class TestReplayBuffer(unittest.TestCase):
"""Tests for the ReplayBuffer object."""
def setUp(self):
self.replay_buffer = ReplayBuffer(
buffer_size=2, batch_size=1, obs_dim=1, ac_dim=1)
def tearDown(self):
del self.replay_buffer
def test_buffer_size(self):
"""Validate the buffer_size output from the replay buffer."""
self.assertEqual(self.replay_buffer.buffer_size, 2)
def test_add_sample(self):
"""Test the `add` and `sample` methods the replay buffer."""
# Add an element.
self.replay_buffer.add(
obs_t=np.array([0]),
action=np.array([1]),
reward=2,
obs_tp1=np.array([3]),
done=False
)
# Check is_full in the False case.
self.assertEqual(self.replay_buffer.is_full(), False)
# Add an element.
self.replay_buffer.add(
obs_t=np.array([0]),
action=np.array([1]),
reward=2,
obs_tp1=np.array([3]),
done=False
)
# Check is_full in the True case.
self.assertEqual(self.replay_buffer.is_full(), True)
# Check can_sample in the True case.
self.assertEqual(self.replay_buffer.can_sample(), True)
# Test the `sample` method.
obs_t, actions_t, rewards, obs_tp1, done = self.replay_buffer.sample()
np.testing.assert_array_almost_equal(obs_t, [[0]])
np.testing.assert_array_almost_equal(actions_t, [[1]])
np.testing.assert_array_almost_equal(rewards, [2])
np.testing.assert_array_almost_equal(obs_tp1, [[3]])
np.testing.assert_array_almost_equal(done, [False])
def setUp(self):
self.replay_buffer = ReplayBuffer(buffer_size=2,
batch_size=1,
obs_dim=1,
ac_dim=1)
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
actor_lr,
critic_lr,
verbose,
tau,
gamma,
layer_norm,
layers,
act_fun,
use_huber,
noise,
target_policy_noise,
target_noise_clip,
scope=None,
zero_fingerprint=False,
fingerprint_dim=2):
"""Instantiate the feed-forward neural network policy.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
tau : float
target update rate
gamma : float
discount factor
layer_norm : bool
enable layer normalisation
layers : list of int or None
the size of the Neural network for the policy
act_fun : tf.nn.*
the activation function to use in the neural network
use_huber : bool
specifies whether to use the huber distance function as the loss
for the critic. If set to False, the mean-squared error metric is
used instead
noise : float
scaling term to the range of the action space, that is subsequently
used as the standard deviation of Gaussian noise added to the
action if `apply_noise` is set to True in `get_action`
target_policy_noise : float
standard deviation term to the noise from the output of the target
actor policy. See TD3 paper for more.
target_noise_clip : float
clipping term for the noise injected in the target actor policy
scope : str
an upper-level scope term. Used by policies that call this one.
zero_fingerprint : bool
whether to zero the last two elements of the observations for the
actor and critic computations. Used for the worker policy when
fingerprints are being implemented.
fingerprint_dim : int
the number of fingerprint elements in the observation. Used when
trying to zero the fingerprint elements.
Raises
------
AssertionError
if the layers is not a list of at least size 1
"""
super(FeedForwardPolicy, self).__init__(sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
actor_lr=actor_lr,
critic_lr=critic_lr,
verbose=verbose,
tau=tau,
gamma=gamma,
layer_norm=layer_norm,
layers=layers,
act_fun=act_fun,
use_huber=use_huber)
# action magnitudes
ac_mag = 0.5 * (ac_space.high - ac_space.low)
self.noise = noise * ac_mag
self.target_policy_noise = np.array([ac_mag * target_policy_noise])
self.target_noise_clip = np.array([ac_mag * target_noise_clip])
self.zero_fingerprint = zero_fingerprint
self.fingerprint_dim = fingerprint_dim
assert len(self.layers) >= 1, \
"Error: must have at least one hidden layer for the policy."
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.terminals1 = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rew_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
self.obs1_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs1')
# logging of rewards to tensorboard
with tf.compat.v1.variable_scope("input_info", reuse=False):
tf.compat.v1.summary.scalar('rewards', tf.reduce_mean(self.rew_ph))
# =================================================================== #
# Step 3: Create actor and critic variables. #
# =================================================================== #
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
self.actor_tf = self.make_actor(self.obs_ph)
self.critic_tf = [
self.make_critic(self.obs_ph,
self.action_ph,
scope="qf_{}".format(i)) for i in range(2)
]
self.critic_with_actor_tf = [
self.make_critic(self.obs_ph,
self.actor_tf,
reuse=True,
scope="qf_{}".format(i)) for i in range(2)
]
with tf.compat.v1.variable_scope("target", reuse=False):
# create the target actor policy
actor_target = self.make_actor(self.obs1_ph)
# smooth target policy by adding clipped noise to target actions
target_noise = tf.random.normal(tf.shape(actor_target),
stddev=self.target_policy_noise)
target_noise = tf.clip_by_value(target_noise,
-self.target_noise_clip,
self.target_noise_clip)
# clip the noisy action to remain in the bounds
noisy_actor_target = tf.clip_by_value(actor_target + target_noise,
self.ac_space.low,
self.ac_space.high)
# create the target critic policies
critic_target = [
self.make_critic(self.obs1_ph,
noisy_actor_target,
scope="qf_{}".format(i)) for i in range(2)
]
# Create the target update operations.
init, soft = self._setup_target_updates('model', 'target', scope, tau,
verbose)
self.target_init_updates = init
self.target_soft_updates = soft
# =================================================================== #
# Step 4: Setup the optimizers for the actor and critic. #
# =================================================================== #
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
self._setup_actor_optimizer(scope)
self._setup_critic_optimizer(critic_target, scope)
tf.compat.v1.summary.scalar('actor_loss', self.actor_loss)
tf.compat.v1.summary.scalar('Q1_loss', self.critic_loss[0])
tf.compat.v1.summary.scalar('Q2_loss', self.critic_loss[1])
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
class FeedForwardPolicy(ActorCriticPolicy):
"""Feed-forward neural network actor-critic policy.
Attributes
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow debug
layers : list of int
the size of the Neural network for the policy
tau : float
target update rate
gamma : float
discount factor
layer_norm : bool
enable layer normalisation
act_fun : tf.nn.*
the activation function to use in the neural network
use_huber : bool
specifies whether to use the huber distance function as the loss for
the critic. If set to False, the mean-squared error metric is used
instead
noise : float
scaling term to the range of the action space, that is subsequently
used as the standard deviation of Gaussian noise added to the action if
`apply_noise` is set to True in `get_action`
target_policy_noise : float
standard deviation term to the noise from the output of the target
actor policy. See TD3 paper for more.
target_noise_clip : float
clipping term for the noise injected in the target actor policy
zero_fingerprint : bool
whether to zero the last two elements of the observations for the actor
and critic computations. Used for the worker policy when fingerprints
are being implemented.
fingerprint_dim : int
the number of fingerprint elements in the observation. Used when trying
to zero the fingerprint elements.
replay_buffer : hbaselines.fcnet.replay_buffer.ReplayBuffer
the replay buffer
terminals1 : tf.compat.v1.placeholder
placeholder for the next step terminals
rew_ph : tf.compat.v1.placeholder
placeholder for the rewards
action_ph : tf.compat.v1.placeholder
placeholder for the actions
obs_ph : tf.compat.v1.placeholder
placeholder for the observations
obs1_ph : tf.compat.v1.placeholder
placeholder for the next step observations
actor_tf : tf.Variable
the output from the actor network
critic_tf : list of tf.Variable
the output from the critic networks. Two networks are used to stabilize
training.
critic_with_actor_tf : list of tf.Variable
the output from the critic networks with the action provided directly
by the actor policy
target_init_updates : tf.Operation
an operation that sets the values of the trainable parameters of the
target actor/critic to match those actual actor/critic
target_soft_updates : tf.Operation
soft target update function
actor_loss : tf.Operation
the operation that returns the loss of the actor
actor_optimizer : tf.Operation
the operation that updates the trainable parameters of the actor
critic_loss : tf.Operation
the operation that returns the loss of the critic
critic_optimizer : tf.Operation
the operation that updates the trainable parameters of the critic
"""
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
actor_lr,
critic_lr,
verbose,
tau,
gamma,
layer_norm,
layers,
act_fun,
use_huber,
noise,
target_policy_noise,
target_noise_clip,
scope=None,
zero_fingerprint=False,
fingerprint_dim=2):
"""Instantiate the feed-forward neural network policy.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
tau : float
target update rate
gamma : float
discount factor
layer_norm : bool
enable layer normalisation
layers : list of int or None
the size of the Neural network for the policy
act_fun : tf.nn.*
the activation function to use in the neural network
use_huber : bool
specifies whether to use the huber distance function as the loss
for the critic. If set to False, the mean-squared error metric is
used instead
noise : float
scaling term to the range of the action space, that is subsequently
used as the standard deviation of Gaussian noise added to the
action if `apply_noise` is set to True in `get_action`
target_policy_noise : float
standard deviation term to the noise from the output of the target
actor policy. See TD3 paper for more.
target_noise_clip : float
clipping term for the noise injected in the target actor policy
scope : str
an upper-level scope term. Used by policies that call this one.
zero_fingerprint : bool
whether to zero the last two elements of the observations for the
actor and critic computations. Used for the worker policy when
fingerprints are being implemented.
fingerprint_dim : int
the number of fingerprint elements in the observation. Used when
trying to zero the fingerprint elements.
