def train(self):
for i in range(1, self.eff+1):
with self.sess.as_default() as sess:
logger.log("----------- Adaptation rollouts per meta-task = ", i, " -----------")
# self.sampler.rollouts_per_meta_task = 10000
self.sampler.update_batch_size(i)
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [var for var in tf.global_variables() if not sess.run(tf.is_variable_initialized(var))]
sess.run(tf.variables_initializer(uninit_vars))
self.task = self.env.sample_tasks(self.sampler.meta_batch_size, is_eval=True)
self.sampler.set_tasks(self.task)
#logger.log("\n ---------------- Iteration %d ----------------" % itr)
logger.log("Sampling set of tasks/goals for this meta-batch...")
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
paths = self.sampler.obtain_samples(log=True, log_prefix='train-')
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
samples_data = self.sample_processor.process_samples(paths, log='all', log_prefix='train-')
self.log_diagnostics(sum(paths.values(), []), prefix='train-')
#""" ------------------ Policy Update ---------------------"""
#logger.log("Optimizing policy...")
## This needs to take all samples_data so that it can construct graph for meta-optimization.
#time_optimization_step_start = time.time()
#self.algo.optimize_policy(samples_data)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('n_timesteps', self.sampler.total_timesteps_sampled)
#logger.log("Saving snapshot...")
#params = self.get_itr_snapshot(itr)
#logger.save_itr_params(itr, params)
#logger.log("Saved")
logger.dumpkvs()
# if itr == 0:
# sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode:
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
self.task = self.env.sample_tasks(self.sampler.meta_batch_size)
self.sampler.set_tasks(self.task)
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
paths = self.sampler.obtain_samples(log=True,
log_prefix='train-')
sampling_time = time.time() - time_env_sampling_start
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='train-')
proc_samples_time = time.time() - time_proc_samples_start
self.log_diagnostics(sum(paths.values(), []), prefix='train-')
""" ------------------ Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_optimization_step_start = time.time()
self.algo.optimize_policy(samples_data)
""" ------------------ Test-split Performance for logging ---------------------"""
logger.log("Testing on test-tasks split for logging...")
sampler_batch_size = self.sampler.batch_size
self.sampler.update_batch_size(3) ####################2
undiscounted_returns = []
for i in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
# Caution: Here actually i in [0] since self.meta_batch_size=100(when running on linux)
self.sampler.update_tasks(
test=True, start_from=i) # sample from test split!
#self.policy.switch_to_pre_update() # Switch to pre-update policy
logger.log("On Test: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test-')
""" ------------------- Logging Returns --------------------"""
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
self.sampler.update_batch_size(sampler_batch_size)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('test-AverageReturn', test_average_return)
logger.logkv('Time-Optimization',
time.time() - time_optimization_step_start)
logger.logkv('Time-SampleProc', np.sum(proc_samples_time))
logger.logkv('Time-Sampling', sampling_time)
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
if itr == 0:
sess.graph.finalize()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode::
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [var for var in tf.global_variables() if not sess.run(tf.is_variable_initialized(var))]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log("\n ---------------- Iteration %d ----------------" % itr)
logger.log("Sampling set of tasks/goals for this meta-batch...")
#self.sampler.update_tasks()
self.policy.switch_to_pre_update() # Switch to pre-update policy
all_samples_data, all_paths = [], []
list_sampling_time, list_inner_step_time, list_outer_step_time, list_proc_samples_time = [], [], [], []
start_total_inner_time = time.time()
for step in range(self.num_inner_grad_steps+1):
logger.log('** Step ' + str(step) + ' **')
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
paths = self.sampler.obtain_samples(log=True, log_prefix='Step_%d-' % step)
list_sampling_time.append(time.time() - time_env_sampling_start)
all_paths.append(paths)
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.sample_processor.process_samples(paths, log='all', log_prefix='Step_%d-' % step)
all_samples_data.append(samples_data)
list_proc_samples_time.append(time.time() - time_proc_samples_start)
self.log_diagnostics(sum(list(paths.values()), []), prefix='Step_%d-' % step)
""" ------------------- Inner Policy Update --------------------"""
time_inner_step_start = time.time()
if step < self.num_inner_grad_steps:
logger.log("Computing inner policy updates...")
