def update_opt(self,
loss,
target,
inputs,
extra_inputs=None,
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab_maml.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
def get_opt_output():
flat_grad = tensor_utils.flatten_tensor_variables(
tf.gradients(loss, target.get_params(trainable=True)))
return [tf.cast(loss, tf.float64), tf.cast(flat_grad, tf.float64)]
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=get_opt_output(),
))
def update_opt(self, loss, target, inputs, inner_kl, outer_kl, extra_inputs=None, meta_batch_size=1, num_grad_updates=1, **kwargs):
"""
:param inner_kl: Symbolic expression for inner kl
:param outer_kl: Symbolic expression for outer kl
:param meta_batch_size: number of MAML tasks, for batcher
"""
super().update_opt(loss, target, inputs, extra_inputs, **kwargs)
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(inputs + extra_inputs, loss),
f_inner_kl=lambda: tensor_utils.compile_function(inputs + extra_inputs, inner_kl),
f_outer_kl=lambda: tensor_utils.compile_function(inputs + extra_inputs, outer_kl),
)
if self.multi_adam > 1:
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
# for batch norm
updates = tf.group(*update_ops)
with tf.control_dependencies([updates]):
self._train_ops = [optimizer.minimize(loss, var_list=target.get_params(trainable=True)) for optimizer in self._tf_optimizers]
else:
self._train_ops = [optimizer.minimize(loss, var_list=target.get_params(trainable=True)) for optimizer in self._tf_optimizers]
self.meta_batch_size = meta_batch_size
self.num_grad_updates = num_grad_updates
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = tf.gradients(f, xs=params)
for idx, (grad, param) in enumerate(zip(constraint_grads, params)):
if grad is None:
constraint_grads[idx] = tf.zeros_like(param)
xs = tuple([
tensor_utils.new_tensor_like(p.name.split(":")[0], p)
for p in params
])
def Hx_plain():
Hx_plain_splits = tf.gradients(
tf.reduce_sum(
tf.stack([
tf.reduce_sum(g * x)
for g, x in zip(constraint_grads, xs)
])), params)
for idx, (Hx, param) in enumerate(zip(Hx_plain_splits, params)):
if Hx is None:
Hx_plain_splits[idx] = tf.zeros_like(param)
return tensor_utils.flatten_tensor_variables(Hx_plain_splits)
self.opt_fun = ext.lazydict(
f_Hx_plain=lambda: tensor_utils.compile_function(
inputs=inputs + xs,
outputs=Hx_plain(),
log_name="f_Hx_plain",
), )
def __init__(self, dim):
self._dim = dim
weights_var = tf.placeholder(dtype=tf.float32,
shape=(None, dim),
name="weights")
self._f_sample = tensor_utils.compile_function(
inputs=[weights_var],
outputs=tf.multinomial(weights_var, num_samples=1)[:, 0],
)
def update_opt(self, loss, target, leq_constraint, inputs, constraint_name="constraint", *args, **kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab_maml.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
constraint_term, constraint_value = leq_constraint
with tf.variable_scope(self._name):
penalty_var = tf.placeholder(tf.float32, tuple(), name="penalty")
penalized_loss = loss + penalty_var * constraint_term
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
def get_opt_output():
params = target.get_params(trainable=True)
grads = tf.gradients(penalized_loss, params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
return [
tf.cast(penalized_loss, tf.float64),
tf.cast(flat_grad, tf.float64),
]
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(inputs, loss, log_name="f_loss"),
f_constraint=lambda: tensor_utils.compile_function(inputs, constraint_term, log_name="f_constraint"),
f_penalized_loss=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=[penalized_loss, loss, constraint_term],
log_name="f_penalized_loss",
),
f_opt=lambda: tensor_utils.compile_function(
inputs=inputs + [penalty_var],
outputs=get_opt_output(),
)
)
def update_opt(self,
loss,
target,
inputs,
kl,
extra_inputs=None,
**kwargs):
"""
:param inner_kl: Symbolic expression for inner kl
:param outer_kl: Symbolic expression for outer kl
:param meta_batch_size: number of MAML tasks, for batcher
"""
super(PPOOptimizer, self).update_opt(loss, target, inputs,
extra_inputs, **kwargs)
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss),
f_kl=lambda: tensor_utils.compile_function(inputs + extra_inputs,
kl),
)
def update_opt(self,
loss,
target,
inputs,
inner_kl,
extra_inputs=None,
meta_batch_size=1,
num_grad_updates=1,
**kwargs):
"""
:param inner_kl: Symbolic expression for inner kl
:param meta_batch_size: number of MAML tasks, for batcher
"""
super().update_opt(loss, target, inputs, extra_inputs, **kwargs)
if extra_inputs is None:
extra_inputs = list()
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss),
f_inner_kl=lambda: tensor_utils.compile_function(
inputs + extra_inputs, inner_kl))
self.meta_batch_size = meta_batch_size
self.num_grad_updates = num_grad_updates
def __init__(
self,
name,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
prob_network=None,
):
"""
:param env_spec: A spec for the mdp.
:param hidden_sizes: list of sizes for the fully connected hidden layers
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param prob_network: manually specified network for this policy, other network params
are ignored
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Discrete)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
with tf.variable_scope(name):
if prob_network is None:
prob_network = self.create_MLP(
input_shape=(obs_dim,),
output_dim=env_spec.action_space.n,
hidden_sizes=hidden_sizes,
name="prob_network",
)
self._l_obs, self._l_prob = self.forward_MLP('prob_network', prob_network,
n_hidden=len(hidden_sizes), input_shape=(obs_dim,),
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax, reuse=None)
# if you want to input your own tensor.
self._forward_out = lambda x, is_train: self.forward_MLP('prob_network', prob_network,
n_hidden=len(hidden_sizes), hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity, input_tensor=x, is_training=is_train)[1]
self._f_prob = tensor_utils.compile_function(
[self._l_obs],
L.get_output(self._l_prob)
)
self._dist = Categorical(env_spec.action_space.n)
def update_opt(self, f, target, inputs, reg_coeff):
self.target = target
self.reg_coeff = reg_coeff
params = target.get_params(trainable=True)
constraint_grads = tf.gradients(f, xs=params)
for idx, (grad, param) in enumerate(zip(constraint_grads, params)):
if grad is None:
constraint_grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(constraint_grads)
def f_Hx_plain(
*args): #receives inputs and xs(flattened inputs) as arguments
inputs_ = args[:len(inputs)]
xs = args[len(inputs):]
flat_xs = np.concatenate([np.reshape(x, (-1, )) for x in xs])
param_val = self.target.get_param_values(trainable=True)
eps = np.cast['float32'](self.base_eps /
(np.linalg.norm(param_val) + 1e-8))
self.target.set_param_values(param_val + eps * flat_xs,
trainable=True)
flat_grad_dvplus = self.opt_fun["f_grad"](*inputs_)
self.target.set_param_values(param_val, trainable=True)
if self.symmetric:
self.target.set_param_values(param_val - eps * flat_xs,
trainable=True)
flat_grad_dvminus = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad_dvminus) / (2 * eps)
self.target.set_param_values(param_val, trainable=True)
else:
flat_grad = self.opt_fun["f_grad"](*inputs_)
hx = (flat_grad_dvplus - flat_grad) / eps
return hx
self.opt_fun = ext.lazydict(
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_Hx_plain=lambda: f_Hx_plain,
)
def __init__(
self,
name,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
prob_network=None,
):
"""
:param env_spec: A spec for the mdp.
