustainable Home Environments
by Junlin Lu, Patrick Mannion, Karl Mason
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the problem of scheduling residential appliances to achieve sustainable living. They build upon multi-objective reinforcement learning (MORL) by incorporating meta-learning to adapt to changing contexts in non-stationary settings. The team also develops an auto-encoder-based method to detect environment context shifts. To evaluate their approach, they use real-world data from London and create a residential energy environment. Their top-performing method outperforms the best baseline, resulting in cost savings, increased user comfort, and improved expected utility. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses machine learning to schedule appliances in homes for sustainable living. The researchers make a breakthrough by combining two ideas: multi-objective reinforcement learning (MORL) and meta-learning. This helps the system adapt quickly to changing situations at home. They also create a special tool to figure out when the situation changes. To test their idea, they use real data from London homes. Their best approach does better than the others, which means it saves money on electricity bills, makes people more comfortable, and improves how well things work overall. |
Keywords
* Artificial intelligence * Encoder * Machine learning * Meta learning * Reinforcement learning