self.algo._adapt(samples_data)
# train_writer = tf.summary.FileWriter('/home/ignasi/Desktop/meta_policy_search_graph',
# sess.graph)
list_inner_step_time.append(time.time() - time_inner_step_start)
total_inner_time = time.time() - start_total_inner_time
time_maml_opt_start = time.time()
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_outer_step_start = time.time()
self.algo.optimize_policy(all_samples_data)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps', self.sampler.total_timesteps_sampled)
#writer.add_scalar(self.algo.name, self.sample_processor.AR, self.sampler.total_timesteps_sampled)
logger.logkv('Time-OuterStep', time.time() - time_outer_step_start)
logger.logkv('Time-TotalInner', total_inner_time)
logger.logkv('Time-InnerStep', np.sum(list_inner_step_time))
logger.logkv('Time-SampleProc', np.sum(list_proc_samples_time))
logger.logkv('Time-Sampling', np.sum(list_sampling_time))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.logkv('Time-MAMLSteps', time.time() - time_maml_opt_start)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
logger.log("Training finished")
self.sess.close()
def train(self):
policy_0 = self.policy
for i in [4, 3, 2, 1]: #range(1, self.eff+1):
print("On", i, "self.policy == policy_0: ",
self.policy == policy_0)
with self.sess.as_default() as sess:
logger.log("----------- Adaptation rollouts per meta-task = ",
i, " -----------")
undiscounted_returns = []
for j in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
logger.log("---------Testing on task", j, "~",
j + self.sampler.meta_batch_size - 1,
"---------")
# initialize uninitialized vars (only initialize vars that were not loaded)
# uninit_vars = [var for var in tf.global_variables() if
# not sess.run(tf.is_variable_initialized(var))]
# sess.run(tf.variables_initializer(uninit_vars))
uninit_vars = [var for var in tf.global_variables()]
sess.run(tf.variables_initializer(uninit_vars))
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
self.sampler.update_tasks(
test=True, start_from=j) # sample from test split!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
for step in range(self.num_inner_grad_steps + 1):
if step < self.num_inner_grad_steps:
self.sampler.update_batch_size_v2(
i) ######################
logger.log("On step-0: Obtaining samples...")
else:
self.sampler.update_batch_size(2)
logger.log("On step-1: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
samples_data = self.sample_processor.process_samples(
paths, log=False
) # log='all', log_prefix='test-Step_%d-' % step
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test-Step_%d-' % step)
""" ------------------- Inner Policy Update / logging returns --------------------"""
if step < self.num_inner_grad_steps:
logger.log(
"On Test: Computing inner policy updates...")
self.algo._adapt(samples_data)
else:
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
logger.logkv('x', i)
logger.logkv('return', test_average_return)
logger.dumpkvs()
logger.log("------Testing rollouts per meta-task = ", i,
"finished------")
'''
def train(self):
"""
Trains policy on env using algo
Pseudocode::
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
n_timesteps = 0
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
gradients = []
for i in range(self.num_sapling_rounds):
logger.log("\n ----- Sampling Round %d ---" % i)
dry = i < self.num_sapling_rounds - 1
if not dry: self.sampler.update_tasks()
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
all_samples_data, all_paths = [], []
for step in range(self.num_inner_grad_steps + 1):
logger.log('** Step ' + str(step) + ' **')
logger.log("Obtaining samples...")
paths = self.sampler.obtain_samples(
log=True, log_prefix='Step_%d-' % step)
all_paths.append(paths)
logger.log("Processing samples...")
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='Step_%d-' % step)
all_samples_data.append(samples_data)
if not dry:
self.log_diagnostics(sum(list(paths.values()), []),
prefix='Step_%d-' % step)
if step < self.num_inner_grad_steps:
logger.log("Computing inner policy updates...")
