class Learner(object):
def __init__(self, config):
self.config = config
self.sample_data_queue = queue.Queue(
maxsize=config['sample_queue_max_size'])
#=========== Create Agent ==========
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
model = AtariModel(act_dim)
algorithm = parl.algorithms.IMPALA(
model,
sample_batch_steps=self.config['sample_batch_steps'],
gamma=self.config['gamma'],
vf_loss_coeff=self.config['vf_loss_coeff'],
clip_rho_threshold=self.config['clip_rho_threshold'],
clip_pg_rho_threshold=self.config['clip_pg_rho_threshold'])
self.agent = AtariAgent(algorithm, obs_shape, act_dim,
self.learn_data_provider)
if machine_info.is_gpu_available():
assert get_gpu_count() == 1, 'Only support training in single GPU,\
Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
self.cache_params = self.agent.get_weights()
self.params_lock = threading.Lock()
self.params_updated = False
self.cache_params_sent_cnt = 0
self.total_params_sync = 0
#========== Learner ==========
self.lr, self.entropy_coeff = None, None
self.lr_scheduler = PiecewiseScheduler(config['lr_scheduler'])
self.entropy_coeff_scheduler = PiecewiseScheduler(
config['entropy_coeff_scheduler'])
self.total_loss_stat = WindowStat(100)
self.pi_loss_stat = WindowStat(100)
self.vf_loss_stat = WindowStat(100)
self.entropy_stat = WindowStat(100)
self.kl_stat = WindowStat(100)
self.learn_time_stat = TimeStat(100)
self.start_time = None
self.learn_thread = threading.Thread(target=self.run_learn)
self.learn_thread.setDaemon(True)
self.learn_thread.start()
#========== Remote Actor ===========
self.remote_count = 0
self.batch_buffer = []
self.remote_metrics_queue = queue.Queue()
self.sample_total_steps = 0
self.create_actors()
def learn_data_provider(self):
""" Data generator for fluid.layers.py_reader
"""
while True:
sample_data = self.sample_data_queue.get()
self.sample_total_steps += sample_data['obs'].shape[0]
self.batch_buffer.append(sample_data)
buffer_size = sum(
[data['obs'].shape[0] for data in self.batch_buffer])
if buffer_size >= self.config['train_batch_size']:
batch = {}
for key in self.batch_buffer[0].keys():
batch[key] = np.concatenate(
[data[key] for data in self.batch_buffer])
self.batch_buffer = []
obs_np = batch['obs'].astype('float32')
actions_np = batch['actions'].astype('int64')
behaviour_logits_np = batch['behaviour_logits'].astype(
'float32')
rewards_np = batch['rewards'].astype('float32')
dones_np = batch['dones'].astype('float32')
self.lr = self.lr_scheduler.step()
self.entropy_coeff = self.entropy_coeff_scheduler.step()
yield [
obs_np, actions_np, behaviour_logits_np, rewards_np,
dones_np,
np.float32(self.lr),
np.array([self.entropy_coeff], dtype='float32')
]
def run_learn(self):
""" Learn loop
"""
while True:
with self.learn_time_stat:
total_loss, pi_loss, vf_loss, entropy, kl = self.agent.learn()
self.params_updated = True
self.total_loss_stat.add(total_loss)
self.pi_loss_stat.add(pi_loss)
self.vf_loss_stat.add(vf_loss)
self.entropy_stat.add(entropy)
self.kl_stat.add(kl)
def create_actors(self):
""" Connect to the cluster and start sampling of the remote actor.
"""
parl.connect(self.config['master_address'])
logger.info('Waiting for {} remote actors to connect.'.format(
self.config['actor_num']))
for i in range(self.config['actor_num']):
self.remote_count += 1
logger.info('Remote actor count: {}'.format(self.remote_count))
if self.start_time is None:
self.start_time = time.time()
remote_thread = threading.Thread(target=self.run_remote_sample)
remote_thread.setDaemon(True)
remote_thread.start()
def run_remote_sample(self):
""" Sample data from remote actor and update parameters of remote actor.