Raises
------
AssertionError
if the layers is not a list of at least size 1
"""
super(FeedForwardPolicy, self).__init__(sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
actor_lr=actor_lr,
critic_lr=critic_lr,
verbose=verbose,
tau=tau,
gamma=gamma,
layer_norm=layer_norm,
layers=layers,
act_fun=act_fun,
use_huber=use_huber)
# action magnitudes
ac_mag = 0.5 * (ac_space.high - ac_space.low)
self.noise = noise * ac_mag
self.target_policy_noise = np.array([ac_mag * target_policy_noise])
self.target_noise_clip = np.array([ac_mag * target_noise_clip])
self.zero_fingerprint = zero_fingerprint
self.fingerprint_dim = fingerprint_dim
assert len(self.layers) >= 1, \
"Error: must have at least one hidden layer for the policy."
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.terminals1 = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rew_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
self.obs1_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs1')
# logging of rewards to tensorboard
with tf.compat.v1.variable_scope("input_info", reuse=False):
tf.compat.v1.summary.scalar('rewards', tf.reduce_mean(self.rew_ph))
# =================================================================== #
# Step 3: Create actor and critic variables. #
# =================================================================== #
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
self.actor_tf = self.make_actor(self.obs_ph)
self.critic_tf = [
self.make_critic(self.obs_ph,
self.action_ph,
scope="qf_{}".format(i)) for i in range(2)
]
self.critic_with_actor_tf = [
self.make_critic(self.obs_ph,
self.actor_tf,
reuse=True,
scope="qf_{}".format(i)) for i in range(2)
]
with tf.compat.v1.variable_scope("target", reuse=False):
# create the target actor policy
actor_target = self.make_actor(self.obs1_ph)
# smooth target policy by adding clipped noise to target actions
target_noise = tf.random.normal(tf.shape(actor_target),
stddev=self.target_policy_noise)
target_noise = tf.clip_by_value(target_noise,
-self.target_noise_clip,
self.target_noise_clip)
# clip the noisy action to remain in the bounds
noisy_actor_target = tf.clip_by_value(actor_target + target_noise,
self.ac_space.low,
self.ac_space.high)
# create the target critic policies
critic_target = [
self.make_critic(self.obs1_ph,
noisy_actor_target,
scope="qf_{}".format(i)) for i in range(2)
]
# Create the target update operations.
init, soft = self._setup_target_updates('model', 'target', scope, tau,
verbose)
self.target_init_updates = init
self.target_soft_updates = soft
# =================================================================== #
# Step 4: Setup the optimizers for the actor and critic. #
# =================================================================== #
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
self._setup_actor_optimizer(scope)
self._setup_critic_optimizer(critic_target, scope)
tf.compat.v1.summary.scalar('actor_loss', self.actor_loss)
tf.compat.v1.summary.scalar('Q1_loss', self.critic_loss[0])
tf.compat.v1.summary.scalar('Q2_loss', self.critic_loss[1])
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
def _setup_actor_optimizer(self, scope):
"""Create the actor loss, gradient, and optimizer."""
if self.verbose >= 2:
print('setting up actor optimizer')
scope_name = 'model/pi/'
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
actor_shapes = [
var.get_shape().as_list()
for var in get_trainable_vars(scope_name)
]
actor_nb_params = sum(
[reduce(lambda x, y: x * y, shape) for shape in actor_shapes])
print(' actor shapes: {}'.format(actor_shapes))
print(' actor params: {}'.format(actor_nb_params))
# compute the actor loss
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf[0])
# create an optimizer object
optimizer = tf.compat.v1.train.AdamOptimizer(self.actor_lr)
self.actor_optimizer = optimizer.minimize(
self.actor_loss, var_list=get_trainable_vars(scope_name))
def _setup_critic_optimizer(self, critic_target, scope):
"""Create the critic loss, gradient, and optimizer."""
if self.verbose >= 2:
print('setting up critic optimizer')
# compute the target critic term
with tf.compat.v1.variable_scope("loss", reuse=False):
q_obs1 = tf.minimum(critic_target[0], critic_target[1])
target_q = tf.stop_gradient(self.rew_ph + (1. - self.terminals1) *
self.gamma * q_obs1)
tf.compat.v1.summary.scalar('critic_target',
tf.reduce_mean(target_q))
# choose the loss function
if self.use_huber:
loss_fn = tf.compat.v1.losses.huber_loss
else:
loss_fn = tf.compat.v1.losses.mean_squared_error
self.critic_loss = [loss_fn(q, target_q) for q in self.critic_tf]
self.critic_optimizer = []
for i, critic_loss in enumerate(self.critic_loss):
scope_name = 'model/qf_{}/'.format(i)
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
critic_shapes = [
var.get_shape().as_list()
for var in get_trainable_vars(scope_name)
]
critic_nb_params = sum([
reduce(lambda x, y: x * y, shape)
for shape in critic_shapes
])
print(' critic shapes: {}'.format(critic_shapes))
print(' critic params: {}'.format(critic_nb_params))
# create an optimizer object
optimizer = tf.compat.v1.train.AdamOptimizer(self.critic_lr)
# create the optimizer object
self.critic_optimizer.append(
optimizer.minimize(loss=critic_loss,
var_list=get_trainable_vars(scope_name)))
def make_actor(self, obs, reuse=False, scope="pi"):
"""Create an actor tensor.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the actor
Returns
-------
tf.Variable
the output from the actor
"""
with tf.compat.v1.variable_scope(scope, reuse=reuse):
pi_h = obs
# zero out the fingerprint observations for the worker policy
if self.zero_fingerprint:
pi_h = self._remove_fingerprint(pi_h, self.ob_space.shape[0],
self.fingerprint_dim,
self.co_space.shape[0])
# create the hidden layers
for i, layer_size in enumerate(self.layers):
pi_h = self._layer(pi_h,
layer_size,
'fc{}'.format(i),
act_fun=self.act_fun,
layer_norm=self.layer_norm)
# create the output layer
policy = self._layer(
pi_h,
self.ac_space.shape[0],
'output',
act_fun=tf.nn.tanh,
kernel_initializer=tf.random_uniform_initializer(minval=-3e-3,
maxval=3e-3))
# scaling terms to the output from the policy
ac_means = (self.ac_space.high + self.ac_space.low) / 2.
ac_magnitudes = (self.ac_space.high - self.ac_space.low) / 2.
policy = ac_means + ac_magnitudes * tf.to_float(policy)
return policy
def make_critic(self, obs, action, reuse=False, scope="qf"):
"""Create a critic tensor.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
action : tf.compat.v1.placeholder
the input action placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the actor
Returns
-------
tf.Variable
the output from the critic
"""
with tf.compat.v1.variable_scope(scope, reuse=reuse):
# concatenate the observations and actions
qf_h = tf.concat([obs, action], axis=-1)
# zero out the fingerprint observations for the worker policy
if self.zero_fingerprint:
qf_h = self._remove_fingerprint(
qf_h, self.ob_space.shape[0], self.fingerprint_dim,
self.co_space.shape[0] + self.ac_space.shape[0])
# create the hidden layers
for i, layer_size in enumerate(self.layers):
qf_h = self._layer(qf_h,
layer_size,
'fc{}'.format(i),
act_fun=self.act_fun,
layer_norm=self.layer_norm)
# create the output layer
qvalue_fn = self._layer(
qf_h,
1,
'qf_output',
kernel_initializer=tf.random_uniform_initializer(minval=-3e-3,
maxval=3e-3))
return qvalue_fn
def update(self, update_actor=True, **kwargs):
"""Perform a gradient update step.
**Note**; The target update soft updates occur at the same frequency as
the actor update frequencies.
Parameters
----------
update_actor : bool
specifies whether to update the actor policy. The critic policy is
still updated if this value is set to False.
Returns
-------
[float, float]
Q1 loss, Q2 loss
float
actor loss
"""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return [0, 0], 0
# Get a batch
obs0, actions, rewards, obs1, terminals1 = self.replay_buffer.sample()
return self.update_from_batch(obs0,
actions,
rewards,
obs1,
terminals1,
update_actor=update_actor)
def update_from_batch(self,
obs0,
actions,
rewards,
obs1,
terminals1,
update_actor=True):
"""Perform gradient update step given a batch of data.
Parameters
----------
obs0 : np.ndarray
batch of observations
actions : numpy float
batch of actions executed given obs_batch
rewards : numpy float
rewards received as results of executing act_batch
obs1 : np.ndarray
next set of observations seen after executing act_batch
terminals1 : numpy bool
done_mask[i] = 1 if executing act_batch[i] resulted in the end of
an episode and 0 otherwise.
update_actor : bool, optional
specified whether to perform gradient update procedures to the
actor policy. Default set to True. Note that the update procedure
for the critic is always performed when calling this method.
