Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

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Publisher ‏ : ‎ Wiley-IEEE Press; 1st edition (December 26, 2012)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 648 pages
ISBN-10 ‏ : ‎ 111810420X
ISBN-13 ‏ : ‎ 978-1118104200
Item Weight ‏ : ‎ 2.35 pounds
Dimensions ‏ : ‎ 6.45 x 1.4 x 9.5 inches

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Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

In the world of feedback control systems, there are two powerful techniques that have gained significant attention in recent years: reinforcement learning and approximate dynamic programming. These methods have revolutionized the way we approach control problems, offering new ways to optimize performance and adapt to changing environments.

Reinforcement learning is a type of machine learning that enables an agent to learn how to behave in an environment by performing actions and receiving rewards or penalties. By using a trial-and-error approach, the agent can learn optimal control policies that maximize long-term rewards. This technique has been successfully applied to a wide range of control problems, from robotic manipulation to autonomous driving.

Approximate dynamic programming, on the other hand, is a method that uses function approximation to solve large-scale dynamic programming problems. By approximating the value function or policy, it is possible to find near-optimal control strategies for complex systems. This approach has been particularly useful in situations where traditional dynamic programming methods are computationally infeasible.

When combined, reinforcement learning and approximate dynamic programming offer a powerful framework for feedback control. By leveraging the strengths of both techniques, researchers and engineers can develop sophisticated control algorithms that can adapt to uncertain and dynamic environments. These methods have the potential to revolutionize industries ranging from manufacturing to aerospace, offering new opportunities for automation and optimization.

In conclusion, reinforcement learning and approximate dynamic programming are two powerful tools for feedback control that have the potential to transform the way we approach control problems. By combining these methods, researchers can develop innovative solutions that improve performance, adaptability, and efficiency in a wide range of applications.
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