1. Basic Concepts

本章是强化学习的一些基本先验知识，不做过多介绍。

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Reward： \(r(s, a)\) 是状态s和a的函数

Return： 一条trajectory的奖励之和

Discount return： 对于一条trajectory而言 \(discount return = r_{1} + \gamma r_{2} + \gamma^{2} r_{3} + ...\)

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MDP—Markov decision processes

M： Markov property(memoryless property)

\[ p(s_{t+1}|s_t,a_t,s_{t-1},a_{t-1},\ldots,s_0,a_0)=p(s_{t+1}|s_t,a_t), p(r_{t+1}|s_t,a_t,s_{t-1},a_{t-1},\ldots,s_0,a_0)=p(r_{t+1}|s_t,a_t), \]

D: Stochastic Policy

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