Returns
-------
[float, float]
Q1 loss, Q2 loss
float
actor loss
"""
# Reshape to match previous behavior and placeholder shape.
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
# Update operations for the critic networks.
step_ops = [
self.critic_loss, self.critic_optimizer[0],
self.critic_optimizer[1]
]
if update_actor:
# Actor updates and target soft update operation.
step_ops += [
self.actor_loss, self.actor_optimizer, self.target_soft_updates
]
# Perform the update operations and collect the critic loss.
critic_loss, *_vals = self.sess.run(step_ops,
feed_dict={
self.obs_ph: obs0,
self.action_ph: actions,
self.rew_ph: rewards,
self.obs1_ph: obs1,
self.terminals1: terminals1
})
# Extract the actor loss.
actor_loss = _vals[2] if update_actor else 0
return critic_loss, actor_loss
def get_action(self, obs, context, apply_noise, random_actions):
"""See parent class."""
# Add the contextual observation, if applicable.
obs = self._get_obs(obs, context, axis=1)
if random_actions:
action = np.array([self.ac_space.sample()])
else:
action = self.sess.run(self.actor_tf, {self.obs_ph: obs})
if apply_noise:
# compute noisy action
if apply_noise:
action += np.random.normal(0, self.noise, action.shape)
# clip by bounds
action = np.clip(action, self.ac_space.low, self.ac_space.high)
return action
def value(self, obs, context, action):
"""See parent class."""
# Add the contextual observation, if applicable.
obs = self._get_obs(obs, context, axis=1)
return self.sess.run(self.critic_tf,
feed_dict={
self.obs_ph: obs,
self.action_ph: action
})
def store_transition(self,
obs0,
context0,
action,
reward,
obs1,
context1,
done,
is_final_step,
evaluate=False):
"""See parent class."""
if not evaluate:
# Add the contextual observation, if applicable.
obs0 = self._get_obs(obs0, context0, axis=0)
obs1 = self._get_obs(obs1, context1, axis=0)
# Modify the done mask in accordance with the TD3 algorithm. Done
# masks that correspond to the final step are set to False.
done = done and not is_final_step
self.replay_buffer.add(obs0, action, reward, obs1, float(done))
def initialize(self):
"""See parent class.
This method syncs the actor and critic optimizers across CPUs, and
initializes the target parameters to match the model parameters.
"""
self.sess.run(self.target_init_updates)
def _setup_stats(self, base="Model"):
"""Create the running means and std of the model inputs and outputs.
This method also adds the same running means and stds as scalars to
tensorboard for additional storage.
"""
ops = []
names = []
ops += [tf.reduce_mean(self.critic_tf[0])]
names += ['{}/reference_Q1_mean'.format(base)]
ops += [reduce_std(self.critic_tf[0])]
names += ['{}/reference_Q1_std'.format(base)]
ops += [tf.reduce_mean(self.critic_tf[1])]
names += ['{}/reference_Q2_mean'.format(base)]
ops += [reduce_std(self.critic_tf[1])]
names += ['{}/reference_Q2_std'.format(base)]
ops += [tf.reduce_mean(self.critic_with_actor_tf[0])]
names += ['{}/reference_actor_Q1_mean'.format(base)]
ops += [reduce_std(self.critic_with_actor_tf[0])]
names += ['{}/reference_actor_Q1_std'.format(base)]
ops += [tf.reduce_mean(self.critic_with_actor_tf[1])]
names += ['{}/reference_actor_Q2_mean'.format(base)]
ops += [reduce_std(self.critic_with_actor_tf[1])]
names += ['{}/reference_actor_Q2_std'.format(base)]
ops += [tf.reduce_mean(self.actor_tf)]
names += ['{}/reference_action_mean'.format(base)]
ops += [reduce_std(self.actor_tf)]
names += ['{}/reference_action_std'.format(base)]
# Add all names and ops to the tensorboard summary.
for op, name in zip(ops, names):
tf.compat.v1.summary.scalar(name, op)
return ops, names
def get_td_map(self):
"""See parent class."""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return {}
# Get a batch.
obs0, actions, rewards, obs1, done1 = self.replay_buffer.sample()
return self.get_td_map_from_batch(obs0, actions, rewards, obs1, done1)
def get_td_map_from_batch(self, obs0, actions, rewards, obs1, terminals1):
"""Convert a batch to a td_map."""
# Reshape to match previous behavior and placeholder shape.
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
td_map = {
self.obs_ph: obs0,
self.action_ph: actions,
self.rew_ph: rewards,
self.obs1_ph: obs1,
self.terminals1: terminals1
}
return td_map
class FeedForwardPolicy(ActorCriticPolicy):
"""SAC-compatible feedforward policy.
Attributes
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow debug
tau : float
target update rate
gamma : float
discount factor
use_huber : bool
specifies whether to use the huber distance function as the loss for
the critic. If set to False, the mean-squared error metric is used
instead
model_params : dict
dictionary of model-specific parameters. See parent class.
target_entropy : float
target entropy used when learning the entropy coefficient
replay_buffer : hbaselines.fcnet.replay_buffer.ReplayBuffer
the replay buffer
terminals1 : tf.compat.v1.placeholder
placeholder for the next step terminals
rew_ph : tf.compat.v1.placeholder
placeholder for the rewards
action_ph : tf.compat.v1.placeholder
placeholder for the actions
obs_ph : tf.compat.v1.placeholder
placeholder for the observations
obs1_ph : tf.compat.v1.placeholder
placeholder for the next step observations
deterministic_action : tf.Variable
the output from the deterministic actor
policy_out : tf.Variable
the output from the stochastic actor
logp_pi : tf.Variable
the log-probability of a given observation given the output action from
the policy
logp_action : tf.Variable
the log-probability of a given observation given a fixed action. Used
by the hierarchical policy to perform off-policy corrections.
qf1 : tf.Variable
the output from the first Q-function
qf2 : tf.Variable
the output from the second Q-function
value_fn : tf.Variable
the output from the value function
qf1_pi : tf.Variable
the output from the first Q-function with the action provided directly
by the actor policy
qf2_pi : tf.Variable
the output from the second Q-function with the action provided directly
by the actor policy
log_alpha : tf.Variable
the log of the entropy coefficient
alpha : tf.Variable
the entropy coefficient
value_target : tf.Variable
the output from the target value function. Takes as input the next-step
observations
target_init_updates : tf.Operation
an operation that sets the values of the trainable parameters of the
target actor/critic to match those actual actor/critic
target_soft_updates : tf.Operation
soft target update function
alpha_loss : tf.Operation
the operation that returns the loss of the entropy term
alpha_optimizer : tf.Operation
the operation that updates the trainable parameters of the entropy term
actor_loss : tf.Operation
the operation that returns the loss of the actor
actor_optimizer : tf.Operation
the operation that updates the trainable parameters of the actor
critic_loss : tf.Operation
the operation that returns the loss of the critic
critic_optimizer : tf.Operation
the operation that updates the trainable parameters of the critic
"""
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
actor_lr,
critic_lr,
verbose,
tau,
gamma,
use_huber,
l2_penalty,
model_params,
target_entropy,
scope=None,
num_envs=1):
"""Instantiate the feed-forward neural network policy.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
tau : float
target update rate
gamma : float
discount factor
use_huber : bool
specifies whether to use the huber distance function as the loss
for the critic. If set to False, the mean-squared error metric is
used instead
l2_penalty : float
L2 regularization penalty. This is applied to the policy network.
model_params : dict
dictionary of model-specific parameters. See parent class.
target_entropy : float
target entropy used when learning the entropy coefficient. If set
to None, a heuristic value is used.
scope : str
an upper-level scope term. Used by policies that call this one.
"""
super(FeedForwardPolicy, self).__init__(
sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
actor_lr=actor_lr,
critic_lr=critic_lr,
verbose=verbose,
tau=tau,
gamma=gamma,
use_huber=use_huber,
l2_penalty=l2_penalty,
model_params=model_params,
num_envs=num_envs,
)
if target_entropy is None:
self.target_entropy = -np.prod(self.ac_space.shape)
else:
self.target_entropy = target_entropy
self._ac_means = 0.5 * (ac_space.high + ac_space.low)
self._ac_magnitudes = 0.5 * (ac_space.high - ac_space.low)
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.terminals1 = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rew_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
self.obs1_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs1')
# =================================================================== #
# Step 3: Create actor and critic variables. #
# =================================================================== #
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
self.deterministic_action, self.policy_out, self.logp_pi, \
self.logp_action = self.make_actor(self.obs_ph, self.action_ph)
self.qf1, self.qf2, self.value_fn = self.make_critic(
self.obs_ph, self.action_ph, create_qf=True, create_vf=True)
self.qf1_pi, self.qf2_pi, _ = self.make_critic(self.obs_ph,
self.policy_out,
create_qf=True,
create_vf=False,
reuse=True)
# The entropy coefficient or entropy can be learned automatically,
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
self.log_alpha = tf.compat.v1.get_variable('log_alpha',
dtype=tf.float32,
initializer=0.0)
self.alpha = tf.exp(self.log_alpha)
with tf.compat.v1.variable_scope("target", reuse=False):
# Create the value network
_, _, value_target = self.make_critic(self.obs1_ph,
create_qf=False,
create_vf=True)