"""
remote_actor = Actor(self.config)
cnt = 0
remote_actor.set_weights(self.cache_params)
while True:
batch = remote_actor.sample()
self.sample_data_queue.put(batch)
cnt += 1
if cnt % self.config['get_remote_metrics_interval'] == 0:
metrics = remote_actor.get_metrics()
if metrics:
self.remote_metrics_queue.put(metrics)
self.params_lock.acquire()
if self.params_updated and self.cache_params_sent_cnt >= self.config[
'params_broadcast_interval']:
self.params_updated = False
self.cache_params = self.agent.get_weights()
self.cache_params_sent_cnt = 0
self.cache_params_sent_cnt += 1
self.total_params_sync += 1
self.params_lock.release()
remote_actor.set_weights(self.cache_params)
def log_metrics(self):
""" Log metrics of learner and actors
"""
if self.start_time is None:
return
metrics = []
while True:
try:
metric = self.remote_metrics_queue.get_nowait()
metrics.append(metric)
except queue.Empty:
break
episode_rewards, episode_steps = [], []
for x in metrics:
episode_rewards.extend(x['episode_rewards'])
episode_steps.extend(x['episode_steps'])
max_episode_rewards, mean_episode_rewards, min_episode_rewards, \
max_episode_steps, mean_episode_steps, min_episode_steps =\
None, None, None, None, None, None
if episode_rewards:
mean_episode_rewards = np.mean(np.array(episode_rewards).flatten())
max_episode_rewards = np.max(np.array(episode_rewards).flatten())
min_episode_rewards = np.min(np.array(episode_rewards).flatten())
mean_episode_steps = np.mean(np.array(episode_steps).flatten())
max_episode_steps = np.max(np.array(episode_steps).flatten())
min_episode_steps = np.min(np.array(episode_steps).flatten())
metric = {
'sample_steps': self.sample_total_steps,
'max_episode_rewards': max_episode_rewards,
'mean_episode_rewards': mean_episode_rewards,
'min_episode_rewards': min_episode_rewards,
'max_episode_steps': max_episode_steps,
'mean_episode_steps': mean_episode_steps,
'min_episode_steps': min_episode_steps,
'sample_queue_size': self.sample_data_queue.qsize(),
'total_params_sync': self.total_params_sync,
'cache_params_sent_cnt': self.cache_params_sent_cnt,
'total_loss': self.total_loss_stat.mean,
'pi_loss': self.pi_loss_stat.mean,
'vf_loss': self.vf_loss_stat.mean,
'entropy': self.entropy_stat.mean,
'kl': self.kl_stat.mean,
'learn_time_s': self.learn_time_stat.mean,
'elapsed_time_s': int(time.time() - self.start_time),
'lr': self.lr,
'entropy_coeff': self.entropy_coeff,
}
for key, value in metric.items():
if value is not None:
summary.add_scalar(key, value, self.sample_total_steps)
logger.info(metric)
class Learner(object):
def __init__(self, config):
self.config = config
#=========== Create Agent ==========
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
self.config['obs_shape'] = obs_shape
self.config['act_dim'] = act_dim
model = AtariModel(act_dim)
algorithm = parl.algorithms.A3C(model,
vf_loss_coeff=config['vf_loss_coeff'])
self.agent = AtariAgent(algorithm, config)
if machine_info.is_gpu_available():
assert get_gpu_count() == 1, 'Only support training in single GPU,\
Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
#========== Learner ==========
self.total_loss_stat = WindowStat(100)
self.pi_loss_stat = WindowStat(100)
self.vf_loss_stat = WindowStat(100)
self.entropy_stat = WindowStat(100)
self.lr = None
self.entropy_coeff = None
self.learn_time_stat = TimeStat(100)
self.start_time = None
#========== Remote Actor ===========
self.remote_count = 0
self.sample_data_queue = queue.Queue()
self.remote_metrics_queue = queue.Queue()
self.sample_total_steps = 0
self.params_queues = []
self.create_actors()
def create_actors(self):
""" Connect to the cluster and start sampling of the remote actor.
