def testCreateAgent(self):
sac_agent.SacAgent(
self._time_step_spec,
self._action_spec,
critic_network=DummyCriticNet(),
actor_network=None,
actor_optimizer=None,
critic_optimizer=None,
alpha_optimizer=None,
actor_policy_ctor=DummyActorPolicy)
def testPolicy(self):
agent = sac_agent.SacAgent(
self._time_step_spec,
self._action_spec,
critic_network=DummyCriticNet(),
actor_network=None,
actor_optimizer=None,
critic_optimizer=None,
alpha_optimizer=None,
actor_policy_ctor=DummyActorPolicy)
observations = tf.constant([[1, 2]], dtype=tf.float32)
time_steps = ts.restart(observations)
action_step = agent.policy.action(time_steps)
self.evaluate(tf.compat.v1.global_variables_initializer())
action_ = self.evaluate(action_step.action)
self.assertLessEqual(action_, self._action_spec.maximum)
self.assertGreaterEqual(action_, self._action_spec.minimum)
def testActorLoss(self):
agent = sac_agent.SacAgent(
self._time_step_spec,
self._action_spec,
critic_network=DummyCriticNet(),
actor_network=None,
actor_optimizer=None,
critic_optimizer=None,
alpha_optimizer=None,
actor_policy_ctor=DummyActorPolicy)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_steps = ts.restart(observations, batch_size=2)
expected_loss = (2 * 10 - (2 + 1) - (4 + 1)) / 2
loss = agent.actor_loss(time_steps)
self.evaluate(tf.compat.v1.global_variables_initializer())
loss_ = self.evaluate(loss)
self.assertAllClose(loss_, expected_loss)
def testCriticLossQAug(self):
agent = sac_agent.SacAgent(
self._time_step_spec,
self._action_spec,
critic_network=DummyCriticNet(),
actor_network=None,
actor_optimizer=None,
critic_optimizer=None,
alpha_optimizer=None,
actor_policy_ctor=DummyActorPolicy)
observations = tf.constant([[1, 2], [3, 4]], dtype=tf.float32)
time_steps = ts.restart(observations, batch_size=2)
actions = tf.constant([[5], [6]], dtype=tf.float32)
rewards = tf.constant([10, 20], dtype=tf.float32)
discounts = tf.constant([0.9, 0.9], dtype=tf.float32)
next_observations = tf.constant([[5, 6], [7, 8]], dtype=tf.float32)
next_time_steps = ts.transition(next_observations, rewards, discounts)
td_targets = [7.3, 19.1]
pred_td_targets = [7., 10.]
self.evaluate(tf.compat.v1.global_variables_initializer())
# Expected critic loss has factor of 2, for the two TD3 critics.
expected_loss = self.evaluate(2 * tf.compat.v1.losses.mean_squared_error(
tf.constant(td_targets), tf.constant(pred_td_targets)))
loss = agent.critic_loss_q_aug(
time_steps,
actions,
next_time_steps,
target_obs=next_observations,
td_errors_loss_fn=tf.math.squared_difference)
self.evaluate(tf.compat.v1.global_variables_initializer())
loss_ = self.evaluate(loss)
self.assertAllClose(loss_, expected_loss)
def train_eval(
root_dir,
random_seed=None,
# Dataset params
domain_name='cartpole',
task_name='swingup',
frame_shape=(84, 84, 3),
image_aug_type='random_shifting', # None/'random_shifting'
frame_stack=3,
action_repeat=4,
# Params for learning
num_env_steps=1000000,
learn_ceb=True,
use_augmented_q=False,
# Params for CEB
e_ctor=encoders.FRNConv,
e_head_ctor=encoders.MVNormalDiagParamHead,
b_ctor=encoders.FRNConv,
b_head_ctor=encoders.MVNormalDiagParamHead,
conv_feature_dim=50, # deterministic feature used by actor/critic/ceb
ceb_feature_dim=50,
ceb_action_condition=True,
ceb_backward_encode_rewards=True,
initial_feature_step=0,
feature_lr=3e-4,
feature_lr_schedule=None,
ceb_beta=0.01,
ceb_beta_schedule=None,
ceb_generative_ratio=0.0,
ceb_generative_items=None,
feature_grad_clip=None,
enc_ema_tau=0.05, # if enc_ema_tau=None, ceb also learns backend encoder