self.value_target = value_target
# Create the target update operations.
init, soft = self._setup_target_updates('model/value_fns/vf',
'target/value_fns/vf', scope,
tau, verbose)
self.target_init_updates = init
self.target_soft_updates = soft
# =================================================================== #
# Step 4: Setup the optimizers for the actor and critic. #
# =================================================================== #
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
self._setup_actor_optimizer(scope)
self._setup_critic_optimizer(scope)
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
def make_actor(self, obs, action, reuse=False, scope="pi"):
"""Create the actor variables.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
action : tf.compat.v1.placeholder
the input action placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the actor
Returns
-------
tf.Variable
the output from the deterministic actor
tf.Variable
the output from the stochastic actor
tf.Variable
the log-probability of a given observation given the output action
from the policy
tf.Variable
the log-probability of a given observation given a fixed action
"""
# Initial image pre-processing (for convolutional policies).
if self.model_params["model_type"] == "conv":
pi_h = create_conv(
obs=obs,
image_height=self.model_params["image_height"],
image_width=self.model_params["image_width"],
image_channels=self.model_params["image_channels"],
ignore_flat_channels=self.model_params["ignore_flat_channels"],
ignore_image=self.model_params["ignore_image"],
filters=self.model_params["filters"],
kernel_sizes=self.model_params["kernel_sizes"],
strides=self.model_params["strides"],
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
scope=scope,
reuse=reuse,
)
else:
pi_h = obs
# Create the model.
policy_mean, log_std = create_fcnet(
obs=pi_h,
layers=self.model_params["layers"],
num_output=self.ac_space.shape[0],
stochastic=True,
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
scope=scope,
reuse=reuse,
)
# OpenAI Variation to cap the standard deviation
log_std = tf.clip_by_value(log_std, LOG_STD_MIN, LOG_STD_MAX)
std = tf.exp(log_std)
# Reparameterization trick
policy = policy_mean + tf.random.normal(tf.shape(policy_mean)) * std
logp_pi = gaussian_likelihood(policy, policy_mean, log_std)
logp_ac = gaussian_likelihood(action, policy_mean, log_std)
# Apply squashing and account for it in the probability
_, _, logp_ac = apply_squashing_func(policy_mean, action, logp_ac)
deterministic_policy, policy, logp_pi = apply_squashing_func(
policy_mean, policy, logp_pi)
return deterministic_policy, policy, logp_pi, logp_ac
def make_critic(self,
obs,
action=None,
reuse=False,
scope="value_fns",
create_qf=True,
create_vf=True):
"""Create the critic variables.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
action : tf.compat.v1.placeholder
the input action placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the actor
create_qf : bool
whether to create the Q-functions
create_vf : bool
whether to create the value function
Returns
-------
tf.Variable
the output from the first Q-function. Set to None if `create_qf` is
False.
tf.Variable
the output from the second Q-function. Set to None if `create_qf`
is False.
tf.Variable
the output from the value function. Set to None if `create_vf` is
False.
"""
conv_params = dict(
image_height=self.model_params["image_height"],
image_width=self.model_params["image_width"],
image_channels=self.model_params["image_channels"],
ignore_flat_channels=self.model_params["ignore_flat_channels"],
ignore_image=self.model_params["ignore_image"],
filters=self.model_params["filters"],
kernel_sizes=self.model_params["kernel_sizes"],
strides=self.model_params["strides"],
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
reuse=reuse,
)
fcnet_params = dict(
layers=self.model_params["layers"],
num_output=1,
stochastic=False,
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
reuse=reuse,
)
with tf.compat.v1.variable_scope(scope, reuse=reuse):
# Value function
if create_vf:
if self.model_params["model_type"] == "conv":
vf_h = create_conv(obs=obs, scope="vf", **conv_params)
else:
vf_h = obs
value_fn = create_fcnet(obs=vf_h,
scope="vf",
output_pre="vf_",
**fcnet_params)
else:
value_fn = None
# Double Q values to reduce overestimation
if create_qf:
# Concatenate the observations and actions.
qf_h = tf.concat([obs, action], axis=-1)
if self.model_params["model_type"] == "conv":
qf1_h = create_conv(obs=qf_h, scope="qf1", **conv_params)
qf2_h = create_conv(obs=qf_h, scope="qf2", **conv_params)
else:
qf1_h = qf_h
qf2_h = qf_h
qf1 = create_fcnet(obs=qf1_h,
scope="qf1",
output_pre="qf_",
**fcnet_params)
qf2 = create_fcnet(obs=qf2_h,
scope="qf2",
output_pre="qf_",
**fcnet_params)
else:
qf1, qf2 = None, None
return qf1, qf2, value_fn
def update(self, **kwargs):
"""Perform a gradient update step."""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return
# Get a batch
obs0, actions, rewards, obs1, done1 = self.replay_buffer.sample()
return self.update_from_batch(obs0, actions, rewards, obs1, done1)
def update_from_batch(self,
obs0,
actions,
rewards,
obs1,
terminals1,
update_actor=True):
"""Perform gradient update step given a batch of data.
Parameters
----------
obs0 : array_like
batch of observations
actions : array_like
batch of actions executed given obs_batch
rewards : array_like
rewards received as results of executing act_batch
obs1 : array_like
next set of observations seen after executing act_batch
terminals1 : numpy bool
done_mask[i] = 1 if executing act_batch[i] resulted in the end of
an episode and 0 otherwise.
update_actor : bool
whether to update the actor policy. Unused by this method.
"""
del update_actor # unused by this method
# Normalize the actions (bounded between [-1, 1]).
actions = (actions - self._ac_means) / self._ac_magnitudes
# Reshape to match previous behavior and placeholder shape.
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
# Collect all update and loss call operations.
step_ops = [
self.critic_optimizer,
self.actor_optimizer,
self.alpha_optimizer,
self.target_soft_updates,
]
# Prepare the feed_dict information.
feed_dict = {
self.obs_ph: obs0,
self.action_ph: actions,
self.rew_ph: rewards,
self.obs1_ph: obs1,
self.terminals1: terminals1
}
# Perform the update operations.
self.sess.run(step_ops, feed_dict)
def get_action(self, obs, context, apply_noise, random_actions, env_num=0):
"""See parent class."""
# Add the contextual observation, if applicable.
obs = self._get_obs(obs, context, axis=1)
if random_actions:
return np.array([self.ac_space.sample()])
elif apply_noise:
normalized_action = self.sess.run(self.policy_out,
feed_dict={self.obs_ph: obs})
return self._ac_magnitudes * normalized_action + self._ac_means
else:
normalized_action = self.sess.run(self.deterministic_action,
feed_dict={self.obs_ph: obs})
return self._ac_magnitudes * normalized_action + self._ac_means
def _setup_critic_optimizer(self, scope):
"""Create minimization operation for critic Q-function.
Create a `tf.optimizer.minimize` operation for updating critic
Q-function with gradient descent.
See Equations (5, 6) in [1], for further information of the Q-function
update rule.
"""
scope_name = 'model/value_fns'
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
print('setting up critic optimizer')
for name in ['qf1', 'qf2', 'vf']:
scope_i = '{}/{}'.format(scope_name, name)
print_params_shape(scope_i, name)
# Take the min of the two Q-Values (Double-Q Learning)
min_qf_pi = tf.minimum(self.qf1_pi, self.qf2_pi)
# Target for Q value regression
q_backup = tf.stop_gradient(self.rew_ph + (1 - self.terminals1) *
self.gamma * self.value_target)
# choose the loss function
if self.use_huber:
loss_fn = tf.compat.v1.losses.huber_loss
else:
loss_fn = tf.compat.v1.losses.mean_squared_error
# Compute Q-Function loss
qf1_loss = loss_fn(q_backup, self.qf1)
qf2_loss = loss_fn(q_backup, self.qf2)
# Target for value fn regression
# We update the vf towards the min of two Q-functions in order to
# reduce overestimation bias from function approximation error.
v_backup = tf.stop_gradient(min_qf_pi - self.alpha * self.logp_pi)
value_loss = loss_fn(self.value_fn, v_backup)
self.critic_loss = (qf1_loss, qf2_loss, value_loss)
# Combine the loss functions for the optimizer.
critic_loss = qf1_loss + qf2_loss + value_loss
# Critic train op
critic_optimizer = tf.compat.v1.train.AdamOptimizer(self.critic_lr)
self.critic_optimizer = critic_optimizer.minimize(
critic_loss, var_list=get_trainable_vars(scope_name))
def _setup_actor_optimizer(self, scope):
"""Create minimization operations for policy and entropy.
Creates a `tf.optimizer.minimize` operations for updating policy and
entropy with gradient descent.
See Section 4.2 in [1], for further information of the policy update,
and Section 5 in [1] for further information of the entropy update.
"""
scope_name = 'model/pi/'
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
print('setting up actor and alpha optimizers')
print_params_shape(scope_name, "actor")
# Take the min of the two Q-Values (Double-Q Learning)
min_qf_pi = tf.minimum(self.qf1_pi, self.qf2_pi)
# Compute the entropy temperature loss.
self.alpha_loss = -tf.reduce_mean(
self.log_alpha *
tf.stop_gradient(self.logp_pi + self.target_entropy))
alpha_optimizer = tf.compat.v1.train.AdamOptimizer(self.actor_lr)
self.alpha_optimizer = alpha_optimizer.minimize(
self.alpha_loss, var_list=self.log_alpha)
# Compute the policy loss
self.actor_loss = tf.reduce_mean(self.alpha * self.logp_pi - min_qf_pi)
# Add a regularization penalty.
self.actor_loss += self._l2_loss(self.l2_penalty, scope_name)
# Policy train op (has to be separate from value train op, because
# min_qf_pi appears in policy_loss)
actor_optimizer = tf.compat.v1.train.AdamOptimizer(self.actor_lr)
self.actor_optimizer = actor_optimizer.minimize(
self.actor_loss, var_list=get_trainable_vars(scope_name))
def _setup_stats(self, base):
"""Create the running means and std of the model inputs and outputs.
