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train_impala.py
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train_impala.py
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import json
import time
import utils
import wrappers
import tensorflow as tf
import numpy as np
from tensorboardX import SummaryWriter
from distributed_queue import buffer_queue
from agent import impala
flags = tf.app.flags
FLAGS = tf.app.flags.FLAGS
flags.DEFINE_integer('task', -1, "Task id. Use -1 for local training")
flags.DEFINE_enum('job_name',
'learner',
['learner', 'actor'],
'Job name. Ignore when task is set to -1')
def main(_):
data = json.load(open('config.json'))
data = data['impala']
utils.check_properties(data)
local_job_device = f'/job:{FLAGS.job_name}/task:{FLAGS.task}'
shared_job_device = '/job:learner/task:0'
is_learner = FLAGS.job_name == 'learner'
cluster = tf.train.ClusterSpec({
'actor': ['localhost:{}'.format(data['server_port']+1+i) for i in range(data['num_actors'])],
'learner': ['{}:{}'.format(data['server_ip'], data['server_port'])]})
server = tf.train.Server(cluster, job_name=FLAGS.job_name, task_index=FLAGS.task)
with tf.device(shared_job_device):
with tf.device('/cpu'):
queue = buffer_queue.FIFOQueue(
trajectory=data['trajectory'],
input_shape=data['model_input'],
output_size=data['model_output'],
queue_size=data['queue_size'],
batch_size=data['batch_size'],
num_actors=data['num_actors'],
lstm_size=data['lstm_size'])
learner = impala.Agent(
trajectory=data['trajectory'],
input_shape=data['model_input'],
num_action=data['model_output'],
lstm_hidden_size=data['lstm_size'],
discount_factor=data['discount_factor'],
start_learning_rate=data['start_learning_rate'],
end_learning_rate=data['end_learning_rate'],
learning_frame=data['learning_frame'],
baseline_loss_coef=data['baseline_loss_coef'],
entropy_coef=data['entropy_coef'],
gradient_clip_norm=data['gradient_clip_norm'],
reward_clipping=data['reward_clipping'],
model_name='learner',
learner_name='learner')
with tf.device(local_job_device):
actor = impala.Agent(
trajectory=data['trajectory'],
input_shape=data['model_input'],
num_action=data['model_output'],
lstm_hidden_size=data['lstm_size'],
discount_factor=data['discount_factor'],
start_learning_rate=data['start_learning_rate'],
end_learning_rate=data['end_learning_rate'],
learning_frame=data['learning_frame'],
baseline_loss_coef=data['baseline_loss_coef'],
entropy_coef=data['entropy_coef'],
gradient_clip_norm=data['gradient_clip_norm'],
reward_clipping=data['reward_clipping'],
model_name=f'actor_{FLAGS.task}',
learner_name='learner')
sess = tf.Session(server.target)
queue.set_session(sess)
learner.set_session(sess)
if not is_learner:
actor.set_session(sess)
if is_learner:
writer = SummaryWriter('runs/learner')
train_step = 0
while True:
size = queue.get_size()
if size > 3 * data['batch_size']:
print('train {}'.format(train_step))
train_step += 1
batch = queue.sample_batch()
s = time.time()
pi_loss, baseline_loss, entropy, learning_rate = learner.train(
state=np.stack(batch.state),
reward=np.stack(batch.reward),
action=np.stack(batch.action),
done=np.stack(batch.done),
behavior_policy=np.stack(batch.behavior_policy),
previous_action=np.stack(batch.previous_action),
initial_h=np.stack(batch.previous_h),
initial_c=np.stack(batch.previous_c))
writer.add_scalar('data/pi_loss', pi_loss, train_step)
writer.add_scalar('data/baseline_loss', baseline_loss, train_step)
writer.add_scalar('data/entropy', entropy, train_step)
writer.add_scalar('data/learning_rate', learning_rate, train_step)
writer.add_scalar('data/time', time.time() - s, train_step)
else:
trajectory = utils.UnrolledTrajectory()
env = wrappers.make_uint8_env(data['env'][FLAGS.task])
state = env.reset()
previous_action = 0
previous_h = np.zeros([data['lstm_size']])
previous_c = np.zeros([data['lstm_size']])
episode = 0
score = 0
episode_step = 0
total_max_prob = 0
lives = 5
writer = SummaryWriter('runs/{}/actor_{}'.format(data['env'][FLAGS.task], FLAGS.task))
while True:
trajectory.initialize()
actor.parameter_sync()
for _ in range(data['trajectory']):
action, behavior_policy, max_prob, h, c = actor.get_policy_and_action(
state, previous_action, previous_h, previous_c)
episode_step += 1
total_max_prob += max_prob
next_state, reward, done, info = env.step(action % data['available_action'][FLAGS.task])
score += reward
if lives != info['ale.lives']:
r = -1
d = True
else:
r = reward
d = False
trajectory.append(
state=state, next_state=next_state, reward=r,
done=d, action=action, behavior_policy=behavior_policy,
previous_action=previous_action,
initial_h=previous_h, initial_c=previous_c)
state = next_state
previous_action = action
previous_h = h
previous_c = c
lives = info['ale.lives']
if done:
print(episode, score)
writer.add_scalar('data/{}/prob'.format(data['env'][FLAGS.task]), total_max_prob / episode_step, episode)
writer.add_scalar('data/{}/score'.format(data['env'][FLAGS.task]), score, episode)
writer.add_scalar('data/{}/episode_step'.format(data['env'][FLAGS.task]), episode_step, episode)
episode += 1
score = 0
episode_step = 0
total_max_prob = 0
state = env.reset()
previous_action = 0
previous_h = np.zeros([data['lstm_size']])
previous_c = np.zeros([data['lstm_size']])
lives = 5
unrolled_data = trajectory.extract()
queue.append_to_queue(
task=FLAGS.task,
unrolled_state=unrolled_data['state'],
unrolled_next_state=unrolled_data['next_state'],
unrolled_reward=unrolled_data['reward'],
unrolled_done=unrolled_data['done'],
unrolled_behavior_policy=unrolled_data['behavior_policy'],
unrolled_action=unrolled_data['action'],
unrolled_previous_action=unrolled_data['previous_action'],
unrolled_previous_h=unrolled_data['initial_h'],
unrolled_previous_c=unrolled_data['initial_c'])
if __name__ == '__main__':
tf.app.run()