"""
parl.connect(self.config['master_address'])
logger.info('Waiting for {} remote actors to connect.'.format(
self.config['actor_num']))
for i in six.moves.range(self.config['actor_num']):
params_queue = queue.Queue()
self.params_queues.append(params_queue)
self.remote_count += 1
logger.info('Remote actor count: {}'.format(self.remote_count))
remote_thread = threading.Thread(target=self.run_remote_sample,
args=(params_queue, ))
remote_thread.setDaemon(True)
remote_thread.start()
logger.info('All remote actors are ready, begin to learn.')
self.start_time = time.time()
def run_remote_sample(self, params_queue):
""" Sample data from remote actor and update parameters of remote actor.
"""
remote_actor = Actor(self.config)
cnt = 0
while True:
latest_params = params_queue.get()
remote_actor.set_weights(latest_params)
batch = remote_actor.sample()
self.sample_data_queue.put(batch)
cnt += 1
if cnt % self.config['get_remote_metrics_interval'] == 0:
metrics = remote_actor.get_metrics()
if metrics:
self.remote_metrics_queue.put(metrics)
def step(self):
"""
1. kick off all actors to synchronize parameters and sample data;
2. collect sample data of all actors;
3. update parameters.
"""
latest_params = self.agent.get_weights()
for params_queue in self.params_queues:
params_queue.put(latest_params)
train_batch = defaultdict(list)
for i in range(self.config['actor_num']):
sample_data = self.sample_data_queue.get()
for key, value in sample_data.items():
train_batch[key].append(value)
self.sample_total_steps += sample_data['obs'].shape[0]
for key, value in train_batch.items():
train_batch[key] = np.concatenate(value)
with self.learn_time_stat:
total_loss, pi_loss, vf_loss, entropy, lr, entropy_coeff = self.agent.learn(
obs_np=train_batch['obs'],
actions_np=train_batch['actions'],
advantages_np=train_batch['advantages'],
target_values_np=train_batch['target_values'])
self.total_loss_stat.add(total_loss)
self.pi_loss_stat.add(pi_loss)
self.vf_loss_stat.add(vf_loss)
self.entropy_stat.add(entropy)
self.lr = lr
self.entropy_coeff = entropy_coeff
def log_metrics(self):
""" Log metrics of learner and actors
"""
if self.start_time is None:
return
metrics = []
while True:
try:
metric = self.remote_metrics_queue.get_nowait()
metrics.append(metric)
except queue.Empty:
break
episode_rewards, episode_steps = [], []
for x in metrics:
episode_rewards.extend(x['episode_rewards'])
episode_steps.extend(x['episode_steps'])
max_episode_rewards, mean_episode_rewards, min_episode_rewards, \
max_episode_steps, mean_episode_steps, min_episode_steps =\
None, None, None, None, None, None
if episode_rewards:
mean_episode_rewards = np.mean(np.array(episode_rewards).flatten())
max_episode_rewards = np.max(np.array(episode_rewards).flatten())
min_episode_rewards = np.min(np.array(episode_rewards).flatten())
mean_episode_steps = np.mean(np.array(episode_steps).flatten())
max_episode_steps = np.max(np.array(episode_steps).flatten())
min_episode_steps = np.min(np.array(episode_steps).flatten())
metric = {
'sample_steps': self.sample_total_steps,
'max_episode_rewards': max_episode_rewards,
'mean_episode_rewards': mean_episode_rewards,
'min_episode_rewards': min_episode_rewards,
'max_episode_steps': max_episode_steps,
'mean_episode_steps': mean_episode_steps,
'min_episode_steps': min_episode_steps,
'total_loss': self.total_loss_stat.mean,
'pi_loss': self.pi_loss_stat.mean,
'vf_loss': self.vf_loss_stat.mean,
'entropy': self.entropy_stat.mean,
'learn_time_s': self.learn_time_stat.mean,
'elapsed_time_s': int(time.time() - self.start_time),
'lr': self.lr,
'entropy_coeff': self.entropy_coeff,
}
for key, value in metric.items():
if value is not None:
summary.add_scalar(key, value, self.sample_total_steps)
logger.info(metric)
def should_stop(self):
return self.sample_total_steps >= self.config['max_sample_steps']