This method also adds the same running means and stds as scalars to
tensorboard for additional storage.
"""
ops = []
names = []
ops += [tf.reduce_mean(self.qf1)]
names += ['{}/reference_Q1_mean'.format(base)]
ops += [reduce_std(self.qf1)]
names += ['{}/reference_Q1_std'.format(base)]
ops += [tf.reduce_mean(self.qf2)]
names += ['{}/reference_Q2_mean'.format(base)]
ops += [reduce_std(self.qf2)]
names += ['{}/reference_Q2_std'.format(base)]
ops += [tf.reduce_mean(self.qf1_pi)]
names += ['{}/reference_actor_Q1_mean'.format(base)]
ops += [reduce_std(self.qf1_pi)]
names += ['{}/reference_actor_Q1_std'.format(base)]
ops += [tf.reduce_mean(self.qf2_pi)]
names += ['{}/reference_actor_Q2_mean'.format(base)]
ops += [reduce_std(self.qf2_pi)]
names += ['{}/reference_actor_Q2_std'.format(base)]
ops += [
tf.reduce_mean(self._ac_magnitudes * self.policy_out +
self._ac_means)
]
names += ['{}/reference_action_mean'.format(base)]
ops += [
reduce_std(self._ac_magnitudes * self.policy_out + self._ac_means)
]
names += ['{}/reference_action_std'.format(base)]
ops += [tf.reduce_mean(self.logp_pi)]
names += ['{}/reference_log_probability_mean'.format(base)]
ops += [reduce_std(self.logp_pi)]
names += ['{}/reference_log_probability_std'.format(base)]
ops += [tf.reduce_mean(self.rew_ph)]
names += ['{}/rewards'.format(base)]
ops += [self.alpha_loss]
names += ['{}/alpha_loss'.format(base)]
ops += [self.actor_loss]
names += ['{}/actor_loss'.format(base)]
ops += [self.critic_loss[0]]
names += ['{}/Q1_loss'.format(base)]
ops += [self.critic_loss[1]]
names += ['{}/Q2_loss'.format(base)]
tf.compat.v1.summary.scalar('Q2_loss', self.critic_loss[1])
ops += [self.critic_loss[2]]
names += ['{}/value_loss'.format(base)]
# Add all names and ops to the tensorboard summary.
for op, name in zip(ops, names):
tf.compat.v1.summary.scalar(name, op)
return ops, names
def initialize(self):
"""See parent class."""
self.sess.run(self.target_init_updates)
def store_transition(self,
obs0,
context0,
action,
reward,
obs1,
context1,
done,
is_final_step,
env_num=0,
evaluate=False):
"""See parent class."""
if not evaluate:
# Add the contextual observation, if applicable.
obs0 = self._get_obs(obs0, context0, axis=0)
obs1 = self._get_obs(obs1, context1, axis=0)
self.replay_buffer.add(obs0, action, reward, obs1, float(done))
def get_td_map(self):
"""See parent class."""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return {}
# Get a batch.
obs0, actions, rewards, obs1, done1 = self.replay_buffer.sample()
return self.get_td_map_from_batch(obs0, actions, rewards, obs1, done1)
def get_td_map_from_batch(self, obs0, actions, rewards, obs1, terminals1):
"""Convert a batch to a td_map."""
# Reshape to match previous behavior and placeholder shape.
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
td_map = {
self.obs_ph: obs0,
self.action_ph: actions,
self.rew_ph: rewards,
self.obs1_ph: obs1,
self.terminals1: terminals1
}
return td_map
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
actor_lr,
critic_lr,
verbose,
tau,
gamma,
use_huber,
l2_penalty,
model_params,
target_entropy,
scope=None,
num_envs=1):
"""Instantiate the feed-forward neural network policy.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
tau : float
target update rate
gamma : float
discount factor
use_huber : bool
specifies whether to use the huber distance function as the loss
for the critic. If set to False, the mean-squared error metric is
used instead
l2_penalty : float
L2 regularization penalty. This is applied to the policy network.
model_params : dict
dictionary of model-specific parameters. See parent class.
target_entropy : float
target entropy used when learning the entropy coefficient. If set
to None, a heuristic value is used.
scope : str
an upper-level scope term. Used by policies that call this one.
"""
super(FeedForwardPolicy, self).__init__(
sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
actor_lr=actor_lr,
critic_lr=critic_lr,
verbose=verbose,
tau=tau,
gamma=gamma,
use_huber=use_huber,
l2_penalty=l2_penalty,
model_params=model_params,
num_envs=num_envs,
)
if target_entropy is None:
self.target_entropy = -np.prod(self.ac_space.shape)
else:
self.target_entropy = target_entropy
self._ac_means = 0.5 * (ac_space.high + ac_space.low)
self._ac_magnitudes = 0.5 * (ac_space.high - ac_space.low)
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.terminals1 = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rew_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
self.obs1_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs1')
# =================================================================== #
# Step 3: Create actor and critic variables. #
# =================================================================== #
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
self.deterministic_action, self.policy_out, self.logp_pi, \
self.logp_action = self.make_actor(self.obs_ph, self.action_ph)
self.qf1, self.qf2, self.value_fn = self.make_critic(
self.obs_ph, self.action_ph, create_qf=True, create_vf=True)
self.qf1_pi, self.qf2_pi, _ = self.make_critic(self.obs_ph,
self.policy_out,
create_qf=True,
create_vf=False,
reuse=True)
# The entropy coefficient or entropy can be learned automatically,
# see Automating Entropy Adjustment for Maximum Entropy RL section
# of https://arxiv.org/abs/1812.05905
self.log_alpha = tf.compat.v1.get_variable('log_alpha',
dtype=tf.float32,
initializer=0.0)
self.alpha = tf.exp(self.log_alpha)
with tf.compat.v1.variable_scope("target", reuse=False):
# Create the value network
_, _, value_target = self.make_critic(self.obs1_ph,
create_qf=False,
create_vf=True)
self.value_target = value_target
# Create the target update operations.
init, soft = self._setup_target_updates('model/value_fns/vf',
'target/value_fns/vf', scope,
tau, verbose)
self.target_init_updates = init
self.target_soft_updates = soft
# =================================================================== #
# Step 4: Setup the optimizers for the actor and critic. #
# =================================================================== #
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
self._setup_actor_optimizer(scope)
self._setup_critic_optimizer(scope)
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
actor_lr,
critic_lr,
verbose,
tau,
gamma,
use_huber,
l2_penalty,
model_params,
noise,
target_policy_noise,
target_noise_clip,
scope=None,
num_envs=1):
"""Instantiate the feed-forward neural network policy.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
tau : float
target update rate
gamma : float
discount factor
use_huber : bool
specifies whether to use the huber distance function as the loss
for the critic. If set to False, the mean-squared error metric is
used instead
l2_penalty : float
L2 regularization penalty. This is applied to the policy network.
model_params : dict
dictionary of model-specific parameters. See parent class.
noise : float
scaling term to the range of the action space, that is subsequently
used as the standard deviation of Gaussian noise added to the
action if `apply_noise` is set to True in `get_action`
target_policy_noise : float
standard deviation term to the noise from the output of the target
actor policy. See TD3 paper for more.
target_noise_clip : float
clipping term for the noise injected in the target actor policy
scope : str
an upper-level scope term. Used by policies that call this one.
"""
super(FeedForwardPolicy, self).__init__(
sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
actor_lr=actor_lr,
critic_lr=critic_lr,
verbose=verbose,
tau=tau,
gamma=gamma,
use_huber=use_huber,
l2_penalty=l2_penalty,
model_params=model_params,
num_envs=num_envs,
)
# action magnitudes
ac_mag = 0.5 * (ac_space.high - ac_space.low)
self.noise = noise * ac_mag
self.target_policy_noise = np.array([ac_mag * target_policy_noise])
self.target_noise_clip = np.array([ac_mag * target_noise_clip])
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.terminals1 = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rew_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
self.obs1_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs1')
# =================================================================== #
# Step 3: Create actor and critic variables. #
# =================================================================== #
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
self.actor_tf = self.make_actor(self.obs_ph)
self.critic_tf = [
self.make_critic(self.obs_ph,
self.action_ph,
scope="qf_{}".format(i)) for i in range(2)
]
self.critic_with_actor_tf = [
self.make_critic(self.obs_ph,
self.actor_tf,
reuse=True,
scope="qf_{}".format(i)) for i in range(2)
]
with tf.compat.v1.variable_scope("target", reuse=False):
# create the target actor policy
actor_target = self.make_actor(self.obs1_ph)
# smooth target policy by adding clipped noise to target actions
target_noise = tf.random.normal(tf.shape(actor_target),
stddev=self.target_policy_noise)
target_noise = tf.clip_by_value(target_noise,
-self.target_noise_clip,
self.target_noise_clip)
# clip the noisy action to remain in the bounds
noisy_actor_target = tf.clip_by_value(actor_target + target_noise,
self.ac_space.low,
self.ac_space.high)
# create the target critic policies
critic_target = [
self.make_critic(self.obs1_ph,
noisy_actor_target,
scope="qf_{}".format(i)) for i in range(2)
]
# Create the target update operations.
init, soft = self._setup_target_updates('model', 'target', scope, tau,
verbose)
self.target_init_updates = init
self.target_soft_updates = soft
# =================================================================== #
# Step 4: Setup the optimizers for the actor and critic. #
# =================================================================== #
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
self._setup_actor_optimizer(scope)
self._setup_critic_optimizer(critic_target, scope)
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
class FeedForwardPolicy(ActorCriticPolicy):
"""Feed-forward neural network actor-critic policy.