:param hidden_sizes: list of sizes for the fully connected hidden layers
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param prob_network: manually specified network for this policy, other network params
are ignored
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Discrete)
with tf.variable_scope(name):
if prob_network is None:
prob_network = MLP(
input_shape=(env_spec.observation_space.flat_dim, ),
output_dim=env_spec.action_space.n,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax,
name="prob_network",
)
self._l_prob = prob_network.output_layer
self._l_obs = prob_network.input_layer
self._f_prob = tensor_utils.compile_function(
[prob_network.input_layer.input_var],
L.get_output(prob_network.output_layer))
self._dist = Categorical(env_spec.action_space.n)
super(CategoricalMLPPolicy, self).__init__(env_spec)
LayersPowered.__init__(self, [prob_network.output_layer])
def update_opt(self, loss, target, inputs, extra_inputs=None, **kwargs):
# Initializes the update opt used in the optimization
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab_maml.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs
:return: No return value.
"""
self._target = target
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
if update_ops:
# for batch norm
updates = tf.group(*update_ops)
with tf.control_dependencies([updates]):
self._train_op = self._tf_optimizer.minimize(
loss, var_list=target.get_params(trainable=True))
if self._init_tf_optimizer is not None:
self._init_train_op = self._init_tf_optimizer.minimize(
loss, var_list=target.get_params(trainable=True))
else:
self._train_op = self._tf_optimizer.minimize(
loss, var_list=target.get_params(trainable=True))
if self._init_tf_optimizer is not None:
self._init_train_op = self._init_tf_optimizer.minimize(
loss, var_list=target.get_params(trainable=True))
if extra_inputs is None:
extra_inputs = list()
self._input_vars = inputs + extra_inputs
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs + extra_inputs, loss), )
self.debug_loss = loss
self.debug_vars = target.get_params(trainable=True)
self.debug_target = target
def __init__(self,
name,
input_shape,
output_dim,
mean_network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
optimizer=None,
use_trust_region=True,
step_size=0.01,
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
std_nonlinearity=None,
normalize_inputs=True,
normalize_outputs=True,
subsample_factor=1.0):
"""
:param input_shape: Shape of the input data.
:param output_dim: Dimension of output.
:param hidden_sizes: Number of hidden units of each layer of the mean network.
:param hidden_nonlinearity: Non-linearity used for each layer of the mean network.
:param optimizer: Optimizer for minimizing the negative log-likelihood.
:param use_trust_region: Whether to use trust region constraint.
:param step_size: KL divergence constraint for each iteration
:param learn_std: Whether to learn the standard deviations. Only effective if adaptive_std is False. If
adaptive_std is True, this parameter is ignored, and the weights for the std network are always learned.
:param adaptive_std: Whether to make the std a function of the states.
:param std_share_network: Whether to use the same network as the mean.
:param std_hidden_sizes: Number of hidden units of each layer of the std network. Only used if
`std_share_network` is False. It defaults to the same architecture as the mean.
:param std_nonlinearity: Non-linearity used for each layer of the std network. Only used if `std_share_network`
is False. It defaults to the same non-linearity as the mean.
"""
Serializable.quick_init(self, locals())
with tf.variable_scope(name):
if optimizer is None:
if use_trust_region:
optimizer = PenaltyLbfgsOptimizer("optimizer")
else:
optimizer = LbfgsOptimizer("optimizer")
self._optimizer = optimizer
self._subsample_factor = subsample_factor
if mean_network is None:
mean_network = MLP(
name="mean_network",
input_shape=input_shape,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=None,
)
l_mean = mean_network.output_layer
if adaptive_std:
l_log_std = MLP(
name="log_std_network",
input_shape=input_shape,
input_var=mean_network.input_layer.input_var,
output_dim=output_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_nonlinearity,
output_nonlinearity=None,
).output_layer
else:
l_log_std = L.ParamLayer(
mean_network.input_layer,
num_units=output_dim,
param=tf.constant_initializer(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
LayersPowered.__init__(self, [l_mean, l_log_std])
xs_var = mean_network.input_layer.input_var
ys_var = tf.placeholder(dtype=tf.float32,
name="ys",
shape=(None, output_dim))
old_means_var = tf.placeholder(dtype=tf.float32,
name="ys",
shape=(None, output_dim))
old_log_stds_var = tf.placeholder(dtype=tf.float32,
name="old_log_stds",
shape=(None, output_dim))
x_mean_var = tf.Variable(
np.zeros((1, ) + input_shape, dtype=np.float32),
name="x_mean",
)
x_std_var = tf.Variable(
np.ones((1, ) + input_shape, dtype=np.float32),
name="x_std",
)
y_mean_var = tf.Variable(
np.zeros((1, output_dim), dtype=np.float32),
name="y_mean",
)
y_std_var = tf.Variable(
np.ones((1, output_dim), dtype=np.float32),
name="y_std",
)
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
normalized_ys_var = (ys_var - y_mean_var) / y_std_var
normalized_means_var = L.get_output(
l_mean, {mean_network.input_layer: normalized_xs_var})
normalized_log_stds_var = L.get_output(
l_log_std, {mean_network.input_layer: normalized_xs_var})
means_var = normalized_means_var * y_std_var + y_mean_var
log_stds_var = normalized_log_stds_var + tf.log(y_std_var)
normalized_old_means_var = (old_means_var - y_mean_var) / y_std_var
normalized_old_log_stds_var = old_log_stds_var - tf.log(y_std_var)
dist = self._dist = DiagonalGaussian(output_dim)
normalized_dist_info_vars = dict(mean=normalized_means_var,
log_std=normalized_log_stds_var)
mean_kl = tf.reduce_mean(
dist.kl_sym(
dict(mean=normalized_old_means_var,
log_std=normalized_old_log_stds_var),
normalized_dist_info_vars,
))
loss = -tf.reduce_mean(
dist.log_likelihood_sym(normalized_ys_var,
normalized_dist_info_vars))
self._f_predict = tensor_utils.compile_function([xs_var],
means_var)
self._f_pdists = tensor_utils.compile_function(
[xs_var], [means_var, log_stds_var])
self._l_mean = l_mean
self._l_log_std = l_log_std
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[
normalized_means_var, normalized_log_stds_var
],
)
if use_trust_region:
optimizer_args["leq_constraint"] = (mean_kl, step_size)
optimizer_args["inputs"] = [
xs_var, ys_var, old_means_var, old_log_stds_var
]
else:
optimizer_args["inputs"] = [xs_var, ys_var]
self._optimizer.update_opt(**optimizer_args)
self._use_trust_region = use_trust_region
self._name = name
self._normalize_inputs = normalize_inputs
self._normalize_outputs = normalize_outputs
self._mean_network = mean_network
self._x_mean_var = x_mean_var
self._x_std_var = x_std_var
self._y_mean_var = y_mean_var
self._y_std_var = y_std_var
def __init__(
self,
name,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
bias_transform=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=tf.nn.tanh,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=tf.identity,
mean_network=None,
std_network=None,
std_parametrization='exp',
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std: boolean indicating whether std shall be a trainable variable
:param bias_transform: boolean indicating whether bias transformation shall be added to the MLP
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:param std_parametrization: how the std should be parametrized. There are a few options:
- exp: the logarithm of the std will be stored, and applied a exponential transformation
- softplus: the std will be computed as log(1+exp(x))
:param grad_step_size: (float) the step size taken in the learner's gradient update
:param trainable_step_size: boolean indicating whether the inner grad_step_size shall be trainable
:param stop_grad: whether or not to stop the gradient through the gradient.
