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
self.all_param_vals = False
print('obs_dim ', obs_dim, flush=True)
# 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,
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,
init_std=1.0,
adaptive_std=False,
std_share_network=False,
std_hidden_sizes=(32, 32),
min_std=1e-6,
max_std=1000.0,
std_modifier=1.0,
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=1.0,
stop_grad=False,
extra_input_dim=0,
# metalearn_baseline=False,
input_img_shape=(32, 64, 3),
conv_hidden_sizes=(),
conv_filters=[16, 16, 16, 16],
conv_filter_sizes=[5, 5, 5, 5],
conv_strides=[2, 2, 2, 1],
conv_pads=['SAME', 'SAME', 'SAME', 'SAME'],
conv_output_dim=32,
):
"""
: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))
: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]
:param stop_grad: whether or not to stop the gradient through the gradient.
:return:
"""
Serializable.quick_init(self, locals())
#assert isinstance(env_spec.action_space, Box)
assert len(input_img_shape) > 0, "input_img_shape needs to be bigger"
obs_dim = env_spec.observation_space.flat_dim - np.prod(
input_img_shape)
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_img_shape = input_img_shape
self.input_shape = (
None,
obs_dim + extra_input_dim + conv_output_dim,
)
self.input_total_shape = (
None,
np.prod(self.input_img_shape) + obs_dim + extra_input_dim,
)
# self.input_shape = self.input_total_shape
print("debug432", self.input_img_shape, self.input_shape,
self.input_total_shape)
self.step_size = grad_step_size
self.stop_grad = stop_grad
# self.metalearn_baseline = metalearn_baseline
if type(self.step_size) == list:
raise NotImplementedError('removing this since it didnt work well')
self.cnn = None
# create network
if mean_network is None:
self.all_params = self.create_MLP_params( # TODO: this should not be a method of the policy! --> helper
name="mean_network",
output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
)
self.input_tensor, _ = self.forward_CNN_MLP(
name='mean_network',
all_params=self.all_params,
conv_filters=conv_filters,
conv_filter_sizes=conv_filter_sizes,
conv_strides=conv_strides,
conv_pads=conv_pads,
conv_output_dim=conv_output_dim,
conv_hidden_sizes=conv_hidden_sizes,
input_tensor=None,
reuse=None # Need to run this for batch norm
)
forward_mean = lambda x, params, is_train: self.forward_CNN_MLP(
name='mean_network',
all_params=params,
conv_filters=conv_filters,
conv_filter_sizes=conv_filter_sizes,
conv_strides=conv_strides,
conv_pads=conv_pads,
conv_output_dim=conv_output_dim,
conv_hidden_sizes=conv_hidden_sizes,
input_tensor=x,
is_training=is_train)[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'])
self.all_param_vals = None
# 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)
max_std_param = np.log(max_std)
elif std_parametrization == 'softplus':
min_std_param = np.log(np.exp(min_std) - 1)
max_std_param = np.log(np.exp(max_std) - 1)
else:
raise NotImplementedError
self.min_std_param = min_std_param # TODO: change these to min_std_param_raw
self.max_std_param = max_std_param
self.std_modifier = np.float64(std_modifier)
#print("initializing max_std debug4", self.min_std_param, self.max_std_param)
self._dist = DiagonalGaussian(self.action_dim)
self._cached_params = {}
super(MAMLGaussianConvMLPPolicy, 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._init_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor],
outputs=[mean_var, log_std_var],
)
self._cur_f_dist = self._init_f_dist
self._cur_f_dist_cnn = tensor_utils.compile_function(
inputs=[self.input_tensor],
outputs=L.get_output(self.cnn._l_out))
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',
grad_step_size=1.0, #
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:
: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输出日志标准的自定义网络
: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: the step size taken in the learner's gradient update, sample uniformly if it is a range e.g. [0.1,1]
在学习者的梯度更新中采用的步长，如果它是一个范围，则均匀地采样，例如
:param stop_grad: whether or not to stop the gradient through the gradient.是否通过渐变停止渐变。
:return:
"""
Serializable.quick_init(self, locals())
assert isinstance(env_spec.action_space, Box)
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.step_size = grad_step_size
self.stop_grad = stop_grad
if type(self.step_size) == list:
raise NotImplementedError('removing this since it didnt work well')
# create network创建网络
if mean_network is None:
