Titlebook: Reinforcement Learning; Theory and Python Im Zhiqing Xiao Book 2024 Beijing Huazhang Graphics & Information Co., Ltd, China Machine Press 2

過度

Introduction of Reinforcement Learning (RL), is a type of machine learning task where decisionmakers try to maximize long-term rewards or minimize long-term costs. In an RL task, decision-makers observe the environments, and act according to the observations. After the actions, the decision-makers can get rewards or costs.

長矛

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畫布

高談闊論

PG: Policy Gradient,The policy optimization algorithms in Chaps. 2–6 use the optimal value estimates to find the optimal policy, so those algorithms are called optimal value algorithm. However, estimating optimal values are not necessary for policy optimization.

去才蔑視

,AC: Actor–Critic,Actor–critic method combines the policy gradient method and bootstrapping. On the one hand, it uses policy gradient theorem to calculate policy gradient and update parameters. This part is called actor. On the other hand, it estimates values, and uses the value estimate to bootstrap.

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薄膜

Maximum-Entropy RL,This chapter introduces maximum-entropy RL, which uses the concept of entropy in information theory to encourage exploration.