:param: parameter_space_noise: (boolean) whether parameter space noise shall be used when sampling from the policy
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box) or isinstance(
env_spec.action_space, BoxMAML)
obs_dim = env_spec.observation_space.flat_dim
self.action_dim = env_spec.action_space.flat_dim
self.n_hidden = len(hidden_sizes)
self.hidden_nonlinearity = hidden_nonlinearity
self.output_nonlinearity = output_nonlinearity
self.input_shape = (
None,
obs_dim,
)
self.name = name
with tf.variable_scope(self.name):
# create network
if mean_network is None:
self.all_params = create_MLP( # TODO: this should not be a method of the policy! --> helper
name="mean_network",
input_shape=self.input_shape,
output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
bias_transform=bias_transform,
)
self.input_tensor, _ = forward_MLP(
'mean_network',
self.input_shape,
self.n_hidden,
self.hidden_nonlinearity,
self.output_nonlinearity,
self.all_params,
reuse=None, # Need to run this for batch norm
bias_transform=bias_transform,
)
forward_mean = lambda x, params, is_train: forward_MLP(
'mean_network',
self.input_shape,
self.n_hidden,
self.hidden_nonlinearity,
self.output_nonlinearity,
params,
input_tensor=x,
is_training=is_train,
bias_transform=bias_transform)[1]
else:
raise NotImplementedError('Not supported.')
if std_network is not None:
raise NotImplementedError('Not supported.')
else:
if adaptive_std:
raise NotImplementedError('Not supported.')
else:
if std_parametrization == 'exp':
init_std_param = np.log(init_std)
elif std_parametrization == 'softplus':
init_std_param = np.log(np.exp(init_std) - 1)
else:
raise NotImplementedError
self.all_params['std_param'] = make_param_layer(
num_units=self.action_dim,
param=tf.constant_initializer(init_std_param),
name="output_std_param",
trainable=learn_std,
)
forward_std = lambda x, params: forward_param_layer(
x, params['std_param'])
# unify forward mean and forward std into a single function
self._forward = lambda obs, params, is_train: (forward_mean(
obs, params, is_train), forward_std(obs, params))
self.std_parametrization = std_parametrization
if std_parametrization == 'exp':
min_std_param = np.log(min_std)
elif std_parametrization == 'softplus':
min_std_param = np.log(np.exp(min_std) - 1)
else:
raise NotImplementedError
self.min_std_param = min_std_param
self._dist = DiagonalGaussian(self.action_dim)
self._cached_params = {}
super(BaseMLPPolicy, self).__init__(env_spec)
dist_info_sym = self.dist_info_sym(self.input_tensor,
dict(),
is_training=False)
mean_var = dist_info_sym["mean"]
log_std_var = dist_info_sym["log_std"]
# pre-update policy
self._cur_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor],
outputs=[mean_var, log_std_var],
)
def init_opt(self):
is_recurrent = int(self.policy.recurrent)
obs_var = self.env.observation_space.new_tensor_variable(
'obs',
extra_dims=1 + is_recurrent,
)
action_var = self.env.action_space.new_tensor_variable(
'action',
extra_dims=1 + is_recurrent,
)
advantage_var = tensor_utils.new_tensor(
name='advantage',
ndim=1 + is_recurrent,
dtype=tf.float32,
)
dist = self.policy.distribution
old_dist_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name='old_%s' % k)
for k, shape in dist.dist_info_specs
}
old_dist_info_vars_list = [
old_dist_info_vars[k] for k in dist.dist_info_keys
]
state_info_vars = {
k: tf.placeholder(tf.float32,
shape=[None] * (1 + is_recurrent) + list(shape),
name=k)
for k, shape in self.policy.state_info_specs
}
state_info_vars_list = [
state_info_vars[k] for k in self.policy.state_info_keys
]
if is_recurrent:
valid_var = tf.placeholder(tf.float32,
shape=[None, None],
name="valid")
else:
valid_var = None
dist_info_vars = self.policy.dist_info_sym(obs_var, state_info_vars)
logli = dist.log_likelihood_sym(action_var, dist_info_vars)
kl = dist.kl_sym(old_dist_info_vars, dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
if is_recurrent:
surr_obj = -tf.reduce_sum(
logli * advantage_var * valid_var) / tf.reduce_sum(valid_var)
mean_kl = tf.reduce_sum(kl * valid_var) / tf.reduce_sum(valid_var)
max_kl = tf.reduce_max(kl * valid_var)
else:
surr_obj = -tf.reduce_mean(logli * advantage_var)
mean_kl = tf.reduce_mean(kl)
max_kl = tf.reduce_max(kl)
input_list = [obs_var, action_var, advantage_var
] + state_info_vars_list
if is_recurrent:
input_list.append(valid_var)
#self.policy.set_init_surr_obj(input_list, [surr_obj]) # debugging
self.optimizer.update_opt(loss=surr_obj,
target=self.policy,
inputs=input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],
)
self.opt_info = dict(f_kl=f_kl, )
def compute_updated_dists(self, samples):
""" Compute fast gradients once per iteration and pull them out of tensorflow for sampling with the post-update policy.
