Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint

Research output: Contribution to conferencePaper


In the context of urgent climate challenges and the pressing need for rapid technology development, Reinforcement Learning (RL) stands as a compelling data-driven method for controlling real-world physical systems. However, RL implementation often entails time-consuming and computationally intensive data collection and training processes, rendering them inefficient for real-time applications that lack non-real-time models. To address these limitations, real-time emulation techniques have emerged as valuable tools for the lab-scale rapid prototyping of intricate energy systems. While emulated systems offer a bridge between simulation and reality, they too face constraints, hindering comprehensive characterization, testing, and development. In this research, we construct a surrogate model using limited data from simulated systems, enabling an efficient and effective training process for a Double Deep Q-Network (DDQN) agent for future deployment. Our approach is illustrated through a hydropower application, demonstrating the practical impact of our approach on climate-related technology development.
Original languageAmerican English
Number of pages9
StatePublished - 2023

NREL Publication Number

  • NREL/CP-5D00-87406


  • deep Q-learning
  • digital real time simulation
  • reinforcement learning
  • surrogate modeling


Dive into the research topics of 'Efficient Reinforcement Learning for Real-Time Hardware-Based Energy System Experiments: Preprint'. Together they form a unique fingerprint.

Cite this