Attributes
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow debug
tau : float
target update rate
gamma : float
discount factor
use_huber : bool
specifies whether to use the huber distance function as the loss for
the critic. If set to False, the mean-squared error metric is used
instead
l2_penalty : float
L2 regularization penalty. This is applied to the policy network.
model_params : dict
dictionary of model-specific parameters. See parent class.
noise : float
scaling term to the range of the action space, that is subsequently
used as the standard deviation of Gaussian noise added to the action if
`apply_noise` is set to True in `get_action`
target_policy_noise : float
standard deviation term to the noise from the output of the target
actor policy. See TD3 paper for more.
target_noise_clip : float
clipping term for the noise injected in the target actor policy
replay_buffer : hbaselines.fcnet.replay_buffer.ReplayBuffer
the replay buffer
terminals1 : tf.compat.v1.placeholder
placeholder for the next step terminals
rew_ph : tf.compat.v1.placeholder
placeholder for the rewards
action_ph : tf.compat.v1.placeholder
placeholder for the actions
obs_ph : tf.compat.v1.placeholder
placeholder for the observations
obs1_ph : tf.compat.v1.placeholder
placeholder for the next step observations
actor_tf : tf.Variable
the output from the actor network
critic_tf : list of tf.Variable
the output from the critic networks. Two networks are used to stabilize
training.
critic_with_actor_tf : list of tf.Variable
the output from the critic networks with the action provided directly
by the actor policy
target_init_updates : tf.Operation
an operation that sets the values of the trainable parameters of the
target actor/critic to match those actual actor/critic
target_soft_updates : tf.Operation
soft target update function
actor_loss : tf.Operation
the operation that returns the loss of the actor
actor_optimizer : tf.Operation
the operation that updates the trainable parameters of the actor
critic_loss : tf.Operation
the operation that returns the loss of the critic
critic_optimizer : tf.Operation
the operation that updates the trainable parameters of the critic
"""
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
actor_lr,
critic_lr,
verbose,
tau,
gamma,
use_huber,
l2_penalty,
model_params,
noise,
target_policy_noise,
target_noise_clip,
scope=None,
num_envs=1):
"""Instantiate the feed-forward neural network policy.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
actor_lr : float
actor learning rate
critic_lr : float
critic learning rate
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
tau : float
target update rate
gamma : float
discount factor
use_huber : bool
specifies whether to use the huber distance function as the loss
for the critic. If set to False, the mean-squared error metric is
used instead
l2_penalty : float
L2 regularization penalty. This is applied to the policy network.
model_params : dict
dictionary of model-specific parameters. See parent class.
noise : float
scaling term to the range of the action space, that is subsequently
used as the standard deviation of Gaussian noise added to the
action if `apply_noise` is set to True in `get_action`
target_policy_noise : float
standard deviation term to the noise from the output of the target
actor policy. See TD3 paper for more.
target_noise_clip : float
clipping term for the noise injected in the target actor policy
scope : str
an upper-level scope term. Used by policies that call this one.
"""
super(FeedForwardPolicy, self).__init__(
sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
actor_lr=actor_lr,
critic_lr=critic_lr,
verbose=verbose,
tau=tau,
gamma=gamma,
use_huber=use_huber,
l2_penalty=l2_penalty,
model_params=model_params,
num_envs=num_envs,
)
# action magnitudes
ac_mag = 0.5 * (ac_space.high - ac_space.low)
self.noise = noise * ac_mag
self.target_policy_noise = np.array([ac_mag * target_policy_noise])
self.target_noise_clip = np.array([ac_mag * target_noise_clip])
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.terminals1 = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rew_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
self.obs1_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs1')
# =================================================================== #
# Step 3: Create actor and critic variables. #
# =================================================================== #
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
self.actor_tf = self.make_actor(self.obs_ph)
self.critic_tf = [
self.make_critic(self.obs_ph,
self.action_ph,
scope="qf_{}".format(i)) for i in range(2)
]
self.critic_with_actor_tf = [
self.make_critic(self.obs_ph,
self.actor_tf,
reuse=True,
scope="qf_{}".format(i)) for i in range(2)
]
with tf.compat.v1.variable_scope("target", reuse=False):
# create the target actor policy
actor_target = self.make_actor(self.obs1_ph)
# smooth target policy by adding clipped noise to target actions
target_noise = tf.random.normal(tf.shape(actor_target),
stddev=self.target_policy_noise)
target_noise = tf.clip_by_value(target_noise,
-self.target_noise_clip,
self.target_noise_clip)
# clip the noisy action to remain in the bounds
noisy_actor_target = tf.clip_by_value(actor_target + target_noise,
self.ac_space.low,
self.ac_space.high)
# create the target critic policies
critic_target = [
self.make_critic(self.obs1_ph,
noisy_actor_target,
scope="qf_{}".format(i)) for i in range(2)
]
# Create the target update operations.
init, soft = self._setup_target_updates('model', 'target', scope, tau,
verbose)
self.target_init_updates = init
self.target_soft_updates = soft
# =================================================================== #
# Step 4: Setup the optimizers for the actor and critic. #
# =================================================================== #
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
self._setup_actor_optimizer(scope)
self._setup_critic_optimizer(critic_target, scope)
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
def _setup_actor_optimizer(self, scope):
"""Create the actor loss, gradient, and optimizer."""
scope_name = 'model/pi/'
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
print('setting up actor optimizer')
print_params_shape(scope_name, "actor")
# Compute the actor loss.
self.actor_loss = -tf.reduce_mean(self.critic_with_actor_tf[0])
# Add a regularization penalty.
self.actor_loss += self._l2_loss(self.l2_penalty, scope_name)
# Create an optimizer object.
optimizer = tf.compat.v1.train.AdamOptimizer(self.actor_lr)
self.actor_optimizer = optimizer.minimize(
self.actor_loss, var_list=get_trainable_vars(scope_name))
def _setup_critic_optimizer(self, critic_target, scope):
"""Create the critic loss, gradient, and optimizer."""
if self.verbose >= 2:
print('setting up critic optimizer')
# compute the target critic term
with tf.compat.v1.variable_scope("loss", reuse=False):
q_obs1 = tf.minimum(critic_target[0], critic_target[1])
target_q = tf.stop_gradient(self.rew_ph + (1. - self.terminals1) *
self.gamma * q_obs1)
tf.compat.v1.summary.scalar('critic_target',
tf.reduce_mean(target_q))
# choose the loss function
if self.use_huber:
loss_fn = tf.compat.v1.losses.huber_loss
else:
loss_fn = tf.compat.v1.losses.mean_squared_error
self.critic_loss = [loss_fn(q, target_q) for q in self.critic_tf]
self.critic_optimizer = []
for i, critic_loss in enumerate(self.critic_loss):
scope_name = 'model/qf_{}/'.format(i)
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
print_params_shape(scope_name, "critic {}".format(i))
# create an optimizer object
optimizer = tf.compat.v1.train.AdamOptimizer(self.critic_lr)
# create the optimizer object
self.critic_optimizer.append(
optimizer.minimize(loss=critic_loss,
var_list=get_trainable_vars(scope_name)))
def make_actor(self, obs, reuse=False, scope="pi"):
"""Create an actor tensor.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the actor
Returns
-------
tf.Variable
the output from the actor
"""
# Initial image pre-processing (for convolutional policies).
if self.model_params["model_type"] == "conv":
pi_h = create_conv(
obs=obs,
image_height=self.model_params["image_height"],
image_width=self.model_params["image_width"],
image_channels=self.model_params["image_channels"],
ignore_flat_channels=self.model_params["ignore_flat_channels"],
ignore_image=self.model_params["ignore_image"],
filters=self.model_params["filters"],
kernel_sizes=self.model_params["kernel_sizes"],
strides=self.model_params["strides"],
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
scope=scope,
reuse=reuse,
)
else:
pi_h = obs
# Create the model.
policy = create_fcnet(
obs=pi_h,
layers=self.model_params["layers"],
num_output=self.ac_space.shape[0],
stochastic=False,
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
scope=scope,
reuse=reuse,
)
# Scaling terms to the output from the policy.
ac_means = (self.ac_space.high + self.ac_space.low) / 2.
ac_magnitudes = (self.ac_space.high - self.ac_space.low) / 2.
