深度强化学习入门：用TensorFlow构建你的第一个游戏AI

learn from randomly

for i, idx in enumerate(np.random.randint(0, len_memory,

size=inputs.shape[0])):

"""

Here we load one transition <s, a, r, s’> from memory

state_t: initial state s

action_t: action taken a

reward_t: reward earned r

state_tp1: the state that followed s’

"""

state_t, action_t, reward_t, state_tp1 = self.memory[idx][0]

#We also need to know whether the game ended at this state

game_over = self.memory[idx][1]

#add the state s to the input

inputs[i:i+1] = state_t

# First we fill the target values with the predictions of the model.

# They will not be affected by training (since the training loss for them is 0)

targets[i] = model.predict(state_t)[0]

"""

If the game ended, the expected reward Q(s,a) should be the final reward r.

Otherwise the target value

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