use_critic_grad=True,
# Params for SAC
actor_kernel_init='glorot_uniform',
normal_proj_net=sac_agent.sac_normal_projection_net,
critic_kernel_init='glorot_uniform',
critic_last_kernel_init='glorot_uniform',
actor_fc_layers=(256, 256),
critic_obs_fc_layers=None,
critic_action_fc_layers=None,
critic_joint_fc_layers=(256, 256),
# Params for collect
collect_every=1,
initial_collect_steps=1000,
collect_steps_per_iteration=1,
replay_buffer_capacity=100000,
# Params for target update
target_update_tau=0.005,
target_update_period=1,
# Params for train
batch_size=256,
actor_learning_rate=3e-4,
actor_lr_schedule=None,
critic_learning_rate=3e-4,
critic_lr_schedule=None,
alpha_learning_rate=3e-4,
alpha_lr_schedule=None,
td_errors_loss_fn=tf.compat.v1.losses.mean_squared_error,
gamma=0.99,
reward_scale_factor=1.0,
gradient_clipping=None,
use_tf_functions=True,
drivers_in_graph=True,
# Params for eval
num_eval_episodes=10,
eval_env_interval=5000, # number of env steps
greedy_eval_policy=True,
train_next_frame_decoder=False,
# Params for summaries and logging
baseline_log_fn=None,
checkpoint_env_interval=100000, # number of env steps
log_env_interval=1000, # number of env steps
summary_interval=1000,
image_summary_interval=0,
summaries_flush_secs=10,
debug_summaries=False,
summarize_grads_and_vars=False,
eval_metrics_callback=None):
"""train and eval for PI-SAC."""
if random_seed is not None:
tf.compat.v1.set_random_seed(random_seed)
np.random.seed(random_seed)
# Load baseline logs and write to tensorboard
if baseline_log_fn is not None:
baseline_log_fn(root_dir, domain_name, task_name, action_repeat)
if root_dir is None:
raise AttributeError('train_eval requires a root_dir.')
# Set iterations and intervals to be computed relative to the number of
# environment steps rather than the number of gradient steps.
num_iterations = (
num_env_steps * collect_every // collect_steps_per_iteration +
(initial_feature_step))
checkpoint_interval = (checkpoint_env_interval * collect_every //
collect_steps_per_iteration)
eval_interval = (eval_env_interval * collect_every //
collect_steps_per_iteration)
log_interval = (log_env_interval * collect_every //
collect_steps_per_iteration)
logging.info('num_env_steps = %d (env steps)', num_env_steps)
logging.info('initial_feature_step = %d (gradient steps)',
initial_feature_step)
logging.info('num_iterations = %d (gradient steps)', num_iterations)
logging.info('checkpoint interval (env steps) = %d',
checkpoint_env_interval)
logging.info('checkpoint interval (gradient steps) = %d',
checkpoint_interval)
logging.info('eval interval (env steps) = %d', eval_env_interval)
logging.info('eval interval (gradient steps) = %d', eval_interval)
logging.info('log interval (env steps) = %d', log_env_interval)
logging.info('log interval (gradient steps) = %d', log_interval)
root_dir = os.path.expanduser(root_dir)
summary_writer = tf.compat.v2.summary.create_file_writer(
root_dir, flush_millis=summaries_flush_secs * 1000)
summary_writer.set_as_default()
eval_histograms = [
pisac_metric_utils.ReturnHistogram(buffer_size=num_eval_episodes),
]
eval_metrics = [
tf_metrics.AverageReturnMetric(buffer_size=num_eval_episodes),
pisac_metric_utils.ReturnStddevMetric(buffer_size=num_eval_episodes),
tf_metrics.AverageEpisodeLengthMetric(buffer_size=num_eval_episodes)
]
# create training environment
render_configs = {
'height': frame_shape[0],
'width': frame_shape[1],
'camera_id': dict(quadruped=2).get(domain_name, 0),
}
tf_env = tf_py_environment.TFPyEnvironment(
