From Shortest Paths To Reinforcement Learning
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From Shortest Paths to Reinforcement Learning
Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.
Proceedings of the 2nd International Conference on Internet of Things, Communication and Intelligent Technology
This conference discussed the application of communication and IoT engineering in the era of smart technologies from the perspective of disciplinary integration, combining the theory and relevant algorithms of IoT and smart technologies. The book encompasses the entire spectrum of IoT solutions, from IoT to cybersecurity. It explores communication systems, including sixth generation (6G) mobile, D2D and M2M communications. It also focuses on intelligent technologies, especially information systems modeling and simulation. In addition, it explores the areas of pervasive computing, distributed computing, high performance computing, pervasive and mobile computing, and cloud computing.
Statistical Reinforcement Learning
Reinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and gaming have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. The book provides a bridge between RL and data mining and machine learning research.