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Titlebook: Reinforcement Learning Algorithms: Analysis and Applications; Boris Belousov,Hany Abdulsamad,Jan Peters Book 2021 The Editor(s) (if applic

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樓主: Hayes
41#
發(fā)表于 2025-3-28 16:00:37 | 只看該作者
1860-949X e field.This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is pl
42#
發(fā)表于 2025-3-28 20:19:26 | 只看該作者
43#
發(fā)表于 2025-3-29 01:34:00 | 只看該作者
Fisher Information Approximations in Policy Gradient Methodsffline estimation methods as well as surveys more recent developments such as the expectation approximation technique based on the Kronecker-factored approximate curvature (KFAC) method and extensions thereof. The trade-offs introduced by the approximations in the context of policy gradient methods are discussed.
44#
發(fā)表于 2025-3-29 05:01:44 | 只看該作者
45#
發(fā)表于 2025-3-29 09:29:00 | 只看該作者
Challenges of Model Predictive Control in a Black Box Environmentg prominently discussed in the corressponding papers are crucial to the algorithm. In this paper, we review recent approaches revolving around the use of MPC for model-based RL in order to connect them to the conceptual problems that need to be tackled when using MPC in a learning scenario.
46#
發(fā)表于 2025-3-29 14:52:51 | 只看該作者
47#
發(fā)表于 2025-3-29 17:36:19 | 只看該作者
Model-Free Deep Reinforcement Learning—Algorithms and Applicationslyzed and associated with new improvements in order to overcome previous problems. Further, the survey shows application scenarios for difficult domains, including the game of Go, Starcraft II, Dota 2, and the Rubik’s Cube.
48#
發(fā)表于 2025-3-29 22:09:23 | 只看該作者
Actor vs Critic: Learning the Policy or Learning the Valuein circumstances. In this paper, we will compare these methods and identify their advantages and disadvantages. Moreover, we will illustrate the insights obtained using the examples of REINFORCE, DQN and DDPG for a better understanding. Finally, we will give brief suggestions about which approach to use under certain conditions.
49#
發(fā)表于 2025-3-30 03:14:46 | 只看該作者
Distributed Methods for Reinforcement Learning Surveyroaches. We introduce the general principle and problem formulation, and discuss the historical development of distributed methods. We also analyze technical challenges, such as process communication and memory requirements, and give an overview of different application areas.
50#
發(fā)表于 2025-3-30 07:25:37 | 只看該作者
er auf dem Gebiet Automotive.Includes supplementary material.Kraftfahrzeuge bestimmen wesentlich unser t?gliches Leben. Ihre Entwicklung ist eng verknüpft mit der jeweiligen wirtschaftlichen, politischen und sozialen Situation. Eine wichtige Rolle spielen die wissenschaftlichen Methoden und Erkenntn
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