Reinforcement Learning for Sequential Decision and Optimal Control

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(as of Dec 24,2024 14:31:42 UTC – Details)

Publisher ‏ : ‎ Springer; 2023rd edition (April 7, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 496 pages
ISBN-10 ‏ : ‎ 9811977860
ISBN-13 ‏ : ‎ 978-9811977862
Item Weight ‏ : ‎ 1.69 pounds
Dimensions ‏ : ‎ 6.61 x 1.12 x 9.45 inches

Reinforcement Learning for Sequential Decision and Optimal Control

Reinforcement learning is a powerful machine learning technique that is used to train agents to make sequential decisions in order to achieve optimal control. In this post, we will explore how reinforcement learning can be applied to the field of sequential decision making and optimal control.

Sequential decision making involves making a series of decisions over time in order to achieve a desired outcome. This can be seen in a wide range of applications, such as robotics, autonomous driving, and game playing. In these scenarios, an agent must learn how to navigate a complex environment and make decisions that will lead to the best possible outcome.

Reinforcement learning is a type of machine learning that involves training an agent through trial and error. The agent receives feedback in the form of rewards or penalties based on its actions, and uses this feedback to learn how to make better decisions over time. By using reinforcement learning, agents can learn how to navigate complex environments and make decisions that lead to optimal control.

In the context of sequential decision making, reinforcement learning can be used to train agents to make decisions that lead to optimal control. For example, in the case of autonomous driving, a reinforcement learning agent can learn how to navigate a city while obeying traffic laws and avoiding accidents. By receiving feedback in the form of rewards for safe driving and penalties for accidents, the agent can learn how to make decisions that lead to optimal control.

Overall, reinforcement learning is a powerful tool for training agents to make sequential decisions and achieve optimal control. By using this technique, agents can learn how to navigate complex environments and make decisions that lead to the best possible outcomes. As the field of reinforcement learning continues to advance, we can expect to see even more applications of this technique in the field of sequential decision making and optimal control.
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