env_load_fn(domain_name, task_name, render_configs, frame_stack,
action_repeat))
eval_tf_env = tf_py_environment.TFPyEnvironment(
env_load_fn(domain_name, task_name, render_configs, frame_stack,
action_repeat))
# Define global step
g_step = common.create_variable('g_step')
# Spec
ims_shape = frame_shape[:2] + (frame_shape[2] * frame_stack, )
ims_spec = tf.TensorSpec(shape=ims_shape, dtype=tf.uint8)
conv_feature_spec = tf.TensorSpec(shape=(conv_feature_dim, ),
dtype=tf.float32)
action_spec = tf_env.action_spec()
# Forward encoder
e_enc = e_ctor(ims_spec, output_dim=conv_feature_dim, name='e')
e_enc_t = e_ctor(ims_spec, output_dim=conv_feature_dim, name='e_t')
e_enc.create_variables()
e_enc_t.create_variables()
common.soft_variables_update(e_enc.variables,
e_enc_t.variables,
tau=1.0,
tau_non_trainable=1.0)
# Forward encoder head
if e_head_ctor is None:
e_head = None
else:
stacked_action_spec = tensor_spec.BoundedTensorSpec(
action_spec.shape[:-1] + (action_spec.shape[-1] * frame_stack),
action_spec.dtype,
action_spec.minimum.tolist() * frame_stack,
action_spec.maximum.tolist() * frame_stack, action_spec.name)
e_head_spec = [conv_feature_spec, stacked_action_spec
] if ceb_action_condition else conv_feature_spec
e_head = e_head_ctor(e_head_spec,
output_dim=ceb_feature_dim,
name='e_head')
e_head.create_variables()
# Backward encoder
b_enc = b_ctor(ims_spec, output_dim=conv_feature_dim, name='b')
b_enc.create_variables()
# Backward encoder head
if b_head_ctor is None:
b_head = None
else:
stacked_reward_spec = tf.TensorSpec(shape=(frame_stack, ),
dtype=tf.float32)
b_head_spec = [conv_feature_spec, stacked_reward_spec
] if ceb_backward_encode_rewards else conv_feature_spec
b_head = b_head_ctor(b_head_spec,
output_dim=ceb_feature_dim,
name='b_head')
b_head.create_variables()
# future decoder for generative formulation
future_deconv = None
future_reward_mlp = None
y_decoders = None
if ceb_generative_ratio > 0.0:
future_deconv = utils.SimpleDeconv(conv_feature_spec,
output_tensor_spec=ims_spec)
future_deconv.create_variables()
future_reward_mlp = utils.MLP(conv_feature_spec,
hidden_dims=(ceb_feature_dim,
ceb_feature_dim // 2,
frame_stack))
future_reward_mlp.create_variables()
y_decoders = [future_deconv, future_reward_mlp]
m_vars = e_enc.trainable_variables
if enc_ema_tau is None:
m_vars += b_enc.trainable_variables
else: # do not train b_enc
common.soft_variables_update(e_enc.variables,
b_enc.variables,
tau=1.0,
tau_non_trainable=1.0)
if e_head_ctor is not None:
m_vars += e_head.trainable_variables
if b_head_ctor is not None:
m_vars += b_head.trainable_variables
if ceb_generative_ratio > 0.0:
m_vars += future_deconv.trainable_variables
m_vars += future_reward_mlp.trainable_variables
feature_lr_fn = schedule_utils.get_schedule_fn(base=feature_lr,
sched=feature_lr_schedule,
step=g_step)
m_optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=feature_lr_fn)
# CEB beta schedule, e.q. 'berp@0:1.0:1000_10000:0.3:0'
beta_fn = schedule_utils.get_schedule_fn(base=ceb_beta,
sched=ceb_beta_schedule,
step=g_step)
def img_pred_summary_fn(obs, pred):
utils.replay_summary('y0',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(
obs, None)
utils.replay_summary('y0_pred',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(
pred, None)
utils.replay_summary('y0_pred_diff',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(
((obs - pred) / 2.0 + 0.5), None)
ceb = ceb_task.CEB(beta_fn=beta_fn,
generative_ratio=ceb_generative_ratio,
generative_items=ceb_generative_items,
