Koopman-Assisted Reinforcement Learning

📰 ArXiv cs.AI

Learn how Koopman-Assisted Reinforcement Learning simplifies complex systems using data-driven Koopman operators, making reinforcement learning more efficient

advanced Published 5 May 2026

Action Steps

Apply the Koopman operator to lift nonlinear systems into linear coordinates
Use the lifted coordinates to simplify the Bellman equation or Hamilton-Jacobi-Bellman equation
Implement the Koopman-Assisted Reinforcement Learning algorithms to improve learning efficiency
Test the algorithms on high-dimensional or nonlinear systems to evaluate performance
Compare the results with traditional reinforcement learning methods to assess improvements

Who Needs to Know This

Researchers and engineers working on reinforcement learning and control theory can benefit from this approach to tackle high-dimensional or nonlinear systems

Key Insight

💡 The Koopman operator can transform nonlinear systems into approximately linear ones, making reinforcement learning more tractable

Key Takeaways

Learn how Koopman-Assisted Reinforcement Learning simplifies complex systems using data-driven Koopman operators, making reinforcement learning more efficient

Full Article

Title: Koopman-Assisted Reinforcement Learning

Abstract:
arXiv:2403.02290v2 Announce Type: replace Abstract: The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theory. However, these equations become intractable for high-dimensional or nonlinear systems. This paper develops two new reinforcement learning algorithms based on the data-driven Koopman operator, which lifts a nonlinear system into new coordinates where the dynamics become approximately linear, and where H

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