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Optimal chaos control through reinforcement learning.
Sabino Gadaleta1, Gerhard Dangelmayr
1Colorado State University, Department of Mathematics, Engineering E121, Ft. Collins, Colorado 80523.
Chaos (Woodbury, N.Y.)
|June 5, 2003
Summary
A novel reinforcement learning algorithm effectively controls chaos by stabilizing unstable orbits without prior system knowledge. This method demonstrates robust performance even in noisy, nonstationary environments.
Area of Science:
- Nonlinear dynamics
- Chaos theory
- Machine learning
Background:
- Chaotic systems exhibit complex, unpredictable behavior.
- Controlling chaos is crucial for applications in physics and engineering.
- Existing methods often require detailed system information.
Purpose of the Study:
- Introduce a general-purpose chaos control algorithm.
- Apply the algorithm to stabilize unstable periodic orbits.
- Utilize reinforcement learning for adaptive control.
Main Methods:
- Developed a reinforcement learning-based algorithm.
- Applied the algorithm to diverse chaotic systems.
- Tested performance in stabilization and targeting problems.
Main Results:
- The algorithm successfully stabilized unstable periodic orbits.
- It achieved fast performance in numerical tests.
- Demonstrated robustness under noisy and nonstationary conditions.
Conclusions:
- Reinforcement learning offers a powerful approach to chaos control.
- The developed algorithm is versatile and requires no prior system knowledge.
- This method provides an efficient solution for controlling chaotic dynamics.