October 2020
Intermediate to advanced
693 pages
16h 26m
Chinese
Content preview from 机器学习实战：基于Scikit-Learn、Keras 和TensorFlow （原书第2 版）
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强化学习
｜
565
观察值都是 84
×
84
×
4。类似地，step_type 张量的形状为 [2，3] ：在此示例中，两
个轨迹在一个回合的中间都包含三个连续步骤（类型 1、1、1 ）。在第二个轨迹中，你几
乎看不到第一个观测值的左下角的球，并且在接下来的两个观测值中球消失了，因此智
能体即将要失去生命，但是该回合不会立即结束，因为还有几条生命。
图 18-15：两个包含三个连续步骤的轨迹
每个轨迹都是连续时间步骤和动作步骤序列的简明表示，旨在避免冗余。为何如此？如
图 18-16 所示，转换
n
由时间步骤
n
、
动作步骤
n
和时间步骤
n
+ 1 组成，而转换
n
+1
由时间步骤
n
+ 1、动作步骤
n
+ 1 和时间步长
n
+ 2 组成。如果我们将这两个转换直接
存储在重播缓冲区中，则时间步骤
n
+ 1 会有重复。为了避免这种重复，第
n
个轨迹步
骤仅包含时间步骤
n
的类型和观察结果（不包括其奖励和折扣），不包含时间步骤
n
+ 1
的观察结果（但是，它确实包含了下一步骤的类型的副本，这是唯一的重复）。
注意
：
轨迹步骤
n
也包括
图 18-16：轨迹、转换、时间步骤和动作步骤
566
｜
第
18
章
因此，如果你有一批轨迹，其中每条轨迹有
t
+ 1 步（从时间步骤
n
到时间步骤
n
+
t
），
那么它包含从时间步骤
n
到时间步骤
n
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