def save(self, epoch, paths=None):
"""Save snapshot of current batch.
Args:
itr(int): Index of iteration (epoch).
paths(dict): Batch of samples after preprocessed.
"""
assert self.has_setup
logger.log('Saving snapshot...')
params = dict()
# Save arguments
params['setup_args'] = self.setup_args
params['train_args'] = self.train_args
# Save states
params['env'] = self.env
params['algo'] = self.algo
if paths:
params['paths'] = paths
params['last_epoch'] = epoch
snapshotter.save_itr_params(epoch, params)
logger.log('Saved')
def train(self):
plotter = Plotter()
if self.plot:
plotter.init_plot(self.env, self.policy)
self.start_worker()
self.init_opt()
for itr in range(self.current_itr, self.n_itr):
with logger.prefix('itr #{} | '.format(itr)):
paths = self.sampler.obtain_samples(itr)
samples_data = self.sampler.process_samples(itr, paths)
self.log_diagnostics(paths)
self.optimize_policy(itr, samples_data)
logger.log('Saving snapshot...')
params = self.get_itr_snapshot(itr, samples_data)
self.current_itr = itr + 1
params['algo'] = self
if self.store_paths:
params['paths'] = samples_data['paths']
snapshotter.save_itr_params(itr, params)
logger.log('saved')
logger.log(tabular)
if self.plot:
plotter.update_plot(self.policy, self.max_path_length)
if self.pause_for_plot:
input('Plotting evaluation run: Press Enter to '
'continue...')
plotter.close()
self.shutdown_worker()
def train(self, sess=None):
address = ('localhost', 6000)
conn = Client(address)
last_average_return = None
try:
created_session = True if (sess is None) else False
if sess is None:
sess = tf.compat.v1.Session()
sess.__enter__()
sess.run(tf.compat.v1.global_variables_initializer())
conn.send(ExpLifecycle.START)
self.start_worker(sess)
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
with logger.prefix('itr #%d | ' % itr):
logger.log('Obtaining samples...')
conn.send(ExpLifecycle.OBTAIN_SAMPLES)
paths = self.obtain_samples(itr)
logger.log('Processing samples...')
conn.send(ExpLifecycle.PROCESS_SAMPLES)
samples_data = self.process_samples(itr, paths)
last_average_return = samples_data['average_return']
logger.log('Logging diagnostics...')
self.log_diagnostics(paths)
logger.log('Optimizing policy...')
conn.send(ExpLifecycle.OPTIMIZE_POLICY)
self.optimize_policy(itr, samples_data)
logger.log('Saving snapshot...')
params = self.get_itr_snapshot(itr)
if self.store_paths:
params['paths'] = samples_data['paths']
snapshotter.save_itr_params(itr, params)
logger.log('Saved')
tabular.record('Time', time.time() - start_time)
tabular.record('ItrTime', time.time() - itr_start_time)
logger.log(tabular)
if self.plot:
conn.send(ExpLifecycle.UPDATE_PLOT)
self.plotter.update_plot(self.policy,
self.max_path_length)
if self.pause_for_plot:
input('Plotting evaluation run: Press Enter to '
'continue...')
conn.send(ExpLifecycle.SHUTDOWN)
self.shutdown_worker()
if created_session:
sess.close()
finally:
conn.close()
return last_average_return
def train(self):
address = ('localhost', 6000)
conn = Client(address)
try:
plotter = Plotter()
if self.plot:
plotter.init_plot(self.env, self.policy)
conn.send(ExpLifecycle.START)
self.start_worker()
self.init_opt()
for itr in range(self.current_itr, self.n_itr):
with logger.prefix('itr #{} | '.format(itr)):
conn.send(ExpLifecycle.OBTAIN_SAMPLES)
paths = self.sampler.obtain_samples(itr)
conn.send(ExpLifecycle.PROCESS_SAMPLES)
samples_data = self.sampler.process_samples(itr, paths)
self.log_diagnostics(paths)
conn.send(ExpLifecycle.OPTIMIZE_POLICY)
self.optimize_policy(itr, samples_data)
logger.log('saving snapshot...')
params = self.get_itr_snapshot(itr, samples_data)
self.current_itr = itr + 1
params['algo'] = self
if self.store_paths:
params['paths'] = samples_data['paths']
snapshotter.save_itr_params(itr, params)
logger.log('saved')
logger.log(tabular)
if self.plot:
conn.send(ExpLifecycle.UPDATE_PLOT)
plotter.update_plot(self.policy, self.max_path_length)
if self.pause_for_plot:
input('Plotting evaluation run: Press Enter to '
'continue...')
conn.send(ExpLifecycle.SHUTDOWN)
plotter.close()
self.shutdown_worker()
finally:
conn.close()
