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

2026-03-09

AI Summary

This work addresses the slow adaptation and limited generalization of conventional reinforcement learning in multi-task and non-stationary energy systems by proposing a novel meta-reinforcement learning framework. The approach integrates bilevel optimization with a hybrid Actor-Critic architecture, jointly optimizing a shared state feature extractor and incorporating a parameter-sharing mechanism between inner- and outer-loop policy networks to significantly enhance sample efficiency and cross-task adaptability. Experimental evaluation on a decade-long real-world building energy management dataset demonstrates that the proposed framework achieves faster adaptation upon task revisitation and superior control performance compared to existing reinforcement learning and meta-reinforcement learning methods.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Meta-Reinforcement Learning
• Fast Adaptation
• Energy Systems
• Multi-task Learning
• Non-stationary Environments

Innovation

• Meta-Reinforcement Learning
• Shared Representations
• Bi-level Optimization
• Actor-Critic Architecture
• Sample Efficiency