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Update 10.1.1-强化学习的基本概念.md #185

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4 changes: 2 additions & 2 deletions Textbook/第10章-强化学习系统/10.1.1-强化学习的基本概念.md
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<div align="center">
<img src="./img/concept_of_rl.png" ch="500" width=60%/>
</div>
<center>图10.1.1. 强化学习里的基本概念 </center>
<div align=center>图10.1.1. 强化学习里的基本概念</div>

由于真实的环境复杂度较高，且存在很多与当前问题无关的冗余信息, 通常构建模拟器（Simulator）来模拟真实环境。智能体（Agent）通常指做出决策的模型，即强化学习算法本身。智能体会根据当前这一时刻的状态 $s_t$ 执行动作 $a_t$ 来和环境（Envoriment）交互；同时，环境会将智能体执行动作后到达的下一个时刻的状态 $s_{t+1}$ 和这个动作拿到的奖励 $r_t$ 返回给智能体。智能体可以收集一个时间段内的状态，动作和奖励 $(𝑎_1, 𝑠_1, 𝑟_1, . . . , 𝑎_𝑡, 𝑠_𝑡, 𝑟_𝑡)$ 等历史信息，并将其用来训练自身的强化学习模型。

Expand Down Expand Up @@ -97,4 +97,4 @@ $$V^{\pi}\left(s_{t}=s\right)=\mathbb{E}_{\pi}\left[r_{t}+\gamma r_{t+1}+\gamma^
- [14] Wang S, Jia D, Weng X. Deep reinforcement learning for autonomous driving[J]. arXiv preprint arXiv:1811.11329, 2018.
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