Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions

Price: $156.95 – $91.63
(as of Dec 24,2024 02:20:31 UTC – Details)

Publisher ‏ : ‎ Wiley; 1st edition (March 15, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 1136 pages
ISBN-10 ‏ : ‎ 1119815037
ISBN-13 ‏ : ‎ 978-1119815037
Item Weight ‏ : ‎ 1 pounds
Dimensions ‏ : ‎ 0.39 x 0.39 x 0.39 inches

Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions

In the realm of artificial intelligence, two key concepts have emerged as powerful tools for making decisions in complex, uncertain environments: reinforcement learning and stochastic optimization. While these two techniques have traditionally been viewed as separate approaches, recent research has shown that they can be effectively combined into a unified framework for making sequential decisions.

Reinforcement learning is a type of machine learning that involves learning to make decisions by interacting with an environment and receiving feedback in the form of rewards or penalties. This feedback allows the algorithm to learn which actions lead to desirable outcomes and which do not, ultimately leading to a policy that maximizes the cumulative reward over time.

Stochastic optimization, on the other hand, is a mathematical technique for finding the optimal solution to a problem in the presence of uncertainty. This uncertainty can arise from factors such as noisy observations, stochastic processes, or incomplete information. By modeling this uncertainty and incorporating it into the decision-making process, stochastic optimization techniques can help to find robust solutions that perform well under a wide range of conditions.

By combining reinforcement learning with stochastic optimization, researchers have developed a unified framework that leverages the strengths of both approaches. In this framework, the reinforcement learning algorithm learns a policy for making decisions in a dynamic environment, while the stochastic optimization algorithm adjusts this policy to account for uncertainty and variability in the system.

This unified framework has been applied to a wide range of real-world problems, including autonomous driving, robotic manipulation, and financial trading. By incorporating both reinforcement learning and stochastic optimization, researchers have been able to develop decision-making systems that are not only robust and reliable but also capable of adapting to changing conditions in real-time.

Overall, the combination of reinforcement learning and stochastic optimization represents a powerful approach to making sequential decisions in complex, uncertain environments. By unifying these two techniques into a single framework, researchers have opened up new possibilities for developing intelligent systems that can learn, adapt, and thrive in a wide range of applications.
#Reinforcement #Learning #Stochastic #Optimization #Unified #Framework #Sequential #Decisions