class Bot():
"""
This is our Test agent. It's gonna pick some actions after training!
"""
def __init__(self, lr):
self.lr = lr
self.game = Pixelcopter(width=480, height=480)
self.p = PLE(self.game, fps=60, display_screen=True)
self.actions = self.p.getActionSet()
#def pickAction(self, reward, obs):
# return random.choice(self.actions)
def frame_step(act_inp):
terminal = False
reward = self.p.act(act_inp)
if self.p.game_over():
self.p.reset_game()
terminal = True
reward = -1
else:
reward = 1
self.score = self.p.getScore()
img = self.p.getScreenGrayscale()
img = transform.resize(img, (80, 80))
img = np.ravel(exposure.rescale_intensity(img, out_range=(0, 255)))
return img, reward, terminal
def build_model(self):
print("Building the model..")
model = Sequential()
model.add(
Convolution2D(32,
8,
8,
subsample=(4, 4),
border_mode='same',
input_shape=(img_rows, img_cols,
img_channels))) #80*80*4
model.add(Activation('relu'))
model.add(Convolution2D(64, 4, 4, subsample=(2, 2),
border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, subsample=(1, 1),
border_mode='same'))
model.add(Activation('relu'))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dense(2))
adam = Adam(lr=self.lr)
model.compile(loss='mse', optimizer=adam)
self.model = model
print("Finished building the model..")
def trainNetwork(self, mode):
D = deque()
x_t, r_0, terminal = self.frame_step(self.actions[0])
x_t = x_t / 255.0
s_t = np.stack((x_t, x_t, x_t, x_t), axis=2)
#print (s_t.shape)
#need to reshape for keras
s_t = s_t.reshape(1, s_t.shape[0], s_t.shape[1],
s_t.shape[2]) #1*80*80*4
if mode == 'Run':
OBSERVE = 999999999 #We keep observe, never train
epsilon = FINAL_EPSILON
print("Now we load weight")
self.model.load_weights("model.h5")
adam = Adam(lr=self.lr)
self.model.compile(loss='mse', optimizer=adam)
print("Weight load successfully")
else: #We go to training mode
OBSERVE = OBSERVATION
epsilon = INITIAL_EPSILON
