CMU《最优控制和强化学习|16-745: Optimal Control and Reinforcement Learning》中英字幕deepseek共计23条视频,包括:[01]HW0 Walkthrough.zh_en、[02]Lecture 1_ Dynamics Review.zh_en、[03]Lecture 2_ Dynamics Discretization and Stability.zh_en等,UP主更多精彩视
引言:《Reinforcement Learning for Sequential Decision and Optimal Control》是2023年由Springer出版的强化学习英文书籍。本书面向工程领域的科研人员和工程师,按照原理剖析、主流算法、典型示例的架构,介绍用于复杂系统动态决策及最优控制的强化学习方法。本书内容涵盖了强化学习的基本概念、蒙特卡洛法、时序差分法、动态规...
优化控制(Optimal Control, OC):通过使用精确的数学模型和在线求解复杂的优化问题实现高性能控制。文章特别提到了模型预测控制(MPC)作为一种典型的优化控制方法。 强化学习(Reinforcement Learning, RL):通过试错优化控制策略,以最大化奖励信号。特别是无模型强化学习在多个领域(如自主无人机比赛和四足机器人在复杂地形上...
深入探讨强化学习、近似动态规划和神经动态规划的平台。作者控制界超级大佬,巧妙地将人工智能与最优控制理论结合,为这两个领域的研究者搭建了一座沟通的桥梁。本书的主要优势在于:实用性强:书中提供了大量实例算法和应用,使理论知识更易于理解和应用。跨学科视角:通过融合机器学习和控制理论,为读者提供了更广阔的学习视...
reinforcement learning and optimal control -pdf 摘要: 1.强化学习与最优控制的关系 2.强化学习的基本原理 3.强化学习的应用实例 4.最优控制的基本原理 5.最优控制的应用实例 6.强化学习与最优控制的比较 正文: 强化学习与最优控制是两个在控制理论领域中密切相关的概念。它们都是通过试错学习的方式来达到控制...
The development of reinforcement learning and optimal control has become an impetus of engineering, which has show large potentials on automation. Currently, the optimization applications on robot are facing challenges caused by model bias, high dimensional systems, and computational complexity. To solve...
(展开全部) 作者简介· ··· Dimitri Bertsekas is McAffee Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology, and a member of the National Academy of Engineering. He has researched a broad variety of subjects from optimization theory, control theory,...
ReinforcementLearningandOptimalControlbyDimitriP.BertsekasMassachusettsInstituteofTechnologyDRAFTTEXTBOOKThisisadraftofatextbookthatis..
Optimal controlReinforcement learning (RL)Policy iteration (PI)Adaptive dynamic programming (ADP)a b s t r a c tThis paper studies the infinite-horizon adaptive optimal control of continuous-time linear periodic(CTLP) systems, using reinforcement learning techniques. By means of policy iteration (...