self.all_params = self.create_MLP( # TODO: this should not be a method of the policy! --> helper
name="mean_network",
output_dim=self.action_dim,
hidden_sizes=hidden_sizes,
)
self.input_tensor, _ = self.forward_MLP('mean_network', self.all_params,
reuse=None # Need to run this for batch norm需要运行此批处理规范
)
forward_mean = lambda x, params, is_train: self.forward_MLP('mean_network', params,
input_tensor=x, is_training=is_train)[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'])
self.all_param_vals = None
# unify forward mean and forward std into a single function统一前向均值并将std转换为单个函数
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(MAMLGaussianMLPPolicy, 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更新前的政策
self._init_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor],
outputs=[mean_var, log_std_var],
)
self._cur_f_dist = self._init_f_dist
def __init__(
self,
env_spec,
subsample_factor=1.,
num_seq_inputs=1,
learning_rate=0.01,
algo_discount=0.99,
repeat=30,
repeat_sym=30,
momentum=0.5,
hidden_sizes=(32, 32),
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.identity,
init_meta_constant=0.0,
normalize_inputs=True,
normalize_outputs=True,
extra_input_dim=0,
):
Serializable.quick_init(self, locals())
self.env_spec = env_spec
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,
2 * (obs_dim + extra_input_dim) + 3,
)
self.input_to_discard = extra_input_dim #multiply by 0 the last extra_input_dim elements of obs vector
self.obs_mask = np.array([1.0] * obs_dim + [0.] * extra_input_dim)
self.learning_rate = learning_rate
self.algo_discount = algo_discount
self.max_path_length = 100
self._normalize_inputs = normalize_inputs
self._normalize_outputs = normalize_outputs
#
# self._enh_obs_mean_var = tf.Variable(
# tf.zeros((1,) + self.input_shape, dtype=tf.float32),
# name="enh_obs_mean",
# trainable=False
# )
# self._enh_obs_std_var = tf.Variable(
# tf.ones((1,) + self.input_shape, dtype=tf.float32),
# name="enh_obs_std",
# trainable=False
# )
self.output_dim = 1
self._ret_mean_var = tf.Variable(tf.zeros((self.output_dim),
dtype=tf.float32),
name="ret_mean",
trainable=False)
self._ret_std_var = tf.Variable(tf.ones((self.output_dim),
dtype=tf.float32),
name="ret_std",
trainable=False)
self.all_params = self.create_MLP(
name="mean_baseline_network",
output_dim=1,
hidden_sizes=hidden_sizes,
)
self.input_tensor, _ = self.forward_MLP('mean_baseline_network',
self.all_params,
reuse=None)
print("debug, input_tensor", self.input_tensor)
self.normalized_input_tensor = normalize_sym(self.input_tensor)
self.all_params['meta_constant'] = make_param_layer(
num_units=1,
param=tf.constant_initializer(init_meta_constant),
name="output_bas_meta_constant",
trainable=True,
)
forward_mean = lambda x, params, is_train: self.forward_MLP(
'mean_baseline_network',
all_params=params,
input_tensor=x,
is_training=is_train)[1]
forward_meta_constant = lambda x, params: forward_param_layer(
x, params['meta_constant'])
self._forward = lambda normalized_enh_obs, params, is_train: (
forward_mean(normalized_enh_obs, params, is_train),
forward_meta_constant(normalized_enh_obs, params))
self.all_param_vals = None
# sess = tf.get_default_session()
# if sess is None:
# sess = tf.Session()
# sess.run(tf.global_variables_initializer())
self.learning_rate_per_param = OrderedDict(
zip(self.all_params.keys(), [
tf.Variable(self.learning_rate *
tf.ones(tf.shape(self.all_params[key])),
trainable=False) for key in self.all_params.keys()
]))
# sess.run(tf.global_variables_initializer())
self.accumulation = OrderedDict(
zip(self.all_params.keys(), [
tf.Variable(tf.zeros(tf.shape(self.all_params[key])),
trainable=False) for key in self.all_params.keys()
]))
# self.last_grad = OrderedDict(zip(self.all_params.keys(),[tf.Variable(tf.zeros_like(self.all_params[key]), trainable=False) for key in self.all_params.keys()]))
# self._dist = DiagonalGaussian(1)
self._cached_params = {}
super(MAMLGaussianMLPBaseline, self).__init__(env_spec)
normalized_predict_sym = self.normalized_predict_sym(
normalized_enh_obs_vars=self.normalized_input_tensor)
mean_var = normalized_predict_sym[
'mean'] * self._ret_std_var + self._ret_mean_var
meta_constant_var = normalized_predict_sym['meta_constant']
self._init_f_dist = tensor_utils.compile_function(
inputs=[self.input_tensor],
outputs=[mean_var, meta_constant_var],
)
self._cur_f_dist = self._init_f_dist
self.initialized = 30
self.lr_mult = 1.0
self.repeat = repeat
self.repeat_sym = repeat_sym
self.momentum = momentum