# Apply squashing and scale by action space.
return ac_means + ac_magnitudes * tf.nn.tanh(policy)
def make_critic(self, obs, action, reuse=False, scope="qf"):
"""Create a critic tensor.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
action : tf.compat.v1.placeholder
the input action placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the actor
Returns
-------
tf.Variable
the output from the critic
"""
# Concatenate the observations and actions.
qf_h = tf.concat([obs, action], axis=-1)
# Initial image pre-processing (for convolutional policies).
if self.model_params["model_type"] == "conv":
qf_h = create_conv(
obs=qf_h,
image_height=self.model_params["image_height"],
image_width=self.model_params["image_width"],
image_channels=self.model_params["image_channels"],
ignore_flat_channels=self.model_params["ignore_flat_channels"],
ignore_image=self.model_params["ignore_image"],
filters=self.model_params["filters"],
kernel_sizes=self.model_params["kernel_sizes"],
strides=self.model_params["strides"],
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
scope=scope,
reuse=reuse,
)
return create_fcnet(
obs=qf_h,
layers=self.model_params["layers"],
num_output=1,
stochastic=False,
act_fun=self.model_params["act_fun"],
layer_norm=self.model_params["layer_norm"],
scope=scope,
reuse=reuse,
output_pre="qf_",
)
def update(self, update_actor=True, **kwargs):
"""Perform a gradient update step.
**Note**; The target update soft updates occur at the same frequency as
the actor update frequencies.
Parameters
----------
update_actor : bool
specifies whether to update the actor policy. The critic policy is
still updated if this value is set to False.
"""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return
# Get a batch
obs0, actions, rewards, obs1, terminals1 = self.replay_buffer.sample()
return self.update_from_batch(obs0, actions, rewards, obs1, terminals1,
update_actor)
def update_from_batch(self,
obs0,
actions,
rewards,
obs1,
terminals1,
update_actor=True):
"""Perform gradient update step given a batch of data.
Parameters
----------
obs0 : array_like
batch of observations
actions : array_like
batch of actions executed given obs_batch
rewards : array_like
rewards received as results of executing act_batch
obs1 : array_like
next set of observations seen after executing act_batch
terminals1 : numpy bool
done_mask[i] = 1 if executing act_batch[i] resulted in the end of
an episode and 0 otherwise.
update_actor : bool, optional
specified whether to perform gradient update procedures to the
actor policy. Default set to True. Note that the update procedure
for the critic is always performed when calling this method.
"""
# Reshape to match previous behavior and placeholder shape.
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
# Update operations for the critic networks.
step_ops = [self.critic_optimizer[0], self.critic_optimizer[1]]
if update_actor:
# Actor updates and target soft update operation.
step_ops += [self.actor_optimizer, self.target_soft_updates]
# Perform the update operations.
self.sess.run(step_ops,
feed_dict={
self.obs_ph: obs0,
self.action_ph: actions,
self.rew_ph: rewards,
self.obs1_ph: obs1,
self.terminals1: terminals1
})
def get_action(self, obs, context, apply_noise, random_actions, env_num=0):
"""See parent class."""
# Add the contextual observation, if applicable.
obs = self._get_obs(obs, context, axis=1)
if random_actions:
action = np.array([self.ac_space.sample()])
else:
action = self.sess.run(self.actor_tf, {self.obs_ph: obs})
if apply_noise:
# compute noisy action
if apply_noise:
action += np.random.normal(0, self.noise, action.shape)
# clip by bounds
action = np.clip(action, self.ac_space.low, self.ac_space.high)
return action
def store_transition(self,
obs0,
context0,
action,
reward,
obs1,
context1,
done,
is_final_step,
env_num=0,
evaluate=False):
"""See parent class."""
if not evaluate:
# Add the contextual observation, if applicable.
obs0 = self._get_obs(obs0, context0, axis=0)
obs1 = self._get_obs(obs1, context1, axis=0)
# Modify the done mask in accordance with the TD3 algorithm. Done
# masks that correspond to the final step are set to False.
done = done and not is_final_step
self.replay_buffer.add(obs0, action, reward, obs1, float(done))
def initialize(self):
"""See parent class.
This method initializes the target parameters to match the model
parameters.
"""
self.sess.run(self.target_init_updates)
def _setup_stats(self, base):
"""Create the running means and std of the model inputs and outputs.
This method also adds the same running means and stds as scalars to
tensorboard for additional storage.
"""
ops = []
names = []
ops += [tf.reduce_mean(self.critic_tf[0])]
names += ['{}/reference_Q1_mean'.format(base)]
ops += [reduce_std(self.critic_tf[0])]
names += ['{}/reference_Q1_std'.format(base)]
ops += [tf.reduce_mean(self.critic_tf[1])]
names += ['{}/reference_Q2_mean'.format(base)]
ops += [reduce_std(self.critic_tf[1])]
names += ['{}/reference_Q2_std'.format(base)]
ops += [tf.reduce_mean(self.critic_with_actor_tf[0])]
names += ['{}/reference_actor_Q1_mean'.format(base)]
ops += [reduce_std(self.critic_with_actor_tf[0])]
names += ['{}/reference_actor_Q1_std'.format(base)]
ops += [tf.reduce_mean(self.critic_with_actor_tf[1])]
names += ['{}/reference_actor_Q2_mean'.format(base)]
ops += [reduce_std(self.critic_with_actor_tf[1])]
names += ['{}/reference_actor_Q2_std'.format(base)]
ops += [tf.reduce_mean(self.actor_tf)]
names += ['{}/reference_action_mean'.format(base)]
ops += [reduce_std(self.actor_tf)]
names += ['{}/reference_action_std'.format(base)]
ops += [tf.reduce_mean(self.rew_ph)]
names += ['{}/rewards'.format(base)]
ops += [self.actor_loss]
names += ['{}/actor_loss'.format(base)]
ops += [self.critic_loss[0]]
names += ['{}/Q1_loss'.format(base)]
ops += [self.critic_loss[1]]
names += ['{}/Q2_loss'.format(base)]
# Add all names and ops to the tensorboard summary.
for op, name in zip(ops, names):
tf.compat.v1.summary.scalar(name, op)
return ops, names
def get_td_map(self):
"""See parent class."""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return {}
# Get a batch.
obs0, actions, rewards, obs1, done1 = self.replay_buffer.sample()
return self.get_td_map_from_batch(obs0, actions, rewards, obs1, done1)
def get_td_map_from_batch(self, obs0, actions, rewards, obs1, terminals1):
"""Convert a batch to a td_map."""
# Reshape to match previous behavior and placeholder shape.
rewards = rewards.reshape(-1, 1)
terminals1 = terminals1.reshape(-1, 1)
td_map = {
self.obs_ph: obs0,
self.action_ph: actions,
self.rew_ph: rewards,
self.obs1_ph: obs1,
self.terminals1: terminals1
}
return td_map
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
learning_rate,
verbose,
layer_norm,
layers,
act_fun,
use_huber,
stochastic,
scope=None):
"""Instantiate the policy object.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
learning_rate : float
the learning rate for the policy
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
layer_norm : bool
enable layer normalisation
layers : list of int or None
the size of the Neural network for the policy
act_fun : tf.nn.*
the activation function to use in the neural network
use_huber : bool
specifies whether to use the huber distance function as the loss
function. If set to False, the mean-squared error metric is used
instead
stochastic : bool
specifies whether the policies are stochastic or deterministic
scope : str
an upper-level scope term. Used by policies that call this one.
"""
super(FeedForwardPolicy, self).__init__(sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
learning_rate=learning_rate,
verbose=verbose,
layer_norm=layer_norm,
layers=layers,
act_fun=act_fun,
use_huber=use_huber,
stochastic=stochastic)
assert len(self.layers) >= 1, \
"Error: must have at least one hidden layer for the policy."
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
# =================================================================== #
# Step 3: Create policy variables. #
# =================================================================== #
self.policy = None
self.logp_ac = None
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
if self.stochastic:
self._setup_stochastic_policy(self.obs_ph, self.action_ph)
else:
self._setup_deterministic_policy(self.obs_ph)
# =================================================================== #
# Step 4: Setup the optimizer. #
# =================================================================== #
self.loss = None
self.optimizer = None
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
if self.stochastic:
self._setup_stochastic_optimizer(scope)
else:
self._setup_deterministic_optimizer(self.action_ph, scope)
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
class FeedForwardPolicy(ImitationLearningPolicy):
"""Fully-connected neural network imitation learning policy.
Attributes
----------
replay_buffer : hbaselines.fcnet.replay_buffer.ReplayBuffer
the replay buffer
action_ph : tf.compat.v1.placeholder
placeholder for the actions
obs_ph : tf.compat.v1.placeholder
placeholder for the observations
policy : tf.Variable
the output from the imitation learning policy
logp_ac : tf.Operation
the operation that computes the log-probability of a given action. Only
applies to stochastic policies.
loss : tf.Operation
the operation that computes the loss
optimizer : tf.Operation
the operation that updates the trainable parameters of the policy
"""
def __init__(self,
sess,
ob_space,
ac_space,
co_space,
buffer_size,
batch_size,
learning_rate,
verbose,
layer_norm,
layers,
act_fun,
use_huber,
stochastic,
scope=None):
"""Instantiate the policy object.
Parameters
----------
sess : tf.compat.v1.Session
the current TensorFlow session
ob_space : gym.spaces.*
the observation space of the environment
ac_space : gym.spaces.*
the action space of the environment
co_space : gym.spaces.*
the context space of the environment
buffer_size : int
the max number of transitions to store
batch_size : int
SGD batch size
learning_rate : float
the learning rate for the policy
verbose : int
the verbosity level: 0 none, 1 training information, 2 tensorflow
debug
layer_norm : bool
enable layer normalisation
layers : list of int or None
the size of the Neural network for the policy
act_fun : tf.nn.*
the activation function to use in the neural network
use_huber : bool
specifies whether to use the huber distance function as the loss
function. If set to False, the mean-squared error metric is used
instead
stochastic : bool
specifies whether the policies are stochastic or deterministic
scope : str
an upper-level scope term. Used by policies that call this one.