"""
start = time.time()
num_tasks = len(samples)
param_keys = self.all_params.keys()
update_param_keys = param_keys
no_update_param_keys = []
sess = tf.get_default_session()
obs_list, action_list, adv_list = [], [], []
for i in range(num_tasks):
inputs = ext.extract(samples[i], 'observations', 'actions',
'advantages')
obs_list.append(inputs[0])
action_list.append(inputs[1])
adv_list.append(inputs[2])
inputs = obs_list + action_list + adv_list
# To do a second update, replace self.all_params below with the params that were used to collect the policy.
init_param_values = None
if self.all_param_vals is not None: # skip this in first iteration
init_param_values = self.get_variable_values(self.all_params)
step_size = self.step_size
for i in range(num_tasks):
if self.all_param_vals is not None: # skip this in first iteration
self.assign_params(self.all_params, self.all_param_vals[i])
if 'all_fast_params_tensor' not in dir(
self): # only enter if first iteration
# make computation graph once
self.all_fast_params_tensor = []
# compute gradients for a current task (symbolic)
for i in range(num_tasks):
# compute gradients for a current task (symbolic)
gradients = dict(
zip(
update_param_keys,
tf.gradients(self.surr_objs[i], [
self.all_params[key] for key in update_param_keys
])))
# gradient update for params of current task (symbolic)
fast_params_tensor = OrderedDict(
zip(update_param_keys, [
self.all_params[key] - step_size * gradients[key]
for key in update_param_keys
]))
# undo gradient update for no_update_params (symbolic)
for k in no_update_param_keys:
fast_params_tensor[k] = self.all_params[k]
# tensors that represent the updated params for all of the tasks (symbolic)
self.all_fast_params_tensor.append(fast_params_tensor)
# pull new param vals out of tensorflow, so gradient computation only done once ## first is the vars, second the values
# these are the updated values of the params after the gradient step
self.all_param_vals = sess.run(
self.all_fast_params_tensor,
feed_dict=dict(list(zip(self.input_list_for_grad, inputs))))
# reset parameters to original ones
if init_param_values is not None: # skip this in first iteration
self.assign_params(self.all_params, init_param_values)
# compile the _cur_f_dist with updated params
outputs = []
inputs = tf.split(self.input_tensor, num_tasks, 0)
for i in range(num_tasks):
# TODO - use a placeholder to feed in the params, so that we don't have to recompile every time.
task_inp = inputs[i]
info, _ = self.dist_info_sym(task_inp,
dict(),
all_params=self.all_param_vals[i],
is_training=False)
outputs.append([info['mean'], info['log_std']])
self._cur_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor],
outputs=outputs,
)
total_time = time.time() - start
logger.record_tabular("ComputeUpdatedDistTime", total_time)
def __init__(self,
name,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=tf.nn.tanh,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
mean_network=None,
std_network=None,
std_parametrization='exp'):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:param std_parametrization: how the std should be parametrized. There are a few options:
- exp: the logarithm of the std will be stored, and applied a exponential transformation
- softplus: the std will be computed as log(1+exp(x))
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
with tf.variable_scope(name):
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
mean_network = MLP(
name="mean_network",
input_shape=(obs_dim, ),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
)
self._mean_network = mean_network
l_mean = mean_network.output_layer
obs_var = mean_network.input_layer.input_var
if std_network is not None:
l_std_param = std_network.output_layer
else:
if adaptive_std:
std_network = MLP(
name="std_network",
input_shape=(obs_dim, ),
input_layer=mean_network.input_layer,
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=None,
)
l_std_param = std_network.output_layer
else:
if std_parametrization == 'exp':
init_std_param = np.log(init_std)
elif std_parametrization == 'softplus':
init_std_param = np.log(np.exp(init_std) - 1)
else:
raise NotImplementedError
l_std_param = L.ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=tf.constant_initializer(init_std_param),
name="output_std_param",
trainable=learn_std,
)
self.std_parametrization = std_parametrization
if std_parametrization == 'exp':
min_std_param = np.log(min_std)
elif std_parametrization == 'softplus':
min_std_param = np.log(np.exp(min_std) - 1)
else:
raise NotImplementedError
self.min_std_param = min_std_param
# mean_var, log_std_var = L.get_output([l_mean, l_std_param])
#
# if self.min_std_param is not None:
# log_std_var = tf.maximum(log_std_var, np.log(min_std))
#
# self._mean_var, self._log_std_var = mean_var, log_std_var
self._l_mean = l_mean
self._l_std_param = l_std_param
self._dist = DiagonalGaussian(action_dim)
LayersPowered.__init__(self, [l_mean, l_std_param])
super(GaussianMLPPolicy, self).__init__(env_spec)
dist_info_sym = self.dist_info_sym(
mean_network.input_layer.input_var, dict())
mean_var = dist_info_sym["mean"]
log_std_var = dist_info_sym["log_std"]
self._f_dist = tensor_utils.compile_function(
inputs=[obs_var],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
name,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
prob_network=None,
grad_step_size=1.0,
):
"""
:param env_spec: A spec for the mdp.
:param hidden_sizes: list of sizes for the fully connected hidden layers
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param prob_network: manually specified network for this policy, other network params
are ignored
:param grad_step_size: the step size taken in the learner's gradient update, sample uniformly if it is a range e.g. [0.1,1]
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Discrete)
obs_dim = env_spec.observation_space.flat_dim
self.action_dim = env_spec.action_space.n
self.n_hidden = len(hidden_sizes)
self.hidden_nonlinearity = hidden_nonlinearity
self.input_shape = (
None,
obs_dim,
)
self.step_size = grad_step_size
if prob_network is None:
self.all_params = self.create_MLP(
output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
name="prob_network",
)
self._l_obs, self._l_prob = self.forward_MLP(
'prob_network',
self.all_params,
n_hidden=len(hidden_sizes),
input_shape=(obs_dim, ),
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax,
reuse=None)
# if you want to input your own tensor.
self._forward_out = lambda x, params, is_train: self.forward_MLP(
'prob_network',
params,
n_hidden=len(hidden_sizes),
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax,
input_tensor=x,
is_training=is_train)[1]
self._init_f_prob = tensor_utils.compile_function([self._l_obs],
[self._l_prob])
self._cur_f_prob = self._init_f_prob
self._dist = Categorical(self.action_dim)
self._cached_params = {}
super(MAMLCategoricalMLPPolicy, self).__init__(env_spec)
def compute_updated_dists(self, samples):
"""
Compute fast gradients once per iteration and pull them out of tensorflow for sampling with the post-update policy.
"""
start = time.time()
num_tasks = len(samples)
param_keys = self.all_params.keys()
update_param_keys = param_keys
no_update_param_keys = []
sess = tf.get_default_session()
obs_list, action_list, adv_list, distr_list = [], [], [], []
for i in range(num_tasks):
inputs = ext.extract(samples[i],
'observations', 'actions', 'advantages', 'agent_infos')
obs_list.append(inputs[0])
action_list.append(inputs[1])
adv_list.append(inputs[2])
distr_list.extend(inputs[3][k] for k in self.distribution.dist_info_keys)
