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.Session()
sess.__enter__()
sess.run(tf.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 save_snapshot(self, itr, paths=None):
"""Save snapshot of current batch.
Args:
itr: Index of iteration (epoch).
paths: Batch of samples after preprocessed.
"""
assert self.has_setup
logger.log("Saving snapshot...")
params = self.algo.get_itr_snapshot(itr)
params['env'] = self.env
if paths:
params['paths'] = paths
snapshotter.save_itr_params(itr, params)
logger.log('Saved')
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()
def train(self):
cur_std = self.sigma0
cur_mean = self.policy.get_param_values()
es = cma.CMAEvolutionStrategy(cur_mean, cur_std)
parallel_sampler.populate_task(self.env, self.policy)
if self.plot:
self.plotter.init_plot(self.env, self.policy)
cur_std = self.sigma0
cur_mean = self.policy.get_param_values()
itr = 0
while itr < self.n_itr and not es.stop():
if self.batch_size is None:
# Sample from multivariate normal distribution.
xs = es.ask()
xs = np.asarray(xs)
# For each sample, do a rollout.
infos = (stateful_pool.singleton_pool.run_map(
sample_return,
[(x, self.max_path_length, self.discount) for x in xs]))
else:
cum_len = 0
infos = []
xss = []
done = False
while not done:
sbs = stateful_pool.singleton_pool.n_parallel * 2
# Sample from multivariate normal distribution.
# You want to ask for sbs samples here.
xs = es.ask(sbs)
xs = np.asarray(xs)
xss.append(xs)
sinfos = stateful_pool.singleton_pool.run_map(
sample_return,
[(x, self.max_path_length, self.discount) for x in xs])
for info in sinfos:
infos.append(info)
cum_len += len(info['returns'])
if cum_len >= self.batch_size:
xs = np.concatenate(xss)
done = True
break
# Evaluate fitness of samples (negative as it is minimization
# problem).
fs = -np.array([info['returns'][0] for info in infos])
# When batching, you could have generated too many samples compared
# to the actual evaluations. So we cut it off in this case.
xs = xs[:len(fs)]
# Update CMA-ES params based on sample fitness.
es.tell(xs, fs)
logger.push_prefix('itr #{} | '.format(itr))
tabular.record('Iteration', itr)
tabular.record('CurStdMean', np.mean(cur_std))
undiscounted_returns = np.array(
[info['undiscounted_return'] for info in infos])
tabular.record('AverageReturn', np.mean(undiscounted_returns))
tabular.record('StdReturn', np.mean(undiscounted_returns))
tabular.record('MaxReturn', np.max(undiscounted_returns))
tabular.record('MinReturn', np.min(undiscounted_returns))
tabular.record('AverageDiscountedReturn', np.mean(fs))
tabular.record('AvgTrajLen',
np.mean([len(info['returns']) for info in infos]))
self.policy.log_diagnostics(infos)
snapshotter.save_itr_params(
itr, dict(
itr=itr,
policy=self.policy,
env=self.env,
))
logger.log(tabular)
if self.plot:
self.plotter.update_plot(self.policy, self.max_path_length)
logger.pop_prefix()
# Update iteration.
itr += 1
# Set final params.
self.policy.set_param_values(es.result()[0])
parallel_sampler.terminate_task()
self.plotter.close()
def train(self):
parallel_sampler.populate_task(self.env, self.policy)
if self.plot:
self.plotter.init_plot(self.env, self.policy)
cur_std = self.init_std
cur_mean = self.policy.get_param_values()
# K = cur_mean.size
n_best = max(1, int(self.n_samples * self.best_frac))
for itr in range(self.n_itr):
# sample around the current distribution
extra_var_mult = max(1.0 - itr / self.extra_decay_time, 0)
sample_std = np.sqrt(
np.square(cur_std) +
np.square(self.extra_std) * extra_var_mult)
if self.batch_size is None:
criterion = 'paths'
threshold = self.n_samples
else:
criterion = 'samples'
threshold = self.batch_size
infos = stateful_pool.singleton_pool.run_collect(
_worker_rollout_policy,
threshold=threshold,
args=(dict(
cur_mean=cur_mean,
sample_std=sample_std,
max_path_length=self.max_path_length,
discount=self.discount,
criterion=criterion,
n_evals=self.n_evals), ))
xs = np.asarray([info[0] for info in infos])
paths = [info[1] for info in infos]
fs = np.array([path['returns'][0] for path in paths])
print((xs.shape, fs.shape))
best_inds = (-fs).argsort()[:n_best]
best_xs = xs[best_inds]
cur_mean = best_xs.mean(axis=0)
cur_std = best_xs.std(axis=0)
best_x = best_xs[0]
logger.push_prefix('itr #{} | '.format(itr))
tabular.record('Iteration', itr)
tabular.record('CurStdMean', np.mean(cur_std))
undiscounted_returns = np.array(
[path['undiscounted_return'] for path in paths])
tabular.record('AverageReturn', np.mean(undiscounted_returns))
tabular.record('StdReturn', np.std(undiscounted_returns))
tabular.record('MaxReturn', np.max(undiscounted_returns))
tabular.record('MinReturn', np.min(undiscounted_returns))
tabular.record('AverageDiscountedReturn', np.mean(fs))
tabular.record('NumTrajs', len(paths))
paths = list(chain(
*[d['full_paths']
for d in paths])) # flatten paths for the case n_evals > 1
tabular.record('AvgTrajLen',
np.mean([len(path['returns']) for path in paths]))
self.policy.set_param_values(best_x)
self.policy.log_diagnostics(paths)
snapshotter.save_itr_params(
itr,
dict(
itr=itr,
policy=self.policy,
env=self.env,
cur_mean=cur_mean,
cur_std=cur_std,
))
logger.log(tabular)
logger.pop_prefix()
if self.plot:
self.plotter.update_plot(self.policy, self.max_path_length)
parallel_sampler.terminate_task()
self.plotter.close()