step_counter=g_step,
img_pred_summary_fn=img_pred_summary_fn)
m_ceb = ceb_task.CEBTask(
ceb,
e_enc,
b_enc,
forward_head=e_head,
backward_head=b_head,
y_decoders=y_decoders,
learn_backward_enc=(enc_ema_tau is None),
action_condition=ceb_action_condition,
backward_encode_rewards=ceb_backward_encode_rewards,
optimizer=m_optimizer,
grad_clip=feature_grad_clip,
global_step=g_step)
if train_next_frame_decoder:
ns_optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3)
next_frame_deconv = utils.SimpleDeconv(conv_feature_spec,
output_tensor_spec=ims_spec)
next_frame_decoder = utils.PixelDecoder(
next_frame_deconv,
optimizer=ns_optimizer,
step_counter=g_step,
image_summary_interval=image_summary_interval,
frame_stack=frame_stack)
next_frame_deconv.create_variables()
# Agent training
actor_lr_fn = schedule_utils.get_schedule_fn(base=actor_learning_rate,
sched=actor_lr_schedule,
step=g_step)
critic_lr_fn = schedule_utils.get_schedule_fn(base=critic_learning_rate,
sched=critic_lr_schedule,
step=g_step)
alpha_lr_fn = schedule_utils.get_schedule_fn(base=alpha_learning_rate,
sched=alpha_lr_schedule,
step=g_step)
actor_net = actor_distribution_network.ActorDistributionNetwork(
conv_feature_spec,
action_spec,
kernel_initializer=actor_kernel_init,
fc_layer_params=actor_fc_layers,
activation_fn=tf.keras.activations.relu,
continuous_projection_net=normal_proj_net)
critic_net = critic_network.CriticNetwork(
(conv_feature_spec, action_spec),
observation_fc_layer_params=critic_obs_fc_layers,
action_fc_layer_params=critic_action_fc_layers,
joint_fc_layer_params=critic_joint_fc_layers,
activation_fn=tf.nn.relu,
kernel_initializer=critic_kernel_init,
last_kernel_initializer=critic_last_kernel_init)
tf_agent = sac_agent.SacAgent(
ts.time_step_spec(observation_spec=conv_feature_spec),
action_spec,
actor_network=actor_net,
critic_network=critic_net,
actor_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=actor_lr_fn),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=critic_lr_fn),
alpha_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate=alpha_lr_fn),
target_update_tau=target_update_tau,
target_update_period=target_update_period,
td_errors_loss_fn=td_errors_loss_fn,
gamma=gamma,
reward_scale_factor=reward_scale_factor,
gradient_clipping=gradient_clipping,
debug_summaries=debug_summaries,
summarize_grads_and_vars=summarize_grads_and_vars,
train_step_counter=g_step)
tf_agent.initialize()
env_steps = tf_metrics.EnvironmentSteps(prefix='Train')
average_return = tf_metrics.AverageReturnMetric(
prefix='Train',
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size)
train_metrics = [
tf_metrics.NumberOfEpisodes(prefix='Train'), env_steps, average_return,
tf_metrics.AverageEpisodeLengthMetric(prefix='Train',
buffer_size=num_eval_episodes,
batch_size=tf_env.batch_size),
tf_metrics.AverageReturnMetric(name='LatestReturn',
prefix='Train',
buffer_size=1,
batch_size=tf_env.batch_size)
]
# Collect and eval policies
initial_collect_policy = random_tf_policy.RandomTFPolicy(
tf_env.time_step_spec(), action_spec)
eval_policy = tf_agent.policy
if greedy_eval_policy:
eval_policy = greedy_policy.GreedyPolicy(eval_policy)
def obs_to_feature(observation):
feature, _ = e_enc(observation['pixels'], training=False)
return tf.stop_gradient(feature)
eval_policy = FeaturePolicy(policy=eval_policy,
time_step_spec=tf_env.time_step_spec(),
obs_to_feature_fn=obs_to_feature)
collect_policy = FeaturePolicy(policy=tf_agent.collect_policy,
time_step_spec=tf_env.time_step_spec(),
obs_to_feature_fn=obs_to_feature)
