Physics-enhanced reinforcement learning for real-time optimal control of dynamical systems

2026-07-17 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The novel Physics-EnhAnced Reinforcement Learning (PEARL) paradigm addresses the sample inefficiency and dimensionality curse of traditional RL algorithms in controlling nonlinear, complex dynamical systems. PEARL integrates RL with optimal control by employing an actor-adjoint algorithm that utilizes automatic differentiation to compute policy gradients over short horizons. It also approximates future returns via neural networks, significantly reducing environment interactions and mitigating long-term gradient instabilities. Demonstrated through two challenging parametric navigation problems in unsteady flows, PEARL outperforms existing RL algorithms by effectively exploiting differentiable environments. This physics-guided policy learning makes PEARL sample efficient, enables generalization across multiple scenarios for parametric systems, and scales RL to high-dimensional state and action spaces without needing low-dimensional state representations or multi-agent strategies.

Key takeaway

For AI Scientists and Research Scientists developing optimal control strategies for complex, high-dimensional dynamical systems, PEARL offers a significant advancement. You should consider integrating differentiable physics into your RL frameworks to drastically reduce sample requirements and improve generalization. This approach allows scaling RL to previously challenging high-dimensional state and action spaces, enabling more robust and efficient real-time control solutions.

Key insights

Physics-enhanced reinforcement learning (PEARL) uses differentiable dynamics to overcome RL's sample inefficiency and high-dimensionality limitations.

Principles

• Differentiable dynamics improve RL efficiency.
• Adjoint methods stabilize long-term gradients.
• Physics-guided learning enhances generalization.

Method

PEARL employs an actor-adjoint algorithm, leveraging automatic differentiation for policy gradients and neural networks for adjoint-based sensitivities of future returns.

In practice

• Control high-dimensional dynamical systems.
• Navigate parametric systems in unsteady flows.
• Reduce environment interactions in RL.

Topics

• Reinforcement Learning
• Optimal Control
• Dynamical Systems
• Automatic Differentiation
• Policy Gradients
• Sample Efficiency
• Parametric Systems

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.