深度强化学习入门：用TensorFlow构建你的第一个游戏AI

self.discount = discount

def remember(self, states, game_over):

#Save a state to memory

self.memory.append([states, game_over])

#We don't want to store infinite memories, so if we have too many, we just delete the oldest one

if len(self.memory) > self.max_memory:

del self.memory[0]

def get_batch(self, model, batch_size=10):

#How many experiences do we have?

len_memory = len(self.memory)

#Calculate the number of actions that can possibly be taken in the game

num_actions = model.output_shape[-1]

#Dimensions of the game field

env_dim = self.memory[0][0][0].shape[1]

#We want to return an input and target vector with inputs from an observed state...

inputs = np.zeros((min(len_memory, batch_size), env_dim))

#...and the target r + gamma * max Q(s’,a’)

#Note that our target is a matrix, with possible fields not only for the action taken but also

#for the other possible actions. The actions not take the same value as the prediction to not affect them

targets = np.zeros((inputs.shape[0], num_actions))

#We draw states to

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