# Make the replay buffer.
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=collect_policy.trajectory_spec,
batch_size=1,
max_length=replay_buffer_capacity)
replay_observer = [replay_buffer.add_batch]
# Checkpoints
train_checkpointer = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'train'),
agent=tf_agent,
actor_net=actor_net,
critic_net=critic_net,
global_step=g_step,
metrics=tfa_metric_utils.MetricsGroup(train_metrics, 'train_metrics'))
train_checkpointer.initialize_or_restore()
policy_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'policy'),
policy=eval_policy,
global_step=g_step)
policy_checkpointer.initialize_or_restore()
rb_checkpointer = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'replay_buffer'),
max_to_keep=1,
replay_buffer=replay_buffer,
global_step=g_step)
rb_checkpointer.initialize_or_restore()
if learn_ceb:
d = dict()
if future_deconv is not None:
d.update(future_deconv=future_deconv)
if future_reward_mlp is not None:
d.update(future_reward_mlp=future_reward_mlp)
model_ckpt = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'model'),
forward_encoder=e_enc,
forward_encoder_target=e_enc_t,
forward_head=e_head,
backward_encoder=b_enc,
backward_head=b_head,
global_step=g_step,
**d)
else:
model_ckpt = common.Checkpointer(ckpt_dir=os.path.join(
root_dir, 'model'),
forward_encoder=e_enc,
forward_encoder_target=e_enc_t,
global_step=g_step)
model_ckpt.initialize_or_restore()
if train_next_frame_decoder:
next_frame_decoder_ckpt = common.Checkpointer(
ckpt_dir=os.path.join(root_dir, 'next_frame_decoder'),
next_frame_decoder=next_frame_decoder,
next_frame_deconv=next_frame_deconv,
global_step=g_step)
next_frame_decoder_ckpt.initialize_or_restore()
if use_tf_functions and not drivers_in_graph:
collect_policy.action = common.function(collect_policy.action)
initial_collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
initial_collect_policy,
observers=replay_observer + train_metrics,
num_steps=initial_collect_steps)
collect_driver = dynamic_step_driver.DynamicStepDriver(
tf_env,
collect_policy,
observers=replay_observer + train_metrics,
num_steps=collect_steps_per_iteration)
if use_tf_functions and drivers_in_graph:
initial_collect_driver.run = common.function(
initial_collect_driver.run)
collect_driver.run = common.function(collect_driver.run)
# Collect initial replay data.
if env_steps.result() == 0 or replay_buffer.num_frames() == 0:
qj(initial_collect_steps,
'Initializing replay buffer by collecting random experience',
tic=1)
initial_collect_driver.run()
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=env_steps.result())
qj(s='Done initializing replay buffer', toc=1)
time_step = None
policy_state = collect_policy.get_initial_state(tf_env.batch_size)
time_acc = 0
env_steps_before = env_steps.result().numpy()
paddings = tf.constant([[4, 4], [4, 4], [0, 0]])
def random_shifting(traj, meta):
x0 = traj.observation['pixels'][0]
x1 = traj.observation['pixels'][1]
y0 = traj.observation['pixels'][frame_stack]
y1 = traj.observation['pixels'][frame_stack + 1]
x0 = tf.pad(x0, paddings, 'SYMMETRIC')
x1 = tf.pad(x1, paddings, 'SYMMETRIC')
y0 = tf.pad(y0, paddings, 'SYMMETRIC')
y1 = tf.pad(y1, paddings, 'SYMMETRIC')
x0a = tf.image.random_crop(x0, ims_shape)
x1a = tf.image.random_crop(x1, ims_shape)
x0 = tf.image.random_crop(x0, ims_shape)
x1 = tf.image.random_crop(x1, ims_shape)
y0 = tf.image.random_crop(y0, ims_shape)
y1 = tf.image.random_crop(y1, ims_shape)