self.algo._adapt(samples_data)
""" compute gradients """
gradients.append(
self.algo.compute_gradients(all_samples_data))
if not dry:
""" ------------ Compute and log gradient variance ------------"""
# compute variance of adaptation gradients
for step_id in range(self.num_inner_grad_steps):
meta_batch_size = len(gradients[0][0])
grad_std, grad_rstd = [], []
for task_id in range(meta_batch_size):
stacked_grads = np.stack([
gradients[round_id][step_id][task_id] for
round_id in range(self.num_sapling_rounds)
],
axis=1)
std = np.std(stacked_grads, axis=1)
mean = np.abs(np.mean(stacked_grads, axis=1))
grad_std.append(np.mean(std))
grad_rstd.append(np.mean(std / mean))
logger.logkv('Step_%i-GradientMean', np.mean(mean))
logger.logkv('Step_%i-GradientStd' % step_id,
np.mean(grad_std))
logger.logkv('Step_%i-GradientRStd' % step_id,
np.mean(grad_rstd))
# compute variance of meta gradients
stacked_grads = np.stack([
gradients[round_id][self.num_inner_grad_steps]
for round_id in range(self.num_sapling_rounds)
],
axis=1)
std = np.std(stacked_grads, axis=1)
mean = np.abs(np.mean(stacked_grads, axis=1))
meta_grad_std = np.mean(std)
meta_grad_rstd = np.mean(std / (mean + 1e-8))
meta_grad_rvar = np.mean(std**2 / (mean + 1e-8))
logger.logkv('Meta-GradientMean', np.mean(mean))
logger.logkv('Meta-GradientStd', meta_grad_std)
logger.logkv('Meta-GradientRStd', meta_grad_rstd)
logger.logkv('Meta-GradientRVariance', meta_grad_rvar)
# compute cosine dists
cosine_dists = cdist(np.transpose(stacked_grads),
np.transpose(
np.mean(stacked_grads,
axis=1).reshape(
(-1, 1))),
metric='cosine')
mean_abs_cos_dist = np.mean(np.abs(cosine_dists))
mean_squared_cosine_dists = np.mean(cosine_dists**2)
mean_squared_cosine_dists_sqrt = np.sqrt(
mean_squared_cosine_dists)
logger.logkv('Meta-GradientCosAbs', mean_abs_cos_dist)
logger.logkv('Meta-GradientCosVar',
mean_squared_cosine_dists)
logger.logkv('Meta-GradientCosStd',
mean_squared_cosine_dists_sqrt)
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
self.algo.optimize_policy(all_samples_data)
""" ------------------- Logging Stuff --------------------------"""
n_timesteps += (self.num_inner_grad_steps +
1) * self.sampler.total_samples
logger.logkv('n_timesteps', n_timesteps)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr) # , **kwargs)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.logkv('Itr', itr)
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.dumpkvs()
logger.log("Training finished")
self.sess.close()
def train(self):
"""
Trains policy on env using algo
Pseudocode::
for itr in n_itr:
for step in num_inner_grad_steps:
sampler.sample()
algo.compute_updated_dists()
algo.optimize_policy()
sampler.update_goals()
"""
with self.sess.as_default() as sess:
# initialize uninitialized vars (only initialize vars that were not loaded)
uninit_vars = [
var for var in tf.global_variables()
if not sess.run(tf.is_variable_initialized(var))
]
sess.run(tf.variables_initializer(uninit_vars))
start_time = time.time()
for itr in range(self.start_itr, self.n_itr):
itr_start_time = time.time()
logger.log(
"\n ---------------- Iteration %d ----------------" % itr)
logger.log(
"Sampling set of tasks/goals for this meta-batch...")
self.sampler.update_tasks() # sample tasks!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
all_samples_data, all_paths = [], []
list_sampling_time, list_inner_step_time, list_outer_step_time, list_proc_samples_time = [], [], [], []
start_total_inner_time = time.time()
for step in range(self.num_inner_grad_steps + 1):
logger.log('** Step ' + str(step) + ' **')
""" -------------------- Sampling --------------------------"""
logger.log("Obtaining samples...")
time_env_sampling_start = time.time()
'''
if step == self.num_inner_grad_steps:
temp = self.sampler.batch_size
self.sampler.update_batch_size(2)
paths = self.sampler.obtain_samples(log=True, log_prefix='Step_%d-' % step)
self.sampler.update_batch_size(temp)
else:
paths = self.sampler.obtain_samples(log=True, log_prefix='Step_%d-' % step)
'''
paths = self.sampler.obtain_samples(log=True,
log_prefix='Step_%d-' %
step)
list_sampling_time.append(time.time() -
time_env_sampling_start)
all_paths.append(paths)
""" ----------------- Processing Samples ---------------------"""
logger.log("Processing samples...")
time_proc_samples_start = time.time()
samples_data = self.sample_processor.process_samples(
paths, log='all', log_prefix='Step_%d-' % step)
all_samples_data.append(samples_data)
list_proc_samples_time.append(time.time() -
time_proc_samples_start)
self.log_diagnostics(sum(list(paths.values()), []),
prefix='Step_%d-' % step)
""" ------------------- Inner Policy Update --------------------"""
time_inner_step_start = time.time()
if step < self.num_inner_grad_steps:
logger.log("Computing inner policy updates...")
self.algo._adapt(samples_data)
# train_writer = tf.summary.FileWriter('/home/ignasi/Desktop/meta_policy_search_graph',
# sess.graph)
list_inner_step_time.append(time.time() -
time_inner_step_start)
total_inner_time = time.time() - start_total_inner_time
time_maml_opt_start = time.time()
""" ------------------ Outer Policy Update ---------------------"""
logger.log("Optimizing policy...")
