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Titlebook: Deep Reinforcement Learning; Fundamentals, Resear Hao Dong,Zihan Ding,Shanghang Zhang Book 2020 Springer Nature Singapore Pte Ltd. 2020 Dee

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樓主: 戰(zhàn)神
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發(fā)表于 2025-3-28 16:54:58 | 只看該作者
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發(fā)表于 2025-3-28 20:06:38 | 只看該作者
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發(fā)表于 2025-3-29 02:52:27 | 只看該作者
Robust Image Enhancementshow how to implement an agent on this MDP with PPO algorithm. The experimental environment is constructed by a real-world dataset that contains 5000 photographs with both the raw images and adjusted versions by experts. Codes are available at: ..
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發(fā)表于 2025-3-29 05:05:32 | 只看該作者
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發(fā)表于 2025-3-29 10:59:19 | 只看該作者
https://doi.org/10.1007/978-3-531-92792-3 and optimal policy can be derived through solving the Bellman equations. Three main approaches for solving the Bellman equations are then introduced: dynamic programming, Monte Carlo method, and temporal difference learning. We further introduce deep reinforcement learning for both policy and value
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Introduction to Reinforcement Learning and optimal policy can be derived through solving the Bellman equations. Three main approaches for solving the Bellman equations are then introduced: dynamic programming, Monte Carlo method, and temporal difference learning. We further introduce deep reinforcement learning for both policy and value
48#
發(fā)表于 2025-3-29 21:14:32 | 只看該作者
Book 2020pplications, such as the intelligent transportation system and learning to run, with detailedexplanations.?..The book is intended for computer science students, both undergraduate and postgraduate, who would like to learn DRL from scratch, practice its implementation, and explore the research topics
49#
發(fā)表于 2025-3-30 01:35:54 | 只看該作者
Hao Dong,Zihan Ding,Shanghang ZhangOffers a comprehensive and self-contained introduction to deep reinforcement learning.Covers deep reinforcement learning from scratch to advanced research topics.Provides rich example codes (free acce
50#
發(fā)表于 2025-3-30 04:39:08 | 只看該作者
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