装饰 发表于 2025-3-28 15:42:08
http://reply.papertrans.cn/83/8260/825929/825929_41.pngPANIC 发表于 2025-3-28 20:37:14
Distributional RL,Chapter 2 told us that the return on the condition of state or state–action pair is a random variable, and value is the expectation of the random variable.hereditary 发表于 2025-3-29 02:48:09
Minimize Regret,RL adapts the concept of regret in general online machine learning. First, let us review this concept in general machine learning.为现场 发表于 2025-3-29 06:23:13
http://reply.papertrans.cn/83/8260/825929/825929_44.pngFlatter 发表于 2025-3-29 09:37:46
http://reply.papertrans.cn/83/8260/825929/825929_45.png使苦恼 发表于 2025-3-29 12:21:08
Learn from Feedback and Imitation Learning,RL learns from reward signals. However, some tasks do not provide reward signals. This chapter will consider applying RL-alike algorithms to solve the tasks without reward signals.ARCH 发表于 2025-3-29 17:10:34
Zhiqing XiaoIntroduces not only algorithms and mathematical theory behind them, but also implementation details and usage examples.Covers both classical and modern RL algorithms, including algorithms for large moCHYME 发表于 2025-3-29 22:09:11
http://reply.papertrans.cn/83/8260/825929/825929_48.pngPhagocytes 发表于 2025-3-30 01:24:59
https://doi.org/10.1007/978-981-19-4933-3Reinforcement Learning; Deep Reinforcement Learning; Machine Learning; Artificial Intelligence; Python Iallergy 发表于 2025-3-30 05:20:57
978-981-19-4935-7Beijing Huazhang Graphics & Information Co., Ltd, China Machine Press 2024