inputs = obs_list + action_list + adv_list + distr_list
# To do a second update, replace self.all_params below with the params that were used to collect the policy.
if self.first_inner_step: # skip this in first iteration
self.init_param_values = self.get_variable_values(self.all_params)
self.all_param_vals = [self.get_variable_values(self.all_params) for _ in range(num_tasks)]
if self.params_ph is None:
self.params_ph = [OrderedDict([(key, tf.placeholder(tf.float32, shape=value.shape))
for key, value in self.all_params.items()])
for _ in range(num_tasks)]
if 'all_fast_params_tensor' not in dir(self): # only enter if first iteration
# make computation graph once
self.all_fast_params_tensor = []
# compute gradients for a current task (symbolic)
for i in range(num_tasks):
# compute gradients for a current task (symbolic)
for key in self.all_params.keys():
tf.assign(self.all_params[key], self.params_ph[i][key])
gradients = dict(zip(update_param_keys, tf.gradients(self.surr_objs[i],
[self.all_params[key] for key in
update_param_keys])))
# gradient update for params of current task (symbolic)
fast_params_tensor = OrderedDict(zip(update_param_keys,
[self.all_params[key] - tf.multiply(
self.param_step_sizes[key + "_step_size"], gradients[key]) for
key in update_param_keys]))
# add step sizes to fast_params_tensor
fast_params_tensor.update(self.param_step_sizes)
# undo gradient update for no_update_params (symbolic)
for k in no_update_param_keys:
fast_params_tensor[k] = self.all_params[k]
# tensors that represent the updated params for all of the tasks (symbolic)
self.all_fast_params_tensor.append(fast_params_tensor)
# pull new param vals out of tensorflow, so gradient computation only done once ## first is the vars, second the values
# these are the updated values of the params after the gradient step
feed_dict = list(zip(self.input_list_for_grad, inputs))
feed_dict_params = list((self.params_ph[task][key], self.all_param_vals[task][key])
for task in range(num_tasks) for key in self.params_ph[0].keys())
feed_dict = dict(feed_dict + feed_dict_params)
self.all_param_vals = sess.run(self.all_fast_params_tensor, feed_dict=feed_dict)
if self.all_param_ph is None:
self.all_param_ph = [OrderedDict([(key, tf.placeholder(tf.float32, shape=value.shape))
for key, value in self.all_param_vals[0].items()])
for _ in range(num_tasks)]
# reset parameters to original ones
self.assign_params(self.all_params, self.init_param_values)
# compile the _cur_f_dist with updated params
if not self.compiled:
outputs = []
with tf.variable_scope("post_updated_policy"):
inputs = tf.split(self.input_tensor, num_tasks, 0)
for i in range(num_tasks):
# TODO - use a placeholder to feed in the params, so that we don't have to recompile every time.
task_inp = inputs[i]
info, _ = self.dist_info_sym(task_inp, dict(), all_params=self.all_param_ph[i],
is_training=False)
outputs.append([info['mean'], info['log_std']])
self.__cur_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor, self.param_noise_std_ph] + sum([list(param_ph.values())
for param_ph in self.all_param_ph], []),
outputs=outputs,
)
self.compiled = True
self._cur_f_dist = self.__cur_f_dist
self.first_inner_step = False
def compute_updated_dists(self, samples):
""" Compute fast gradients once and pull them out of tensorflow for sampling.
"""
num_tasks = len(samples)
param_keys = self.all_params.keys()
sess = tf.get_default_session()
obs_list, action_list, adv_list = [], [], []
for i in range(num_tasks):
inputs = ext.extract(samples[i], 'observations', 'actions',
'advantages')
obs_list.append(inputs[0])
action_list.append(inputs[1])
adv_list.append(inputs[2])
inputs = obs_list + action_list + adv_list
# To do a second update, replace self.all_params below with the params that were used to collect the policy.
init_param_values = None
if self.all_param_vals is not None:
init_param_values = self.get_variable_values(self.all_params)
step_size = self.step_size
for i in range(num_tasks):
if self.all_param_vals is not None:
self.assign_params(self.all_params, self.all_param_vals[i])
if 'all_fast_params_tensor' not in dir(self):
# make computation graph once
self.all_fast_params_tensor = []
for i in range(num_tasks):
gradients = dict(
zip(
param_keys,
tf.gradients(
self.surr_objs[i],
[self.all_params[key] for key in param_keys])))
fast_params_tensor = dict(
zip(param_keys, [
self.all_params[key] - step_size * gradients[key]
for key in param_keys
]))
self.all_fast_params_tensor.append(fast_params_tensor)
# pull new param vals out of tensorflow, so gradient computation only done once
self.all_param_vals = sess.run(
self.all_fast_params_tensor,
feed_dict=dict(list(zip(self.input_list_for_grad, inputs))))
if init_param_values is not None:
self.assign_params(self.all_params, init_param_values)
outputs = []
inputs = tf.split(0, num_tasks, self._l_obs)
for i in range(num_tasks):
# TODO - use a placeholder to feed in the params, so that we don't have to recompile every time.
task_inp = inputs[i]
info, _ = self.dist_info_sym(task_inp,
dict(),
all_params=self.all_param_vals[i],
is_training=False)
outputs.append([info['prob']])
self._cur_f_prob = tensor_utils.compile_function(
inputs=[self._l_obs],
outputs=outputs,
)
def __init__(
self,
name,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=tf.nn.tanh,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=tf.identity,
mean_network=None,
std_network=None,
std_parametrization='exp'
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std:
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:param std_parametrization: how the std should be parametrized. There are a few options:
- exp: the logarithm of the std will be stored, and applied a exponential transformation
- softplus: the std will be computed as log(1+exp(x))
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
# create network
if mean_network is None:
self.mean_params = mean_params = self.create_MLP(
name="mean_network",
input_shape=(None, obs_dim,),
output_dim=action_dim,
hidden_sizes=hidden_sizes,
)
input_tensor, mean_tensor = self.forward_MLP('mean_network', mean_params, n_hidden=len(hidden_sizes),
input_shape=(obs_dim,),
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
reuse=None # Needed for batch norm
)
# if you want to input your own thing.
self._forward_mean = lambda x, is_train: self.forward_MLP('mean_network', mean_params, n_hidden=len(hidden_sizes),
hidden_nonlinearity=hidden_nonlinearity, output_nonlinearity=output_nonlinearity, input_tensor=x, is_training=is_train)[1]
else:
raise NotImplementedError('Chelsea does not support this.')
if std_network is not None:
raise NotImplementedError('Minimal Gaussian MLP does not support this.')
else:
if adaptive_std:
# NOTE - this branch isn't tested
raise NotImplementedError('Minimal Gaussian MLP doesnt have a tested version of this.')
