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Major reversion

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Shangtong Zhang
2017-07-30 13:24:04 -06:00
parent ce504e2d0f
commit bbfcbf76f4
7 changed files with 131 additions and 110 deletions
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agent/async_agent.py
+56 -56
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@@ -14,79 +14,79 @@ import pickle
import os
import time
def train(id, config, learning_network, target_network):
worker = config.worker(config, learning_network, target_network)
episode = 0
rewards = []
while not config.stop_signal.value:
steps, reward = worker.episode()
rewards.append(reward)
if len(rewards) > 100: rewards.pop(0)
config.logger.debug('worker %d, episode %d, return %f, avg return %f, episode steps %d, total steps %d' % (
id, episode, rewards[-1], np.mean(rewards[-100:]), steps, config.total_steps.value))
def evaluate(config, task, learning_network):
test_rewards = []
test_points = []
worker = config.worker(config, learning_network, None)
while True:
steps = config.total_steps.value
if steps % config.test_interval == 0:
worker.worker_network.load_state_dict(learning_network.state_dict())
with open('data/%s-%s-model-%s.bin' % (
config.tag, config.worker.__name__, task.name), 'wb') as f:
pickle.dump(learning_network.state_dict(), f)
rewards = np.zeros(config.test_repetitions)
for i in range(config.test_repetitions):
rewards[i] = worker.episode(deterministic=True)[1]
config.logger.info('total steps: %d, averaged return per episode: %f(%f)' % \
(steps, np.mean(rewards), np.std(rewards) / np.sqrt(config.test_repetitions)))
test_rewards.append(np.mean(rewards))
test_points.append(steps)
with open('data/%s-%s-statistics-%s.bin' % (
config.tag, config.worker.__name__, task.name), 'wb') as f:
pickle.dump([test_points, test_rewards], f)
if np.mean(rewards) > task.success_threshold:
config.stop_signal.value = True
break
class AsyncAgent:
def __init__(self, config):
self.config = config
self.config.steps_lock = mp.Lock()
self.config.network_lock = mp.Lock()
self.config.total_steps = mp.Value('i', 0)
self.config.stop_signal = mp.Value('i', False)
def run(self):
config = self.config
task = config.task_fn()
learning_network = config.network_fn()
learning_network.share_memory()
target_network = config.network_fn()
target_network.share_memory()
target_network.load_state_dict(learning_network.state_dict())
self.task = config.task_fn()
self.config.learning_network = learning_network
self.config.target_network = target_network
self.config.steps_lock = mp.Lock()
self.config.network_lock = mp.Lock()
self.config.total_steps = mp.Value('i', 0)
self.config.stop_signal = mp.Value('i', False)
def train(self, id):
worker = self.config.worker(self.config)
episode = 0
rewards = []
while not self.config.stop_signal.value:
steps, reward = worker.episode()
rewards.append(reward)
if len(rewards) > 100: rewards.pop(0)
self.config.logger.debug('worker %d, episode %d, return %f, avg return %f, episode steps %d, total steps %d' % (
id, episode, rewards[-1], np.mean(rewards[-100:]), steps, self.config.total_steps.value))
def save(self, file_name):
with open(file_name, 'wb') as f:
pickle.dump(self.config.learning_network.state_dict(), f)
def evaluate(self, id):
test_rewards = []
test_points = []
worker = self.config.worker(self.config)
while True:
steps = self.config.total_steps.value
if steps % self.config.test_interval == 0:
worker.worker_network.load_state_dict(self.config.learning_network.state_dict())
self.save('data/%s-%s-model-%s.bin' % (
self.config.tag, self.config.worker.__name__, self.task.name))
rewards = np.zeros(self.config.test_repetitions)
for i in range(self.config.test_repetitions):
rewards[i] = worker.episode(deterministic=True)[1]
self.config.logger.info('total steps: %d, averaged return per episode: %f(%f)' %\
(steps, np.mean(rewards), np.std(rewards) / np.sqrt(self.config.test_repetitions)))
test_rewards.append(np.mean(rewards))
test_points.append(steps)
with open('data/%s-%s-statistics-%s.bin' % (
self.config.tag, self.config.worker.__name__, self.task.name
), 'wb') as f:
pickle.dump([test_points, test_rewards], f)
if np.mean(rewards) > self.task.success_threshold:
self.config.stop_signal.value = True
break
def run(self):
os.environ['OMP_NUM_THREADS'] = '1'
procs = [mp.Process(target=self.train, args=(i, )) for i in range(self.config.num_workers)]
procs.append(mp.Process(target=self.evaluate, args=(self.config.num_workers, )))
args = [(i, config, learning_network, target_network) for i in range(config.num_workers)]
args.append((config, task, learning_network))
procs = [mp.Process(target=train, args=args[i]) for i in range(config.num_workers)]
procs.append(mp.Process(target=evaluate, args=args[-1]))
for p in procs: p.start()
while True:
time.sleep(1)
for i, p in enumerate(procs):
if not p.is_alive() and not self.config.stop_signal.value:
self.config.logger.warning('Worker %d exited unexpectedly.' % i)
if not p.is_alive() and not config.stop_signal.value:
config.logger.warning('Worker %d exited unexpectedly.' % i)
p.terminate()
procs[i] = mp.Process(target=self.train, args=(i, ))
if i == config.num_workers:
target = evaluate
else:
target = train
procs[i] = mp.Process(target=target, args=args[i])
procs[i].start()
self.config.logger.warning('Worker %d restarted.' % i)
break
if self.config.stop_signal.value:
if config.stop_signal.value:
break
for p in procs: p.join()