机器学习者强化学习综述

English | July 27, 2024 | ASIN: B0D4FB33VD | 64 pages | EPUB | 0.52 Mb

"A Comprehensive Overview of Reinforcement Learning for Machine Learners"

Explore the frontiers of artificial intelligence (AI) with this in-depth book written for professionals, researchers, and students who are keen to learn about the field of reinforcement learning. The book "A Comprehensive Overview of Reinforcement Learning for Machine Learners" provides a thorough examination of the fundamental ideas, sophisticated methods, and practical uses of reinforcement learning.

Important Elements Intense Reporting: This book covers everything from the fundamentals of reinforcement learning to more complex subjects like safe exploration and hierarchical reinforcement learning. Algorithm Insights: Comprehensive justifications of important algorithms, including Deep Q-Networks, Policy Gradient techniques, Q-learning, and many more. In Real-World Uses: Examine the applications of reinforcement learning in the fields of robotics, gaming, computer vision, natural language processing, and finance. Sophisticated Methods: Learn about intricate subjects including model-based reinforcement learning, meta-reinforcement learning, transfer learning, and multi-agent reinforcement learning. Practical Examples: A plethora of case studies and examples that highlight the application and influence of reinforcement learning in resolving real-world issues. Going Forward: examines the difficulties facing the science of reinforcement learning today and possible advancements in the future.

1. Introduction to Reinforcement Learning: Describes reinforcement learning and contrasts it with other machine learning paradigms in order to lay the groundwork.

2. Reinforcement Learning Foundations: investigates temporal difference learning, dynamic programming, and Monte Carlo techniques.

3. Deep Reinforcement Learning: investigates policy gradient approaches, deep Q-networks, and sophisticated deep RL algorithms.

4. Reinforcement learning based on models: covers hybrid techniques, learning models from data, and planning using learnt models.

5. Reinforcement Learning for Robotics: explains how RL is used in robotics for safety, locomotion, and manipulation.

6. Game Playing with Reinforcement Learning: Examines RL techniques in multiplayer, real-time strategy, and traditional games.

7. Natural Language Processing using Reinforcement Learning: examines the use of RL in text summarization, machine translation, dialogue systems, and text production.

8. Reinforcement Learning for Computer Vision: Investigates the use of RL to robotics, picture production, video interpretation, and recognition.

9. Reinforcement Learning for Finance: Describes the use of RL in financial modeling, trading, risk management, and portfolio management.

10. Advanced Topics in Reinforcement Learning: Explores transfer learning, meta-RL, safe RL, multi-agent systems, and hierarchical RL.

This book gives you the knowledge and resources you need to thrive in the quickly changing field of artificial intelligence, regardless of whether you are an experienced researcher wishing to expand your knowledge or a practitioner looking to apply reinforcement learning in your industry.

英文|2024年7月27日|ASIN:B0D4FB33VD|64页|EPUB|0.52 Mb “机器学习者强化学习综合概述” 通过这本为专业人士、研究人员和热衷于学习强化学习领域的学生撰写的深入书籍，探索人工智能（AI）的前沿。《机器学习者强化学习综合概述》一书对强化学习的基本思想、复杂方法和实际应用进行了全面的考察。 重要元素强化报告：这本书涵盖了从强化学习的基础知识到更复杂的主题，如安全探索和分层强化学习。算法洞察：对重要算法的全面论证，包括深度Q网络、策略梯度技术、Q学习等等。在现实世界中的应用：研究强化学习在机器人、游戏、计算机视觉、自然语言处理和金融领域的应用。复杂方法：了解复杂的主题，包括基于模型的强化学习、元强化学习、迁移学习和多智能体强化学习。实例：大量的案例研究和例子，突出了强化学习在解决现实世界问题中的应用和影响。展望未来：研究当今强化学习科学面临的困难和未来可能的进步。 1.强化学习导论：介绍强化学习，并将其与其他机器学习范式进行对比，以奠定基础。 2.强化学习基础：研究时间差分学习、动态规划和蒙特卡洛技术。 3.深度强化学习：研究策略梯度方法、深度Q网络和复杂的深度强化学习算法。 4.基于模型的强化学习：包括混合技术、从数据中学习模型以及使用学习模型进行规划。 5.机器人强化学习：解释了强化学习如何在机器人中用于安全、运动和操纵。 6.强化学习游戏：考察多人游戏、实时策略和传统游戏中的强化学习技术。 7.使用强化学习的自然语言处理：研究强化学习在文本摘要、机器翻译、对话系统和文本制作中的应用。 8.计算机视觉强化学习：研究强化学习在机器人、图片制作、视频解释和识别中的应用。 9.金融强化学习：描述强化学习在金融建模、交易、风险管理和投资组合管理中的应用。 10.强化学习高级主题：探讨迁移学习、元强化学习、安全强化学习、多智能体系统和分层强化学习。 本书为您提供了在快速变化的人工智能领域蓬勃发展所需的知识和资源，无论您是希望扩展知识的经验丰富的研究人员，还是希望在您的行业中应用强化学习的从业者。

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