深度强化学习入门：用TensorFlow构建你的第一个游戏AI

#Reseting the win counter

win_cnt = 0

# We want to keep track of the progress of the AI over time, so we save its win count history

win_hist = []

#Epochs is the number of games we play

for e in range(epochs):

loss = 0.

#Resetting the game

env.reset()

game_over = False

# get initial input

input_t = env.observe()

while not game_over:

#The learner is acting on the last observed game screen

#input_t is a vector containing representing the game screen

input_tm1 = input_t

#Take a random action with probability epsilon

if np.random.rand() <= epsilon:

#Eat something random from the menu

action = np.random.randint(0, num_actions, size=1)

else:

#Choose yourself

#q contains the expected rewards for the actions

q = model.predict(input_tm1)

#We pick the action with the highest expected reward

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