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 visualize(logdir, outdir, num_agents, num_episodes, checkpoint=None, env_processes=True):
"""Recover checkpoint and render videos from it.
Args:
logdir: Logging directory of the trained algorithm.
outdir: Directory to store rendered videos in.
num_agents: Number of environments to simulate in parallel.
num_episodes: Total number of episodes to simulate.
checkpoint: Checkpoint name to load; defaults to most recent.
env_processes: Whether to step environments in separate processes.
"""
config = utility.load_config(logdir)
with config.unlocked:
config.network = functools.partial(utility.define_network, config.network, config)
config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
config.value_optimizer = getattr(tf.train, config.value_optimizer)
with tf.device('/cpu:0'):
batch_env = utility.define_batch_env(lambda: _create_environment(config, outdir), num_agents,
env_processes)
graph = utility.define_simulation_graph(batch_env, config.algorithm, config)
total_steps = num_episodes * config.max_length
loop = _define_loop(graph, total_steps)
saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step'))
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
utility.initialize_variables(sess, saver, config.logdir, checkpoint, resume=True)
for unused_score in loop.run(sess, saver, total_steps):
pass
batch_env.close()
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 create_env(envid, seed, render=False):
"""Create minitaur or other standard gym environment."""
if 'minitaur' in envid:
from pybullet_envs.minitaur.agents.scripts import utility
from pybullet_envs.minitaur.agents import tools
config = utility.load_config(os.path.expanduser('minitaur_config'))
if 'bad' in envid:
with config.unlocked:
config.env.keywords['accurate_motor_model_enabled'] = False
config.env.keywords['control_latency'] = .0
config.env.keywords['pd_latency'] = .0
config.env.keywords['urdf_version'] = None
env = config.env(render=render)
if config.max_length:
env = tools.wrappers.LimitDuration(env, config.max_length)
env = tools.wrappers.RangeNormalize(env)
env = tools.wrappers.ClipAction(env)
env = tools.wrappers.ConvertTo32Bit(env)
class MySpec(object):
def __init__(self, max_episode_steps):
self.max_episode_steps = max_episode_steps
env.spec = MySpec(1000)
else:
env = gym.make(envid)
# Set up seed.
env.seed(seed)
return env
def main(_):
"""Create or load configuration and launch the trainer."""
utility.set_up_logging()
if not FLAGS.config:
raise KeyError('You must specify a configuration.')
logdir = FLAGS.logdir and os.path.expanduser(
os.path.join(FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp, FLAGS.config)))
try:
config = utility.load_config(logdir)
except IOError:
config = tools.AttrDict(getattr(configs, FLAGS.config)())
config = utility.save_config(config, logdir)
for score in train(config, FLAGS.env_processes):
tf.logging.info('Score {}.'.format(score))
def main(_):
"""Create or load configuration and launch the trainer."""
utility.set_up_logging()
if not FLAGS.config:
raise KeyError('You must specify a configuration.')
logdir = FLAGS.logdir and os.path.expanduser(
os.path.join(FLAGS.logdir, '{}-{}'.format(FLAGS.timestamp,
FLAGS.config)))
try:
config = utility.load_config(logdir)
except IOError:
config = tools.AttrDict(getattr(configs, FLAGS.config)())
config = utility.save_config(config, logdir)
for score in train(config, FLAGS.env_processes):
tf.logging.info('Score {}.'.format(score))
def visualize(logdir,
outdir,
num_agents,
num_episodes,
checkpoint=None,
env_processes=True):
"""Recover checkpoint and render videos from it.
Args:
logdir: Logging directory of the trained algorithm.
outdir: Directory to store rendered videos in.
num_agents: Number of environments to simulate in parallel.
num_episodes: Total number of episodes to simulate.
checkpoint: Checkpoint name to load; defaults to most recent.
env_processes: Whether to step environments in separate processes.
"""
config = utility.load_config(logdir)
with config.unlocked:
config.network = functools.partial(utility.define_network,
config.network, config)
config.policy_optimizer = getattr(tf.train, config.policy_optimizer)
config.value_optimizer = getattr(tf.train, config.value_optimizer)
with tf.device('/cpu:0'):
batch_env = utility.define_batch_env(
lambda: _create_environment(config, outdir), num_agents,
env_processes)
graph = utility.define_simulation_graph(batch_env, config.algorithm,
config)
total_steps = num_episodes * config.max_length
loop = _define_loop(graph, total_steps)
saver = utility.define_saver(exclude=(r'.*_temporary/.*', r'global_step'))
sess_config = tf.ConfigProto(allow_soft_placement=True)
sess_config.gpu_options.allow_growth = True
with tf.Session(config=sess_config) as sess:
utility.initialize_variables(sess,
saver,
config.logdir,
checkpoint,
resume=True)
for unused_score in loop.run(sess, saver, total_steps):
pass
batch_env.close()
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)