self.std_params = std_params = self.create_MLP(
name="std_network",
input_shape=(None, obs_dim,),
output_dim=action_dim,
hidden_sizes=std_hidden_sizes,
)
# if you want to input your own thing.
self._forward_std = lambda x: self.forward_MLP('std_network', std_params, n_hidden=len(hidden_sizes),
hidden_nonlinearity=std_hidden_nonlinearity,
output_nonlinearity=tf.identity,
input_tensor=x)[1]
else:
if std_parametrization == 'exp':
init_std_param = np.log(init_std)
elif std_parametrization == 'softplus':
init_std_param = np.log(np.exp(init_std) - 1)
else:
raise NotImplementedError
self.std_params = make_param_layer(
num_units=action_dim,
param=tf.constant_initializer(init_std_param),
name="output_std_param",
trainable=learn_std,
)
self._forward_std = lambda x: forward_param_layer(x, self.std_params)
self.std_parametrization = std_parametrization
if std_parametrization == 'exp':
min_std_param = np.log(min_std)
elif std_parametrization == 'softplus':
min_std_param = np.log(np.exp(min_std) - 1)
else:
raise NotImplementedError
self.min_std_param = min_std_param
self._dist = DiagonalGaussian(action_dim)
self._cached_params = {}
super(GaussianMLPPolicy, self).__init__(env_spec)
dist_info_sym = self.dist_info_sym(input_tensor, dict(), is_training=False)
mean_var = dist_info_sym["mean"]
log_std_var = dist_info_sym["log_std"]
self._f_dist = tensor_utils.compile_function(
inputs=[input_tensor],
outputs=[mean_var, log_std_var],
)
def __init__(
self,
name,
env_spec,
hidden_sizes=(32, 32),
learn_std=True,
num_tasks=1,
init_std=1.0,
adaptive_std=False,
bias_transform=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
std_hidden_nonlinearity=tf.nn.tanh,
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=tf.identity,
mean_network=None,
std_network=None,
std_parametrization='exp',
grad_step_size=0.1,
trainable_step_size=False,
stop_grad=False,
):
"""
:param env_spec:
:param hidden_sizes: list of sizes for the fully-connected hidden layers
:param learn_std: Is std trainable
:param init_std: Initial std
:param adaptive_std: boolean indicating whether std shall be a trainable variable
:param bias_transform: boolean indicating whether bias transformation shall be added to the MLP
:param std_share_network:
:param std_hidden_sizes: list of sizes for the fully-connected layers for std
:param min_std: whether to make sure that the std is at least some threshold value, to avoid numerical issues
:param std_hidden_nonlinearity:
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:param output_nonlinearity: nonlinearity for the output layer
:param mean_network: custom network for the output mean
:param std_network: custom network for the output log std
:param std_parametrization: how the std should be parametrized. There are a few options:
- exp: the logarithm of the std will be stored, and applied a exponential transformation
- softplus: the std will be computed as log(1+exp(x))
:param grad_step_size: (float) the step size taken in the learner's gradient update
:param trainable_step_size: boolean indicating whether the inner grad_step_size shall be trainable
:param stop_grad: whether or not to stop the gradient through the gradient.
:param: parameter_space_noise: (boolean) whether parameter space noise shall be used when sampling from the policy
"""
Serializable.quick_init(self, locals())
BaseMLPPolicy.__init__(
self,
name,
env_spec,
hidden_sizes=hidden_sizes,
learn_std=learn_std,
init_std=init_std,
adaptive_std=adaptive_std,
bias_transform=bias_transform,
std_share_network=std_share_network,
std_hidden_sizes=std_hidden_sizes,
min_std=min_std,
std_hidden_nonlinearity=std_hidden_nonlinearity,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
mean_network=mean_network,
std_network=std_network,
)
self.stop_grad = stop_grad
self.num_tasks = num_tasks
self.all_fast_params_tensor = []
self._all_param_gradients = []
self.all_param_vals = None #[self.get_variable_values(self.all_params)] * num_tasks
self.init_param_vals = None
self.param_step_sizes = {}
self.grad_step_size = grad_step_size
self.trainable_step_size = trainable_step_size
self._update_input_keys = ['observations', 'actions', 'advantages']
with tf.variable_scope(self.name):
# Create placeholders for the param weights of the different tasks
self.all_params_ph = [
OrderedDict([(key, tf.placeholder(tf.float32,
shape=value.shape))
for key, value in self.all_params.items()])
for _ in range(num_tasks)
]
# Create the variables for the inner learning rate
for key, param in self.all_params.items():
shape = param.get_shape().as_list()
init_stepsize = np.ones(shape,
dtype=np.float32) * self.grad_step_size
self.param_step_sizes[key + "_step_size"] = tf.Variable(
initial_value=init_stepsize,
name='step_size_%s' % key,
dtype=tf.float32,
trainable=self.trainable_step_size)
# compile the _cur_f_dist with updated params
outputs = []
with tf.variable_scope("post_updated_policy"):
inputs = tf.split(self.input_tensor, self.num_tasks, 0)
for i in range(self.num_tasks):
task_inp = inputs[i]
dist_info, _ = self.dist_info_sym(
task_inp,
dict(),
all_params=self.all_params_ph[i],
is_training=False)
outputs.append([dist_info['mean'], dist_info['log_std']])
# TODO: Set a different name for this _cur_f_dist, so you can obtain actions w/o needing the params if
# TODO: you aren't using the get_actions_batch (it'll be needed at test time when you are just evaluating
# TODO: in one task)
self._batch_cur_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor] + sum([
list(param_task_ph.values())
for param_task_ph in self.all_params_ph
], []), # All the parameter values of the policy
outputs=outputs,
)
def __init__(
self,
name,
input_shape,
output_dim,
network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
output_nonlinearity=None,
optimizer=None,
normalize_inputs=True,
):
"""
:param input_shape: Shape of the input data.
:param output_dim: Dimension of output.
:param hidden_sizes: Number of hidden units of each layer of the mean network.
:param hidden_nonlinearity: Non-linearity used for each layer of the mean network.
:param optimizer: Optimizer for minimizing the negative log-likelihood.
"""
Serializable.quick_init(self, locals())
with tf.variable_scope(name):
if optimizer is None:
optimizer = LbfgsOptimizer(name="optimizer")
self.output_dim = output_dim
self.optimizer = optimizer
if network is None:
network = MLP(input_shape=input_shape,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
name="network")
l_out = network.output_layer
LayersPowered.__init__(self, [l_out])
xs_var = network.input_layer.input_var
ys_var = tf.placeholder(dtype=tf.float32,
shape=[None, output_dim],
name="ys")
x_mean_var = tf.get_variable(name="x_mean",
shape=(1, ) + input_shape,
initializer=tf.constant_initializer(
0., dtype=tf.float32))
x_std_var = tf.get_variable(name="x_std",
shape=(1, ) + input_shape,
initializer=tf.constant_initializer(
1., dtype=tf.float32))
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
fit_ys_var = L.get_output(l_out,
{network.input_layer: normalized_xs_var})
loss = -tf.reduce_mean(tf.square(fit_ys_var - ys_var))
self.f_predict = tensor_utils.compile_function([xs_var],
fit_ys_var)
optimizer_args = dict(
loss=loss,
target=self,
network_outputs=[fit_ys_var],
)
optimizer_args["inputs"] = [xs_var, ys_var]
self.optimizer.update_opt(**optimizer_args)
self.name = name
self.l_out = l_out
self.normalize_inputs = normalize_inputs
self.x_mean_var = x_mean_var
self.x_std_var = x_std_var
def init_opt(self):
# TODO Commented out all KL stuff for now, since it is only used for logging
# To see how it can be turned on, see maml_npo.py
is_recurrent = int(self.policy.recurrent)
assert not is_recurrent # not supported right now.