# This needs to take all samples_data so that it can construct graph for meta-optimization.
time_outer_step_start = time.time()
self.algo.optimize_policy(all_samples_data)
""" ------------------ Test-split Performance for logging ---------------------"""
logger.log(
"Testing on test-tasks split for logging, rollout_per_task = 20..."
)
undiscounted_returns = []
for i in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
self.sampler.update_tasks(
test=True, start_from=i) # sample from test split!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
for step in range(self.num_inner_grad_steps + 1):
logger.log("On Test: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
samples_data = self.sample_processor.process_samples(
paths, log=False
) # log='all', log_prefix='test-Step_%d-' % step
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test20-Step_%d-' % step)
""" ------------------- Inner Policy Update / logging returns --------------------"""
if step < self.num_inner_grad_steps:
logger.log(
"On Test: Computing inner policy updates...")
self.algo._adapt(samples_data)
else:
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
logger.logkv('test20-AverageReturn', test_average_return)
logger.log(
"Testing on test-tasks split for logging, rollout_per_task = 2..."
)
sampler_batch_size = self.sampler.batch_size
self.sampler.update_batch_size(2) ##############
undiscounted_returns = []
for i in range(0, self.env.NUM_EVAL,
self.sampler.meta_batch_size):
self.sampler.update_tasks(
test=True, start_from=i) # sample from test split!
self.policy.switch_to_pre_update(
) # Switch to pre-update policy
for step in range(self.num_inner_grad_steps + 1):
logger.log("On Test: Obtaining samples...")
paths = self.sampler.obtain_samples(
log=False,
test=True) # log_prefix='test-Step_%d-' % step
logger.log("On Test: Processing Samples...")
samples_data = self.sample_processor.process_samples(
paths, log=False
) # log='all', log_prefix='test-Step_%d-' % step
self.log_diagnostics(sum(list(paths.values()), []),
prefix='test-Step_%d-' % step)
""" ------------------- Inner Policy Update / logging returns --------------------"""
if step < self.num_inner_grad_steps:
logger.log(
"On Test: Computing inner policy updates...")
self.algo._adapt(samples_data)
else:
paths = self.sample_processor.gao_paths(paths)
undiscounted_returns.extend(
[sum(path["rewards"]) for path in paths])
test_average_return = np.mean(undiscounted_returns)
self.sampler.update_batch_size(sampler_batch_size)
""" ------------------- Logging Stuff --------------------------"""
logger.logkv('Itr', itr)
logger.logkv('n_timesteps',
self.sampler.total_timesteps_sampled)
logger.logkv('test-AverageReturn', test_average_return)
logger.logkv('Time-OuterStep',
time.time() - time_outer_step_start)
logger.logkv('Time-TotalInner', total_inner_time)
logger.logkv('Time-InnerStep', np.sum(list_inner_step_time))
logger.logkv('Time-SampleProc', np.sum(list_proc_samples_time))
logger.logkv('Time-Sampling', np.sum(list_sampling_time))
logger.logkv('Time', time.time() - start_time)
logger.logkv('ItrTime', time.time() - itr_start_time)
logger.logkv('Time-MAMLSteps',
time.time() - time_maml_opt_start)
logger.log("Saving snapshot...")
params = self.get_itr_snapshot(itr)
logger.save_itr_params(itr, params)
logger.log("Saved")
logger.dumpkvs()
logger.log("Training finished")
self.sess.close()
for i in range(params['num_inner_grad_steps']):
paths = sampler.obtain_samples(log=False)
samples_data = sample_processor.process_samples(paths,
log=True,
log_prefix='%i_' %
i)
env.log_diagnostics(sum(list(paths.values()), []),
prefix='%i_' % i)
algo._adapt(samples_data)
paths = sampler.obtain_samples(log=False)
samples_data = sample_processor.process_samples(
paths, log=True, log_prefix='%i_' % params['num_inner_grad_steps'])
env.log_diagnostics(sum(list(paths.values()), []),
prefix='%i_' % params['num_inner_grad_steps'])
logger.dumpkvs()
images = []
# Postupdate:
for _ in range(args.num_trajs):
task_i = np.random.choice(range(params['meta_batch_size']))
env.set_task(tasks[task_i])
print(tasks[task_i])
obs = env.reset()
for _ in range(params['max_path_length']):
action, _ = policy.get_action(obs, task_i)
obs, reward, done, _ = env.step(action)
time.sleep(0.001)
if done:
break
