Multi-fidelity Reinforcement Learning Control For Complex Dynamical Systems

Abstract

Controlling instabilities in complex dynamical systems is challenging in scientific and engineering applications. Deep reinforcement learning (DRL) has seen promising results for applications in different scientific applications. The many-query nature of control tasks requires multiple interactions with real environments of the underlying physics. However, it is usually sparse to collect from the experiments or expensive to simulate for complex dynamics. Alternatively, controlling surrogate modeling could mitigate the computational cost issue. However, a fast and accurate learning-based model by offline training makes it very hard to get accurate pointwise dynamics when the dynamics are chaotic. To bridge this gap, the current work proposes a multi-fidelity reinforcement learning (MFRL) framework that leverages differentiable hybrid models for control tasks, where a physics-based hybrid model is corrected by limited high-fidelity data. We also proposed a spectrum-based reward function

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysun2025multi

Related papers

• Hyperl: Hypernetwork-based Reinforcement Learning For Control Of Parametrized Dynamical Systems (2025)0.00
• Data-assimilated Model-informed Reinforcement Learning (2025)0.00
• Control-optimized Deep Reinforcement Learning For Artificially Intelligent Autonomous Systems (2025)0.00
• Adaptive Multi-fidelity Reinforcement Learning For Variance Reduction In Engineering Design Optimization (2025)0.00
• Model-based Reinforcement Learning For Control Under Time-varying Dynamics (2026)0.00
• Efficient And Scalable Deep Reinforcement Learning For Mean Field Control Games (2024)0.00
• Reinforcement Learning For Control Systems With Time Delays: A Comprehensive Survey (2026)0.00
• RLOC: Neurobiologically Inspired Hierarchical Reinforcement Learning Algorithm For Continuous Control Of Nonlinear Dynamical Systems (2019)0.00