def run_DQN():
import agent
import time
import exp
import rl_vrep
rl_vrep.connect()
rl_vrep.start()
time.sleep(0.5)
agent.setup_task()
time.sleep(0.5)
agent.setup()
time.sleep(0.5)
step = 0
for epi in range(30000):
print('step:', step)
observation = np.empty(shape=(10, 10))
observation_ = np.empty(shape=(10, 10))
for i in range(10):
observation_i = agent.observestate()
observation_i = agent.unwrap_state(observation_i)
observation[i] = np.array(observation_i)
observation = observation[np.newaxis, :]
observation = Robot.sess.run(Robot.rnn_out,
feed_dict={Robot.rnn_in: observation})
#observation = np.mean(observation,axis= 0)
action = Robot.choose_action(observation)
agent.execute_action(action)
print('action:', action)
#time.sleep(0.5)
r = agent.get_reward()
print('reward:', r)
for i in range(10):
observation_i = agent.observestate()
observation_i = agent.unwrap_state(observation_i)
observation_[i] = np.array(observation_i)
#observation_ = np.mean(observation_,axis= 0)
#.........
observation_ = observation_[np.newaxis, :]
observation_ = Robot.sess.run(Robot.rnn_out,
feed_dict={Robot.rnn_in: observation_})
Robot.store(observation, action, r, observation_)
if (step > 200) and (step % 10 == 0):
Robot.learn()
observation = observation_
step += 1
print('run over!')
def setup():
""" task module setup is performed in agent """
agent.setup_task()
update_op = []
for g_v, l_v in zip(global_vars, local_vars):
update_op.append(l_v.assign(g_v))
return update_op
#def anneal_lr(self):
#return tf.cond((self.lr > 0.0),
#lambda:tf.assign_sub(self.lr,self.delta_lr),
#lambda:tf.assign(self.lr,0.0))
if __name__ == "__main__":
rl_vrep.connect()
rl_vrep.start()
time.sleep(0.5)
agent.setup_task()
time.sleep(0.5)
agent.setup()
time.sleep(0.5)
global_steps_counter = itertools.count()
global_net = ACNet('global')
workers = []
for i in range(1, N_WORKERS + 1):
worker = WORKER(i, global_steps_counter)
workers.append(worker)
with tf.Session() as sess:
COORD = tf.train.Coordinator()
print('Initializing\n')
sess.run(tf.global_variables_initializer())
workers_threads = []
for worker in workers:
def setup():
agent.setup_task()
