def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_W_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_W_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_W_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx
)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_W_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output"
)
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = ext.compile_function([l_obs.input_var], action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
python类HeUniform()的实例源码
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_W_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_W_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_W_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx
)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_W_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output"
)
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = ext.compile_function([l_obs.input_var], action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_W_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_W_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_W_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx
)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_W_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output"
)
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = ext.compile_function([l_obs.input_var], action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_W_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_W_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_W_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx
)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_W_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output"
)
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = ext.compile_function([l_obs.input_var], action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
def __init__(
self,
env_spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=NL.rectify,
hidden_W_init=LI.HeUniform(),
hidden_b_init=LI.Constant(0.),
output_nonlinearity=NL.tanh,
output_W_init=LI.Uniform(-3e-3, 3e-3),
output_b_init=LI.Uniform(-3e-3, 3e-3),
bn=False):
Serializable.quick_init(self, locals())
l_obs = L.InputLayer(shape=(None, env_spec.observation_space.flat_dim))
l_hidden = l_obs
if bn:
l_hidden = batch_norm(l_hidden)
for idx, size in enumerate(hidden_sizes):
l_hidden = L.DenseLayer(
l_hidden,
num_units=size,
W=hidden_W_init,
b=hidden_b_init,
nonlinearity=hidden_nonlinearity,
name="h%d" % idx
)
if bn:
l_hidden = batch_norm(l_hidden)
l_output = L.DenseLayer(
l_hidden,
num_units=env_spec.action_space.flat_dim,
W=output_W_init,
b=output_b_init,
nonlinearity=output_nonlinearity,
name="output"
)
# Note the deterministic=True argument. It makes sure that when getting
# actions from single observations, we do not update params in the
# batch normalization layers
action_var = L.get_output(l_output, deterministic=True)
self._output_layer = l_output
self._f_actions = ext.compile_function([l_obs.input_var], action_var)
super(DeterministicMLPPolicy, self).__init__(env_spec)
LasagnePowered.__init__(self, [l_output])