dist = self.policy.distribution
old_dist_info_vars, old_dist_info_vars_list = [], []
for i in range(self.meta_batch_size):
old_dist_info_vars.append({
k: tf.placeholder(tf.float32,
shape=[None] + list(shape),
name='old_%s_%s' % (i, k))
for k, shape in dist.dist_info_specs
})
old_dist_info_vars_list += [
old_dist_info_vars[i][k] for k in dist.dist_info_keys
]
state_info_vars, state_info_vars_list = {}, []
all_surr_objs, input_list = [], []
new_params = None
for j in range(self.num_grad_updates):
obs_vars, action_vars, adv_vars = self.make_vars(str(j))
surr_objs = []
cur_params = new_params
new_params = []
for i in range(self.meta_batch_size):
if j == 0:
dist_info_vars, params = self.policy.dist_info_sym(
obs_vars[i],
state_info_vars,
all_params=self.policy.all_params)
else:
dist_info_vars, params = self.policy.updated_dist_info_sym(
i,
all_surr_objs[-1][i],
obs_vars[i],
params_dict=cur_params[i])
new_params.append(params)
logli = dist.log_likelihood_sym(action_vars[i], dist_info_vars)
# formulate as a minimization problem
# The gradient of the surrogate objective is the policy gradient
surr_objs.append(-tf.reduce_mean(logli * adv_vars[i]))
input_list += obs_vars + action_vars + adv_vars + state_info_vars_list
if j == 0:
# For computing the fast update for sampling
self.policy.set_init_surr_obj(input_list, surr_objs)
init_input_list = input_list
all_surr_objs.append(surr_objs)
obs_vars, action_vars, adv_vars = self.make_vars('test')
surr_objs = []
kls = []
for i in range(self.meta_batch_size):
dist_info_vars, _ = self.policy.updated_dist_info_sym(
i,
all_surr_objs[-1][i],
obs_vars[i],
params_dict=new_params[i])
logli = dist.log_likelihood_sym(action_vars[i], dist_info_vars)
surr_objs.append(-tf.reduce_mean(logli * adv_vars[i]))
kls.append(dist.kl_sym(old_dist_info_vars[i], dist_info_vars))
surr_obj = tf.reduce_mean(tf.stack(surr_objs, 0))
mean_kl = tf.reduce_mean(tf.concat(kls, 0))
max_kl = tf.reduce_max(tf.concat(kls, 0))
input_list += obs_vars + action_vars + adv_vars
if self.use_maml:
self.optimizer.update_opt(loss=surr_obj,
target=self.policy,
inputs=input_list)
else: # baseline method of just training initial policy
self.optimizer.update_opt(loss=tf.reduce_mean(
tf.stack(all_surr_objs[0], 0)),
target=self.policy,
inputs=init_input_list)
f_kl = tensor_utils.compile_function(
inputs=input_list + old_dist_info_vars_list,
outputs=[mean_kl, max_kl],
)
self.opt_info = dict(f_kl=f_kl, )
def __init__(
self,
name,
env_spec,
hidden_dim=32,
feature_network=None,
state_include_action=True,
hidden_nonlinearity=tf.tanh,
gru_layer_cls=L.GRULayer,
):
"""
:param env_spec: A spec for the env.
:param hidden_dim: dimension of hidden layer
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:return:
"""
with tf.variable_scope(name):
assert isinstance(env_spec.action_space, Discrete)
Serializable.quick_init(self, locals())
super(CategoricalGRUPolicy, self).__init__(env_spec)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
if state_include_action:
input_dim = obs_dim + action_dim
else:
input_dim = obs_dim
l_input = L.InputLayer(shape=(None, None, input_dim), name="input")
if feature_network is None:
feature_dim = input_dim
l_flat_feature = None
l_feature = l_input
else:
feature_dim = feature_network.output_layer.output_shape[-1]
l_flat_feature = feature_network.output_layer
l_feature = L.OpLayer(
l_flat_feature,
extras=[l_input],
name="reshape_feature",
op=lambda flat_feature, input: tf.reshape(
flat_feature,
tf.pack([
tf.shape(input)[0],
tf.shape(input)[1], feature_dim
])),
shape_op=lambda _, input_shape:
(input_shape[0], input_shape[1], feature_dim))
prob_network = GRUNetwork(input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=env_spec.action_space.n,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax,
gru_layer_cls=gru_layer_cls,
name="prob_network")
self.prob_network = prob_network
self.feature_network = feature_network
self.l_input = l_input
self.state_include_action = state_include_action
flat_input_var = tf.placeholder(dtype=tf.float32,
shape=(None, input_dim),
name="flat_input")
if feature_network is None:
feature_var = flat_input_var
else:
feature_var = L.get_output(
l_flat_feature,
{feature_network.input_layer: flat_input_var})
self.f_step_prob = tensor_utils.compile_function(
[
flat_input_var,
prob_network.step_prev_hidden_layer.input_var
],
L.get_output([
prob_network.step_output_layer,
prob_network.step_hidden_layer
], {prob_network.step_input_layer: feature_var}))
self.input_dim = input_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.prev_actions = None
self.prev_hiddens = None
self.dist = RecurrentCategorical(env_spec.action_space.n)
out_layers = [prob_network.output_layer]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
def update_opt(self,
loss,
target,
leq_constraint,
inputs,
extra_inputs=None,
constraint_name="constraint",
*args,
**kwargs):
"""
:param loss: Symbolic expression for the loss function.
:param target: A parameterized object to optimize over. It should implement methods of the
:class:`rllab_maml.core.paramerized.Parameterized` class.
:param leq_constraint: A constraint provided as a tuple (f, epsilon), of the form f(*inputs) <= epsilon.
:param inputs: A list of symbolic variables as inputs, which could be subsampled if needed. It is assumed
that the first dimension of these inputs should correspond to the number of data points
:param extra_inputs: A list of symbolic variables as extra inputs which should not be subsampled
:return: No return value.
"""
inputs = tuple(inputs)
if extra_inputs is None:
extra_inputs = tuple()
else:
extra_inputs = tuple(extra_inputs)
constraint_term, constraint_value = leq_constraint
params = target.get_params(trainable=True)
grads = tf.gradients(loss, xs=params)
for idx, (grad, param) in enumerate(zip(grads, params)):
if grad is None:
grads[idx] = tf.zeros_like(param)
flat_grad = tensor_utils.flatten_tensor_variables(grads)
# f=KL-divergence and target is policy
self._hvp_approach.update_opt(f=constraint_term,
target=target,
inputs=inputs + extra_inputs,
reg_coeff=self._reg_coeff)
self._target = target
self._max_constraint_val = constraint_value
self._constraint_name = constraint_name
self._opt_fun = ext.lazydict(
f_loss=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=loss,
log_name="f_loss",
),
f_grad=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=flat_grad,
log_name="f_grad",
),
f_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=constraint_term,
log_name="constraint",
),
f_loss_constraint=lambda: tensor_utils.compile_function(
inputs=inputs + extra_inputs,
outputs=[loss, constraint_term],
log_name="f_loss_constraint",
),
)
def __init__(
self,
name,
input_shape,
output_dim,
prob_network=None,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.tanh,
optimizer=None,
tr_optimizer=None,
use_trust_region=True,
step_size=0.01,
normalize_inputs=True,
no_initial_trust_region=True,
):
"""
:param input_shape: Shape of the input data.
