Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systems

• URL: http://arxiv.org/abs/2603.08418v1
• Date: Mon, 09 Mar 2026 14:15:51 GMT
• Title: Meta-RL with Shared Representations Enables Fast Adaptation in Energy Systems
• Abstract summary: We introduce a novel Meta-RL framework that integrates a bi-level optimization scheme with a hybrid actor-critic architecture.<n>We propose a parameter-sharing mechanism between the outer- and inner-loop actor networks, to reduce redundant learning.<n>Experiments demonstrate effective task adaptation and better performance compared to conventional RL and Meta-RL methods.
• Score: 3.4563147061792527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Meta-Reinforcement Learning addresses the critical limitations of conventional Reinforcement Learning in multi-task and non-stationary environments by enabling fast policy adaptation and improved generalization. We introduce a novel Meta-RL framework that integrates a bi-level optimization scheme with a hybrid actor-critic architecture specially designed to enhance sample efficiency and inter-task adaptability. To improve knowledge transfer, we meta-learn a shared state feature extractor jointly optimized across actor and critic networks, providing efficient representation learning and limiting overfitting to individual tasks or dominant profiles. Additionally, we propose a parameter-sharing mechanism between the outer- and inner-loop actor networks, to reduce redundant learning and accelerate adaptation during task revisitation. The approach is validated on a real-world Building Energy Management Systems dataset covering nearly a decade of temporal and structural variability, for which we propose a task preparation method to promote generalization. Experiments demonstrate effective task adaptation and better performance compared to conventional RL and Meta-RL methods.

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