return (traj, (x0, x1, x0a, x1a, y0, y1)), meta
# Dataset generates trajectories with shape [B, T, ...]
num_steps = frame_stack + 2
with tf.device('/cpu:0'):
if image_aug_type == 'random_shifting':
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=num_steps).unbatch().filter(
utils.filter_invalid_transition).map(
random_shifting,
num_parallel_calls=3).batch(batch_size).map(
utils.replay_summary(
'replay/filtered',
order_frame_stack=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval,
has_augmentations=True))
elif image_aug_type is None:
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=num_steps).unbatch().filter(
utils.filter_invalid_transition).batch(batch_size).map(
utils.replay_summary(
'replay/filtered',
order_frame_stack=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval,
has_augmentations=False))
else:
raise NotImplementedError
iterator_nstep = iter(dataset)
def model_train_step(experience):
if image_aug_type == 'random_shifting':
experience, cropped_frames = experience
x0, x1, _, _, y0, y1 = cropped_frames
r0, r1, a0, a1 = utils.split_xy(experience,
frame_stack,
rewards_n_actions_only=True)
x0 = x0[:, None, ...]
x1 = x1[:, None, ...]
y0 = y0[:, None, ...]
y1 = y1[:, None, ...]
elif image_aug_type is None:
x0, x1, y0, y1, r0, r1, a0, a1 = utils.split_xy(
experience, frame_stack, rewards_n_actions_only=False)
else:
raise NotImplementedError
# Flatten stacked actions
action_shape = a0.shape.as_list()
a0 = tf.reshape(a0, [action_shape[0], action_shape[1], -1])
a1 = tf.reshape(a1, [action_shape[0], action_shape[1], -1])
if image_summary_interval > 0:
utils.replay_summary(
'ceb/x0',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(x0, None)
utils.replay_summary(
'ceb/x1',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(x1, None)
utils.replay_summary(
'ceb/y0',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(y0, None)
utils.replay_summary(
'ceb/y1',
g_step,
reshape=True,
frame_stack=frame_stack,
image_summary_interval=image_summary_interval)(y1, None)
ceb_loss, feat_x0, zx0 = m_ceb.train(x0, a0, y0, y1, r0, r1, m_vars)
if train_next_frame_decoder:
# zx0: [B, 1, Z]
zx0 = tf.squeeze(zx0, axis=1)
# y0: [B, 1, H, W, Cxframe_stack]
next_obs = tf.cast(tf.squeeze(y0, axis=1), tf.float32) / 255.0
next_frame_decoder.train(next_obs, tf.stop_gradient(zx0))
if enc_ema_tau is not None:
common.soft_variables_update(e_enc.variables,
b_enc.variables,
tau=enc_ema_tau,
tau_non_trainable=enc_ema_tau)
def agent_train_step(experience):
# preprocess experience
if image_aug_type == 'random_shifting':
experience, cropped_frames = experience
x0, x1, x0a, x1a, y0, y1 = cropped_frames
experience = tf.nest.map_structure(
lambda t: composite.slice_to(t, axis=1, end=2), experience)
time_steps, actions, next_time_steps = (
tf_agent.experience_to_transitions(experience)) # pylint: disable=protected-access
elif image_aug_type is None:
experience = tf.nest.map_structure(
lambda t: composite.slice_to(t, axis=1, end=2), experience)
time_steps, actions, next_time_steps = (
tf_agent.experience_to_transitions(experience)) # pylint: disable=protected-access
x0 = time_steps.observation['pixels']
x1 = next_time_steps.observation['pixels']
else:
raise NotImplementedError
tf_agent.train_pix(time_steps,
actions,
next_time_steps,
x0,
x1,
x0a=x0a if use_augmented_q else None,
x1a=x1a if use_augmented_q else None,
e_enc=e_enc,
e_enc_t=e_enc_t,
q_aug=use_augmented_q,
use_critic_grad=use_critic_grad)
def checkpoint(step):
rb_checkpointer.save(global_step=step)
train_checkpointer.save(global_step=step)
policy_checkpointer.save(global_step=step)
model_ckpt.save(global_step=step)
if train_next_frame_decoder:
next_frame_decoder_ckpt.save(global_step=step)
def evaluate():
