Reinforcement Learning (RL) has shown promise for analyzing and controlling complex physical systems, but its application is often hindered by lengthy training times and the high computational cost of simulating intricate dynamics. Transfer learning offers a pathway to mitigate these challenges by leveraging knowledge from similar tasks, potentially accelerating RL convergence. This article explores how Reduced Order Models (ROMs) can serve as efficient training environments to facilitate transfer learning for RL in dynamical systems.
The Challenge: Training RL on Complex Physics
Training RL agents for complex dynamical systems—such as those encountered in aerospace, energy, or fluid dynamics—requires many iterations, often thousands or millions, especially for high-dimensional state spaces and nonlinear behaviors. Each iteration may involve simulating the system for minutes or even hours, making the process computationally prohibitive. Traditional RL approaches are therefore often intractable for real-world-scale physics problems.
Transfer Learning as a Solution
Transfer learning reduces training time by repurposing models trained on similar, simpler problems. The underlying assumption is that if two domains share sufficient structural similarity, an RL agent can adapt its prior knowledge with only minor adjustments. For instance, an RL policy trained to control a small RC car might learn to operate a tractor-trailer more quickly than a model trained from scratch, despite substantial differences in dynamics. In the context of physics-based RL, we can apply this idea by first training on a faster, simplified simulation environment before fine-tuning on the full-fidelity model.
Reduced Order Models: Efficient Surrogates for Physics
To enable transfer learning, we need a training environment that is both computationally inexpensive and sufficiently representative of the target dynamical system. Reduced Order Models (ROMs) offer exactly that. A ROM simplifies a high-fidelity system by reducing its complexity while preserving essential dynamics and accuracy. ROMs can be derived through various methods, including physics-based simplifications or purely data-driven techniques. In this article, we focus on an unsupervised learning approach described in Data-Driven Science and Engineering by Brunton and Kutz, which constructs a ROM directly from system measurement data, requiring minimal prior knowledge of the underlying physics.
Dynamical systems evolve over time and are often described by differential equations or discrete-time maps. For complex systems, full-order simulations are expensive, but a data-driven ROM can approximate the dominant behaviors with significantly fewer degrees of freedom, enabling faster simulations for RL training.
Methodology: From Data to ROM to RL
The process of applying ROM-based transfer learning in RL involves several steps:
- Generate or collect data from the high-fidelity dynamical system through simulations or experiments.
- Construct a ROM using dimensionality reduction techniques, such as Proper Orthogonal Decomposition (POD) or autoencoders, to capture essential state dynamics.
- Train an RL agent in the ROM environment, where simulation times are drastically reduced.
- Fine-tune the agent on the full-order environment, starting from the policy learned on the ROM, to achieve near-optimal performance with reduced total training time.
This approach is particularly appealing for 2026, where advances in GPU-accelerated physics engines and deep RL have made such hybrid pipelines more accessible. By integrating ROMs into the RL loop, we can significantly cut the computational burden, allowing RL to be applied to previously intractable physical systems.
Conclusion and Outlook
Reduced Order Models provide a practical bridge between the demands of reinforcement learning and the realities of complex physical simulations. By training on ROM surrogates and then fine-tuning on full models, transfer learning becomes a powerful tool to reduce training times and unlock RL applications in engineering, climate modeling, and beyond. The next steps involve validating this approach on real-world systems, exploring adaptive ROMs that evolve with agent learning, and integrating uncertainty quantification to ensure reliability in safety-critical applications.