def main(argv):
del argv # Unused.
config = utility.load_config(LOG_DIR)
policy_layers = config.policy_layers
value_layers = config.value_layers
env = config.env(render=True)
network = config.network
with tf.Session() as sess:
agent = simple_ppo_agent.SimplePPOPolicy(sess,
env,
network,
policy_layers=policy_layers,
value_layers=value_layers,
checkpoint=os.path.join(
LOG_DIR, CHECKPOINT))
sum_reward = 0
observation = env.reset()
while True:
action = agent.get_action([observation])
observation, reward, done, _ = env.step(action[0])
time.sleep(0.002)
sum_reward += reward
if done:
break
tf.logging.info("reward: %s", sum_reward)
def main(argv):
del argv # Unused.
config = utility.load_config(LOG_DIR)
print(LOG_DIR)
policy_layers = config.policy_layers
value_layers = config.value_layers
env = config.env(render=True)
network = config.network
with tf.Session() as sess:
agent = simple_ppo_agent.SimplePPOPolicy(sess,
env,
network,
policy_layers=policy_layers,
value_layers=value_layers,
checkpoint=os.path.join(
LOG_DIR, CHECKPOINT))
sum_reward = 0
observation = env.reset()
while True:
# 驱动
command = 'd'
command = bytes(command, encoding='utf8')
arduino.write(command)
# ppo get action
action = agent.get_action([observation])
# transfer ppo action to all motor action
o_action = copy.deepcopy(action)
o_action = env.transform_action_to_motor_command(o_action[0])
print("----- each motor radio -----")
print(o_action)
print("----- each motor angle -----")
pi = 3.14159265359
deg = []
i = 0
for each_rad in o_action[:2]:
now = 180 * each_rad / pi
# output action
command = str(i)
command = bytes(command, encoding='utf8')
arduino.write(command)
'''
command = input('请输入转动方向(+-)')
command = bytes(command, encoding='utf8')
arduino.write(command)
'''
command = str(now)
command = bytes(command, encoding='utf8')
print(command)
arduino.write(command)
# 如需更高速度，要更改Arduino代码
command = '10000'
command = bytes(command, encoding='utf8')
print(command)
arduino.write(command)
i = i + 1
'''
msg = arduino.read(14)
msg = binascii.b2a_hex(msg).decode('utf-8')
print(msg)
'''
observation, reward, done, _ = env.step(action[0])
'''
replace observation with a real observation
'''
time.sleep(0.002)
sum_reward += reward
if done:
break
tf.logging.info("reward: %s", sum_reward)