# Override outer record_if that may be out of sync with respect to the
# env_steps.result() value used for the summay step.
with tf.compat.v2.summary.record_if(True):
qj(g_step.numpy(), 'Starting eval at step', tic=1)
results = pisac_metric_utils.eager_compute(
eval_metrics,
eval_tf_env,
eval_policy,
histograms=eval_histograms,
num_episodes=num_eval_episodes,
train_step=env_steps.result(),
summary_writer=summary_writer,
summary_prefix='Eval',
use_function=drivers_in_graph,
)
if eval_metrics_callback is not None:
eval_metrics_callback(results, env_steps.result())
tfa_metric_utils.log_metrics(eval_metrics)
qj(s='Finished eval', toc=1)
def update_target():
common.soft_variables_update(
e_enc.variables,
e_enc_t.variables,
tau=tf_agent.target_update_tau,
tau_non_trainable=tf_agent.target_update_tau)
common.soft_variables_update(
tf_agent._critic_network_1.variables, # pylint: disable=protected-access
tf_agent._target_critic_network_1.variables, # pylint: disable=protected-access
tau=tf_agent.target_update_tau,
tau_non_trainable=tf_agent.target_update_tau)
common.soft_variables_update(
tf_agent._critic_network_2.variables, # pylint: disable=protected-access
tf_agent._target_critic_network_2.variables, # pylint: disable=protected-access
tau=tf_agent.target_update_tau,
tau_non_trainable=tf_agent.target_update_tau)
if use_tf_functions:
if learn_ceb:
m_ceb.train = common.function(m_ceb.train)
model_train_step = common.function(model_train_step)
agent_train_step = common.function(agent_train_step)
tf_agent.train_pix = common.function(tf_agent.train_pix)
update_target = common.function(update_target)
if train_next_frame_decoder:
next_frame_decoder.train = common.function(
next_frame_decoder.train)
if not learn_ceb and initial_feature_step > 0:
raise ValueError('Not learning CEB but initial_feature_step > 0')
with tf.summary.record_if(
lambda: tf.math.equal(g_step % summary_interval, 0)):
if learn_ceb and g_step.numpy() < initial_feature_step:
qj(initial_feature_step, 'Pretraining CEB...', tic=1)
for _ in range(g_step.numpy(), initial_feature_step):
with tf.name_scope('LearningRates'):
tf.summary.scalar(name='CEB learning rate',
data=feature_lr_fn(),
step=g_step)
experience, _ = next(iterator_nstep)
model_train_step(experience)
g_step.assign_add(1)
qj(s='Done pretraining CEB.', toc=1)
first_step = True
for _ in range(g_step.numpy(), num_iterations):
g_step_val = g_step.numpy()
start_time = time.time()
with tf.summary.record_if(
lambda: tf.math.equal(g_step % summary_interval, 0)):
with tf.name_scope('LearningRates'):
tf.summary.scalar(name='Actor learning rate',
data=actor_lr_fn(),
step=g_step)
tf.summary.scalar(name='Critic learning rate',
data=critic_lr_fn(),
step=g_step)
tf.summary.scalar(name='Alpha learning rate',
data=alpha_lr_fn(),
step=g_step)
if learn_ceb:
tf.summary.scalar(name='CEB learning rate',
data=feature_lr_fn(),
step=g_step)
with tf.name_scope('Train'):
tf.summary.scalar(name='StepsVsEnvironmentSteps',
data=env_steps.result(),
step=g_step)
tf.summary.scalar(name='StepsVsAverageReturn',
data=average_return.result(),
step=g_step)
if g_step_val % collect_every == 0:
time_step, policy_state = collect_driver.run(
time_step=time_step,
policy_state=policy_state,
)
experience, _ = next(iterator_nstep)
agent_train_step(experience)
if (g_step_val -
initial_feature_step) % tf_agent.target_update_period == 0:
update_target()
if learn_ceb:
model_train_step(experience)
