1 # -*- coding: utf-8 -*-
 2 """Contains class representing an LSPI sample."""
 3 
 4 
 5 class Sample(object):
 6 
 7     """Represents an LSPI sample tuple ``(s, a, r, s', absorb)``.
 8     #表達了ＬＳＰＩ的采樣，用ｔｕｐｌｅ表示
 9     Parameters＃輸入參數    
10     ----------
11         
12     state : numpy.array＃狀態向量
13         State of the environment at the start of the sample.采樣開始時環境的狀態
14         ``s`` in the sample tuple.
15         (The usual type is a numpy array.)
16     action : int＃執行的動作的編號
17         Index of action that was executed.
18         ``a`` in the sample tuple
19     reward : float＃從環境中獲得的獎勵
20         Reward received from the environment.
21         ``r`` in the sample tuple
22     next_state : numpy.array＃采用了采樣中的動作后的下一個環境狀態
23         State of the environment after executing the sample's action.
24         ``s'`` in the sample tuple
25         (The type should match that of state.)
26     absorb : bool, optional＃如果這個采樣終結了這個episode那么就返回Ｔｒｕｅ
27         True if this sample ended the episode. False otherwise.
28         ``absorb`` in the sample tuple
29         (The default is False, which implies that this is a
30         non-episode-ending sample)
31 
32 
33     Assumes that this is a non-absorbing sample (as the vast majority
34     of samples will be non-absorbing).
35     ＃假設這個ｓａｍｐｌｅ是不會結束episode的，
36     ＃這么做：設成一個類，是為了方便不同的調用方式
37     This class is just a dumb data holder so the types of the different
38     fields can be anything convenient for the problem domain.
39 
40     For states represented by vectors a numpy array works well.
41 
42     """
43 
44     def __init__(self, state, action, reward, next_state, absorb=False):＃初始化
45         """Initialize Sample instance."""
46         self.state = state
47         self.action = action
48         self.reward = reward
49         self.next_state = next_state
50         self.absorb = absorb
51 
52     def __repr__(self):＃打印的時候調用該函數．
53         """Create string representation of tuple."""
54         return 'Sample(%s, %s, %s, %s, %s)' % (self.state,
55                                                self.action,
56                                                self.reward,
57                                                self.next_state,
58                                                self.absorb)