"""
super(FeedForwardPolicy, self).__init__(sess=sess,
ob_space=ob_space,
ac_space=ac_space,
co_space=co_space,
buffer_size=buffer_size,
batch_size=batch_size,
learning_rate=learning_rate,
verbose=verbose,
layer_norm=layer_norm,
layers=layers,
act_fun=act_fun,
use_huber=use_huber,
stochastic=stochastic)
assert len(self.layers) >= 1, \
"Error: must have at least one hidden layer for the policy."
# Compute the shape of the input observation space, which may include
# the contextual term.
ob_dim = self._get_ob_dim(ob_space, co_space)
# =================================================================== #
# Step 1: Create a replay buffer object. #
# =================================================================== #
self.replay_buffer = ReplayBuffer(
buffer_size=self.buffer_size,
batch_size=self.batch_size,
obs_dim=ob_dim[0],
ac_dim=self.ac_space.shape[0],
)
# =================================================================== #
# Step 2: Create input variables. #
# =================================================================== #
with tf.compat.v1.variable_scope("input", reuse=False):
self.action_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) +
ac_space.shape,
name='actions')
self.obs_ph = tf.compat.v1.placeholder(tf.float32,
shape=(None, ) + ob_dim,
name='obs0')
# =================================================================== #
# Step 3: Create policy variables. #
# =================================================================== #
self.policy = None
self.logp_ac = None
# Create networks and core TF parts that are shared across setup parts.
with tf.compat.v1.variable_scope("model", reuse=False):
if self.stochastic:
self._setup_stochastic_policy(self.obs_ph, self.action_ph)
else:
self._setup_deterministic_policy(self.obs_ph)
# =================================================================== #
# Step 4: Setup the optimizer. #
# =================================================================== #
self.loss = None
self.optimizer = None
with tf.compat.v1.variable_scope("Optimizer", reuse=False):
if self.stochastic:
self._setup_stochastic_optimizer(scope)
else:
self._setup_deterministic_optimizer(self.action_ph, scope)
# =================================================================== #
# Step 5: Setup the operations for computing model statistics. #
# =================================================================== #
# Setup the running means and standard deviations of the model inputs
# and outputs.
self.stats_ops, self.stats_names = self._setup_stats(scope or "Model")
def _setup_stochastic_policy(self, obs, action, reuse=False, scope="pi"):
"""Create the variables of a stochastic policy.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
action : tf.compat.v1.placeholder
the input action placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the policy
"""
with tf.compat.v1.variable_scope(scope, reuse=reuse):
pi_h = obs
# create the hidden layers
for i, layer_size in enumerate(self.layers):
pi_h = layer(pi_h,
layer_size,
'fc{}'.format(i),
act_fun=self.act_fun,
layer_norm=self.layer_norm)
# create the output mean
policy_mean = layer(
pi_h,
self.ac_space.shape[0],
'mean',
act_fun=None,
kernel_initializer=tf.random_uniform_initializer(minval=-3e-3,
maxval=3e-3))
# create the output log_std
log_std = layer(
pi_h,
self.ac_space.shape[0],
'log_std',
act_fun=None,
)
# OpenAI Variation to cap the standard deviation
log_std = tf.clip_by_value(log_std, LOG_STD_MIN, LOG_STD_MAX)
std = tf.exp(log_std)
# Reparameterization trick
policy = policy_mean + tf.random.normal(tf.shape(policy_mean)) * std
logp_pi = gaussian_likelihood(policy, policy_mean, log_std)
logp_ac = gaussian_likelihood(action, policy_mean, log_std)
# Apply squashing and account for it in the probability
_, _, logp_ac = apply_squashing_func(policy_mean, action, logp_ac)
_, policy, _ = apply_squashing_func(policy_mean, policy, logp_pi)
# Store the variables under their respective parameters.
self.policy = policy
self.logp_ac = logp_ac
def _setup_stochastic_optimizer(self, scope):
"""Create the loss and optimizer of a stochastic policy."""
scope_name = 'model/pi/'
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
print('setting up optimizer')
print_params_shape(scope_name, "policy")
# Define the loss function.
self.loss = -tf.reduce_mean(self.logp_ac)
# Create an optimizer object.
optimizer = tf.compat.v1.train.AdamOptimizer(self.learning_rate)
# Create the optimizer operation.
self.optimizer = optimizer.minimize(
loss=self.loss, var_list=get_trainable_vars(scope_name))
def _setup_deterministic_policy(self, obs, reuse=False, scope="pi"):
"""Create the variables of deterministic a policy.
Parameters
----------
obs : tf.compat.v1.placeholder
the input observation placeholder
reuse : bool
whether or not to reuse parameters
scope : str
the scope name of the policy
"""
with tf.compat.v1.variable_scope(scope, reuse=reuse):
pi_h = obs
# create the hidden layers
for i, layer_size in enumerate(self.layers):
pi_h = layer(pi_h,
layer_size,
'fc{}'.format(i),
act_fun=self.act_fun,
layer_norm=self.layer_norm)
# create the output layer
policy = layer(pi_h,
self.ac_space.shape[0],
'output',
act_fun=tf.nn.tanh,
kernel_initializer=tf.random_uniform_initializer(
minval=-3e-3, maxval=3e-3))
# scaling terms to the output from the policy
ac_means = (self.ac_space.high + self.ac_space.low) / 2.
ac_magnitudes = (self.ac_space.high - self.ac_space.low) / 2.
policy = ac_means + ac_magnitudes * tf.to_float(policy)
# Store the variables under their respective parameters.
self.policy = policy
def _setup_deterministic_optimizer(self, action, scope=None):
"""Create the loss and optimizer of a deterministic policy."""
scope_name = 'model/pi/'
if scope is not None:
scope_name = scope + '/' + scope_name
if self.verbose >= 2:
print('setting up optimizer')
print_params_shape(scope_name, "policy")
# Choose the loss function.
if self.use_huber:
loss_fn = tf.compat.v1.losses.huber_loss
else:
loss_fn = tf.compat.v1.losses.mean_squared_error
# Define the loss function.
self.loss = loss_fn(action, self.policy)
# Create an optimizer object.
optimizer = tf.compat.v1.train.AdamOptimizer(self.learning_rate)
# Create the optimizer operation.
self.optimizer = optimizer.minimize(
loss=self.loss, var_list=get_trainable_vars(scope_name))
def _setup_stats(self, base):
"""Create the running means and std of the model inputs and outputs.
This method also adds the same running means and stds as scalars to
tensorboard for additional storage.
"""
ops = []
names = []
ops += [tf.reduce_mean(self.policy)]
names += ['{}/reference_action_mean'.format(base)]
ops += [reduce_std(self.policy)]
names += ['{}/reference_action_std'.format(base)]
ops += [tf.reduce_mean(self.loss)]
names += ['{}/reference_loss_mean'.format(base)]
ops += [reduce_std(self.loss)]
names += ['{}/reference_loss_std'.format(base)]
# Add all names and ops to the tensorboard summary.
for op, name in zip(ops, names):
tf.compat.v1.summary.scalar(name, op)
return ops, names
def update(self):
"""See parent class."""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return 0
# Get a batch.
obs0, actions, _, _, _ = self.replay_buffer.sample()
return self.update_from_batch(obs0, actions)
def update_from_batch(self, obs0, actions):
"""Perform gradient update step given a batch of data.
Parameters
----------
obs0 : array_like
batch of observations
actions : array_like
batch of actions executed given obs_batch
Returns
-------
float
policy loss
"""
loss, *_ = self.sess.run([self.loss, self.optimizer],
feed_dict={
self.obs_ph: obs0,
self.action_ph: actions,
})
return loss
def get_action(self, obs, context):
"""See parent class."""
# Add the contextual observation, if applicable.
obs = self._get_obs(obs, context, axis=1)
# Compute the action by the policy.
action = self.sess.run(self.policy, {self.obs_ph: obs})
if self.stochastic:
# Scale the action by the action space of the environment.
ac_means = 0.5 * (self.ac_space.high + self.ac_space.low)
ac_magnitudes = 0.5 * (self.ac_space.high - self.ac_space.low)
action = ac_magnitudes * action + ac_means
return action
def store_transition(self, obs0, context0, action, obs1, context1):
"""See parent class."""
# Add the contextual observation, if applicable.
obs0 = self._get_obs(obs0, context0, axis=0)
obs1 = self._get_obs(obs1, context1, axis=0)
self.replay_buffer.add(obs0, action, 0, obs1, float(False))
def get_td_map(self):
"""See parent class."""
# Not enough samples in the replay buffer.
if not self.replay_buffer.can_sample():
return {}
# Get a batch.
obs0, actions, _, _, _ = self.replay_buffer.sample()
return self.get_td_map_from_batch(obs0, actions)
def get_td_map_from_batch(self, obs0, actions):
"""Convert a batch to a td_map."""
return {
self.obs_ph: obs0,
self.action_ph: actions,
}