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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Introduction to Deep Learningth a naive single-layer network and gradually progress to much more complex but powerful architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). We will end this chapter with a couple of examples that demonstrate how to implement deep learning models in practice.

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https://doi.org/10.1007/978-3-531-92792-3 the typical and popular algorithms in a structural way. We classify reinforcement learning algorithms from different perspectives, including model-based and model-free methods, value-based and policy-based methods (or combination of the two), Monte Carlo methods and temporal-difference methods, on-

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