:param output_dim: Dimension of output.
:param hidden_sizes: Number of hidden units of each layer of the mean network.
:param hidden_nonlinearity: Non-linearity used for each layer of the mean network.
:param optimizer: Optimizer for minimizing the negative log-likelihood.
:param use_trust_region: Whether to use trust region constraint.
:param step_size: KL divergence constraint for each iteration
"""
Serializable.quick_init(self, locals())
with tf.variable_scope(name):
if optimizer is None:
optimizer = LbfgsOptimizer(name="optimizer")
if tr_optimizer is None:
tr_optimizer = ConjugateGradientOptimizer()
self.output_dim = output_dim
self.optimizer = optimizer
self.tr_optimizer = tr_optimizer
if prob_network is None:
prob_network = MLP(input_shape=input_shape,
output_dim=output_dim,
hidden_sizes=hidden_sizes,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=tf.nn.softmax,
name="prob_network")
l_prob = prob_network.output_layer
LayersPowered.__init__(self, [l_prob])
xs_var = prob_network.input_layer.input_var
ys_var = tf.placeholder(dtype=tf.float32,
shape=[None, output_dim],
name="ys")
old_prob_var = tf.placeholder(dtype=tf.float32,
shape=[None, output_dim],
name="old_prob")
x_mean_var = tf.get_variable(name="x_mean",
shape=(1, ) + input_shape,
initializer=tf.constant_initializer(
0., dtype=tf.float32))
x_std_var = tf.get_variable(name="x_std",
shape=(1, ) + input_shape,
initializer=tf.constant_initializer(
1., dtype=tf.float32))
normalized_xs_var = (xs_var - x_mean_var) / x_std_var
prob_var = L.get_output(
l_prob, {prob_network.input_layer: normalized_xs_var})
old_info_vars = dict(prob=old_prob_var)
info_vars = dict(prob=prob_var)
dist = self._dist = Categorical(output_dim)
mean_kl = tf.reduce_mean(dist.kl_sym(old_info_vars, info_vars))
loss = -tf.reduce_mean(dist.log_likelihood_sym(ys_var, info_vars))
predicted = tensor_utils.to_onehot_sym(
tf.argmax(prob_var, dimension=1), output_dim)
self.prob_network = prob_network
self.f_predict = tensor_utils.compile_function([xs_var], predicted)
self.f_prob = tensor_utils.compile_function([xs_var], prob_var)
self.l_prob = l_prob
self.optimizer.update_opt(loss=loss,
target=self,
network_outputs=[prob_var],
inputs=[xs_var, ys_var])
self.tr_optimizer.update_opt(loss=loss,
target=self,
network_outputs=[prob_var],
inputs=[xs_var, ys_var, old_prob_var],
leq_constraint=(mean_kl, step_size))
self.use_trust_region = use_trust_region
self.name = name
self.normalize_inputs = normalize_inputs
self.x_mean_var = x_mean_var
self.x_std_var = x_std_var
self.first_optimized = not no_initial_trust_region
def __init__(
self,
name,
env_spec,
hidden_dim=32,
feature_network=None,
state_include_action=True,
hidden_nonlinearity=tf.tanh,
learn_std=True,
init_std=1.0,
output_nonlinearity=None,
lstm_layer_cls=L.LSTMLayer,
):
"""
:param env_spec: A spec for the env.
:param hidden_dim: dimension of hidden layer
:param hidden_nonlinearity: nonlinearity used for each hidden layer
:return:
"""
with tf.variable_scope(name):
Serializable.quick_init(self, locals())
super(GaussianLSTMPolicy, self).__init__(env_spec)
obs_dim = env_spec.observation_space.flat_dim
action_dim = env_spec.action_space.flat_dim
if state_include_action:
input_dim = obs_dim + action_dim
else:
input_dim = obs_dim
l_input = L.InputLayer(shape=(None, None, input_dim), name="input")
if feature_network is None:
feature_dim = input_dim
l_flat_feature = None
l_feature = l_input
else:
feature_dim = feature_network.output_layer.output_shape[-1]
l_flat_feature = feature_network.output_layer
l_feature = L.OpLayer(
l_flat_feature,
extras=[l_input],
name="reshape_feature",
op=lambda flat_feature, input: tf.reshape(
flat_feature,
tf.pack([
tf.shape(input)[0],
tf.shape(input)[1], feature_dim
])),
shape_op=lambda _, input_shape:
(input_shape[0], input_shape[1], feature_dim))
mean_network = LSTMNetwork(input_shape=(feature_dim, ),
input_layer=l_feature,
output_dim=action_dim,
hidden_dim=hidden_dim,
hidden_nonlinearity=hidden_nonlinearity,
output_nonlinearity=output_nonlinearity,
lstm_layer_cls=lstm_layer_cls,
name="mean_network")
l_log_std = L.ParamLayer(
mean_network.input_layer,
num_units=action_dim,
param=tf.constant_initializer(np.log(init_std)),
name="output_log_std",
trainable=learn_std,
)
l_step_log_std = L.ParamLayer(
mean_network.step_input_layer,
num_units=action_dim,
param=l_log_std.param,
name="step_output_log_std",
trainable=learn_std,
)
self.mean_network = mean_network
self.feature_network = feature_network
self.l_input = l_input
self.state_include_action = state_include_action
flat_input_var = tf.placeholder(dtype=tf.float32,
shape=(None, input_dim),
name="flat_input")
if feature_network is None:
feature_var = flat_input_var
else:
feature_var = L.get_output(
l_flat_feature,
{feature_network.input_layer: flat_input_var})
self.f_step_mean_std = tensor_utils.compile_function(
[
flat_input_var,
mean_network.step_prev_hidden_layer.input_var,
mean_network.step_prev_cell_layer.input_var
],
L.get_output([
mean_network.step_output_layer, l_step_log_std,
mean_network.step_hidden_layer,
mean_network.step_cell_layer
], {mean_network.step_input_layer: feature_var}))
self.l_log_std = l_log_std
self.input_dim = input_dim
self.action_dim = action_dim
self.hidden_dim = hidden_dim
self.prev_actions = None
self.prev_hiddens = None
self.prev_cells = None
self.dist = RecurrentDiagonalGaussian(action_dim)
out_layers = [mean_network.output_layer, l_log_std]
if feature_network is not None:
out_layers.append(feature_network.output_layer)
LayersPowered.__init__(self, out_layers)
