| 书目名称 | Reinforcement Learning for Sequential Decision and Optimal Control |
| 编辑 | Shengbo Eben Li |
| 视频video | |
| 概述 | Provides a comprehensive and thorough introduction to reinforcement learning, ranging from theory to application.Introduce reinforcement learning from both artificial intelligence and optimal control |
| 图书封面 |  |
| 描述 | .Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, including chess games, computer games and robotic control. RL is also considered to be a promising and powerful tool to create general artificial intelligence in the future. ..As an interdisciplinary field of trial-and-error learning and optimal control, RL resembles how humans reinforce their intelligence by interacting with the environment and provides a principled solution for sequential decision making and optimal control in large-scale and complex problems. Since RL contains a wide range of new |
| 出版日期 | Textbook 2023 |
| 关键词 | Reinforcement Learning; Optimal Control; Engineering Application; Artificial Intelligence; Machine Learn |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-19-7784-8 |
| isbn_softcover | 978-981-19-7786-2 |
| isbn_ebook | 978-981-19-7784-8 |
| copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
发表于 2025-3-21 16:22:41
