def __init__(self, params):
print "Initializing Module..."
self.params = params
self.sess = tf.Session()
self.DB = database(self.params["db_size"], self.params["input_dims_proc"])
self.engine = emulator(rom_name="breakout.bin", vis=True)
self.params["num_act"] = len(self.engine.legal_actions)
self.build_nets()
self.Q_global = 0
self.cost_disp = 0
def __init__(self, args, sess):
print('Initializing..')
self.args = args
self.sess = sess
self.per = per.PER(self.args)
self.engine = emulator(rom_name='breakout.bin',
vis=self.args.visualize)
self.args.num_actions = len(self.engine.legal_actions)
# Build model
self.build_model()
self.sess.run(tf.global_variables_initializer())
def __init__(self, params):
print 'Initializing Module...'
self.params = params
self.sess = tf.Session()
self.DB = database(self.params['db_size'],
self.params['input_dims_proc'])
self.engine = emulator(rom_name='breakout.bin', vis=True)
self.params['num_act'] = len(self.engine.legal_actions)
self.build_nets()
self.Q_global = 0
self.cost_disp = 0
def __init__(self,params): print 'Initializing Module...' self.params = params self.gpu_config = tf.ConfigProto(gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.params['gpu_fraction'])) self.sess = tf.Session(config=self.gpu_config) self.DB = database(self.params) self.engine = emulator(rom_name='breakout.bin', vis=self.params['visualize'],windowname=self.params['network_type']+'_preview') self.params['num_act'] = len(self.engine.legal_actions) self.build_net() self.training = True
def __init__(self,params):
print('Initializing Module...')
self.params = params
self.gpu_config = tf.ConfigProto(gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.params['gpu_fraction']))
self.sess = tf.Session(config=self.gpu_config)
self.DB = database(self.params)
self.engine = emulator(rom_name='breakout.bin', vis=self.params['visualize'],windowname=self.params['network_type']+'_preview')
self.params['num_act'] = len(self.engine.legal_actions)
self.build_net()
self.training = True
self.lock = _thread.allocate_lock()
def __init__(self, device=None, init_options=None, game=nature_params['game']):
super(DeepQ,self).__init__(device=device, init_options=init_options)
assert game in ["breakout", "space_invaders", "seaquest"]
self.G = tf.Graph()
# NOTE: moved tf.Graph construction to setup
self.params = nature_params
self.DB = database(self.params)
self.engine = emulator(rom_name='{}.bin'.format(game), vis=self.params['visualize'], frameskip=self.params['frameskip'], windowname=self.params['window_name'])
#self.engine = emulator(rom_name='{}.bin'.format(game), vis=self.params['visualize'], frameskip=self.params['frameskip'], windowname=self.params['window_name'])
self.params['num_act'] = len(self.engine.legal_actions)
with self.G.device(device):
self.build_inference()
def __init__(self,params):
print 'Initializing Module...'
self.params = params
# self.gpu_config = tf.ConfigProto(gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=self.params['gpu_fraction']))
# self.sess = tf.Session(config=self.gpu_config)
# GPU使用率
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.8 #固定比例
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config)
self.DB = database(self.params)
self.engine = emulator(rom_name='roms/breakout.bin', vis=self.params['visualize'])
self.params['num_act'] = len(self.engine.legal_actions)
self.build_net()
self.training = not self.params['only_eval']
self.game_cnt = 0
fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')
self.video = cv2.VideoWriter('ckpt/video.mp4', fourcc, 25, (160, 210), True);
sess.run(tf.initialize_variables(targetnet.param_list))
prevstate = newstate
newstate, reward, terminal = engine.next(action) #IMP: newstate contains terminal info
params['eps'] = 0.1 + max(0, (1 - 0.1) * (100000 - max(0, global_cntr))/100000)
total_reward_ep = total_reward_ep + reward
global_cntr = global_cntr + 1
sys.stdout.write("Episode: %d | Training progress: %d | ep_time: %f | reward: %f \n" % (numeps, global_cntr, time.time()-start_time, total_reward_ep))
sys.stdout.flush()
if __name__ == "__main__":
qvals = []
global_cntr = 1
DB = database(params['db_size'], params['input_dims'])
engine = emulator(rom_name='breakout.bin', vis=False)
params['num_actions'] = len(engine.legal_actions)
# creating Q and target network.
qnet = Model(params, None)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
targetnet = Model(params, qnet)
sess.run(tf.initialize_variables(targetnet.param_list))
#cost calculation
discount = tf.constant(params['discount'])
maxval = tf.mul(discount, tf.reduce_max(targetnet.pyx, 1))
yj_val = tf.add(targetnet.rewards, tf.mul(targetnet.terminals, maxval))
nepisodes = None
episode_reward = None
agent_params = {
'subgoal_dims': 7,
'use_distance': True,
'max_reward': 1000,
'min_reward': -1000,
'rescale_r': True
}
# param setting section ending
# training initial section
ag = agent(agent_params)
emu = emulator("montezuma_revenge.bin", False)
rawstate = emu.newGame()
reward = 0
terminal = False
learn_start = ag.learn_start
time_history[1] = 0
# initial section ending
if META_AGENT:
subgoal = ag.pick_subgoal(rawstate, 0, False, False, None)
action_list = [
'no-op', 'fire', 'up', 'right', 'left', 'down', 'up-right', 'up-left',
'down-right', 'down-left', 'up-fire', 'right-fire', 'left-fire',
'down-fire', 'up-right-fire', 'up-left-fire', 'down-right-fire',
'down-left-fire'
params["eps"] = 0.1 + max(0, (1 - 0.1) * (100000 - max(0, global_cntr)) / 100000)
total_reward_ep = total_reward_ep + reward
global_cntr = global_cntr + 1
sys.stdout.write(
"Episode: %d | Training progress: %d | ep_time: %f | reward: %f \n"
% (numeps, global_cntr, time.time() - start_time, total_reward_ep)
)
sys.stdout.flush()
if __name__ == "__main__":
qvals = []
global_cntr = 1
DB = database(params["db_size"], params["input_dims"])
engine = emulator(rom_name="breakout.bin", vis=False)
params["num_actions"] = len(engine.legal_actions)
# creating Q and target network.
qnet = Model(params, None)
sess = tf.Session()
init = tf.initialize_all_variables()
sess.run(init)
targetnet = Model(params, qnet)
sess.run(tf.initialize_variables(targetnet.param_list))
# cost calculation
discount = tf.constant(params["discount"])
maxval = tf.mul(discount, tf.reduce_max(targetnet.pyx, 1))
yj_val = tf.add(targetnet.rewards, tf.mul(targetnet.terminals, maxval))