time_acc += time.time() - start_time
# Increment global step counter.
g_step.assign_add(1)
g_step_val = g_step.numpy()
if (g_step_val - initial_feature_step) % log_interval == 0:
for train_metric in train_metrics:
train_metric.tf_summaries(train_step=env_steps.result())
logging.info('env steps = %d, average return = %f',
env_steps.result(), average_return.result())
env_steps_per_sec = (env_steps.result().numpy() -
env_steps_before) / time_acc
logging.info('%.3f env steps/sec', env_steps_per_sec)
tf.compat.v2.summary.scalar(name='env_steps_per_sec',
data=env_steps_per_sec,
step=env_steps.result())
time_acc = 0
env_steps_before = env_steps.result().numpy()
if (g_step_val - initial_feature_step) % eval_interval == 0:
eval_start_time = time.time()
evaluate()
logging.info('eval time %.3f sec', time.time() - eval_start_time)
if (g_step_val - initial_feature_step) % checkpoint_interval == 0:
checkpoint(g_step_val)
# Write gin config to Tensorboard
if first_step:
summ = utils.Summ(0, root_dir)
conf = gin.operative_config_str()
conf = ' ' + conf.replace('\n', '\n ')
summ.text('gin/config', conf)
summ.flush()
first_step = False
# Final checkpoint.
checkpoint(g_step.numpy())
# Final evaluation.
evaluate()
def testTrainWithRnn(self):
actor_net = actor_distribution_rnn_network.ActorDistributionRnnNetwork(
self._obs_spec,
self._action_spec,
input_fc_layer_params=None,
output_fc_layer_params=None,
conv_layer_params=None,
lstm_size=(40,),
)
critic_net = critic_rnn_network.CriticRnnNetwork(
(self._obs_spec, self._action_spec),
observation_fc_layer_params=(16,),
action_fc_layer_params=(16,),
joint_fc_layer_params=(16,),
lstm_size=(16,),
output_fc_layer_params=None,
)
counter = common.create_variable('test_train_counter')
optimizer_fn = tf.compat.v1.train.AdamOptimizer
agent = sac_agent.SacAgent(
self._time_step_spec,
self._action_spec,
critic_network=critic_net,
actor_network=actor_net,
actor_optimizer=optimizer_fn(1e-3),
critic_optimizer=optimizer_fn(1e-3),
alpha_optimizer=optimizer_fn(1e-3),
train_sequence_length=None, # for RNN
auto_step=True, # enable auto step
train_step_counter=counter,
)
batch_size = 5
observations = tf.constant(
[[[1, 2], [3, 4], [5, 6]]] * batch_size, dtype=tf.float32)
actions = tf.constant([[[0], [1], [1]]] * batch_size, dtype=tf.float32)
time_steps = ts.TimeStep(
step_type=tf.constant([[1] * 3] * batch_size, dtype=tf.int32),
reward=tf.constant([[1] * 3] * batch_size, dtype=tf.float32),
discount=tf.constant([[1] * 3] * batch_size, dtype=tf.float32),
observation=observations)
experience = trajectory.Trajectory(
time_steps.step_type, observations, actions, (),
time_steps.step_type, time_steps.reward, time_steps.discount)
# Force variable creation.
agent.policy.variables()
if not tf.executing_eagerly():
# Get experience first to make sure optimizer variables are created and
# can be initialized.
experience = agent.train(experience)
with self.cached_session() as sess:
common.initialize_uninitialized_variables(sess)
self.assertEqual(self.evaluate(counter), 0)
self.evaluate(experience)
self.assertEqual(self.evaluate(counter), 1)
else:
self.assertEqual(self.evaluate(counter), 0)
self.evaluate(agent.train(experience))
self.assertEqual(self.evaluate(counter), 1)
