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train_apex.py
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train_apex.py
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from agent import apex
import tensorflow as tf
import numpy as np
import wrappers
import random
import utils
import json
import gym
from tensorboardX import SummaryWriter
from distributed_queue import buffer_queue
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['apex']
print(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'):
apex_queue = buffer_queue.ApexFIFOQueue(
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'])
learner = apex.Agent(
input_shape=data['model_input'],
num_action=data['model_output'],
discount_factor=data['discount_factor'],
gradient_clip_norm=data['gradient_clip_norm'],
reward_clipping=data['reward_clipping'],
start_learning_rate=data['start_learning_rate'],
end_learning_rate=data['end_learning_rate'],
learning_frame=data['learning_frame'],
model_name='learner',
learner_name='learner')
with tf.device(local_job_device):
actor = apex.Agent(
input_shape=data['model_input'],
num_action=data['model_output'],
discount_factor=data['discount_factor'],
gradient_clip_norm=data['gradient_clip_norm'],
reward_clipping=data['reward_clipping'],
start_learning_rate=data['start_learning_rate'],
end_learning_rate=data['end_learning_rate'],
learning_frame=data['learning_frame'],
model_name=f'actor_{FLAGS.task}',
learner_name='learner')
sess = tf.Session(server.target)
apex_queue.set_session(sess)
learner.set_session(sess)
if not is_learner:
actor.set_session(sess)
if is_learner:
import time
learner.target_to_main()
replay_buffer = buffer_queue.Memory(capacity=int(1e5))
buffer_step = 0
train_step = 0
writer = SummaryWriter('runs/learner')
while True:
print(f'train step : {train_step} | buffer step : {buffer_step}')
if apex_queue.get_size():
buffer_step += 1
from_actor = apex_queue.sample_batch(1)
from_actor_state = from_actor.state[0]
from_actor_next_state = from_actor.next_state[0]
from_actor_previous_action = from_actor.previous_action[0]
from_actor_action = from_actor.action[0]
from_actor_reward = from_actor.reward[0]
from_actor_done = from_actor.done[0]
td_error = learner.get_td_error(
state=from_actor_state,
next_state=from_actor_next_state,
previous_action=from_actor_previous_action,
action=from_actor_action,
reward=from_actor_reward,
done=from_actor_done)
for i in range(len(td_error)):
replay_buffer.add(
td_error[i],
[from_actor_state[i],
from_actor_next_state[i],
from_actor_previous_action[i],
from_actor_action[i],
from_actor_reward[i],
from_actor_done[i]])
if buffer_step > 10:
train_step += 1
s = time.time()
minibatch, idxs, is_weight = replay_buffer.sample(data['batch_size'])
minibatch = np.array(minibatch)
state = np.stack(minibatch[:, 0])
next_state = np.stack(minibatch[:, 1])
previous_action = np.stack(minibatch[:, 2])
action = np.stack(minibatch[:, 3])
reward = np.stack(minibatch[:, 4])
done = np.stack(minibatch[:, 5])
loss, td_error = learner.distributed_train(
state=state,
next_state=next_state,
previous_action=previous_action,
action=action,
reward=reward,
done=done,
is_weight=is_weight)
writer.add_scalar('data/loss', loss, train_step)
writer.add_scalar('data/time', time.time() - s, train_step)
if train_step % 100 == 0:
learner.target_to_main()
for i in range(len(idxs)):
replay_buffer.update(idxs[i], td_error[i])
else:
env = wrappers.make_uint8_env(data['env'][FLAGS.task])
local_buffer = buffer_queue.LocalBuffer(
capacity=int(1e4))
epsilon = 1.0
train_step = 0
episode = 0
episode_step = 0
total_max_prob = 0
state = env.reset()
previous_action = 0
score = 0
lives = 5
writer = SummaryWriter('runs/{}/actor_{}'.format(data['env'][FLAGS.task], FLAGS.task))
while True:
actor.parameter_sync()
for _ in range(data['trajectory']):
action, q_value, max_q_value = actor.get_policy_and_action(
state=state, previous_action=previous_action, epsilon=epsilon)
episode_step += 1
total_max_prob += max_q_value
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
local_buffer.append(
state=state, done=d,
reward=r, next_state=next_state,
previous_action=previous_action, action=action)
state = next_state
previous_action = action
lives = info['ale.lives']
if len(local_buffer) > 3 * data['trajectory']:
train_step += 1
sampled_data = local_buffer.sample(data['trajectory'])
apex_queue.append_to_queue(
task=FLAGS.task,
unrolled_state=sampled_data['state'],
unrolled_next_state=sampled_data['next_state'],
unrolled_previous_action=sampled_data['previous_action'],
unrolled_action=sampled_data['action'],
unrolled_reward=sampled_data['reward'],
unrolled_done=sampled_data['done'])
if done:
print(episode, score, epsilon)
writer.add_scalar('data/epsilon', epsilon, episode)
writer.add_scalar('data/episode_step', episode_step, episode)
writer.add_scalar('data/score', score, episode)
writer.add_scalar('data/total_max_prob', total_max_prob / episode_step, episode)
episode_step = 0
episode += 1
score = 0
lives = 5
epsilon = 1 / (episode * 0.05 + 1)
state = env.reset()
previous_action = 0
if __name__ == '__main__':
tf.app.run()