class CustomNetwork(LayerBasedNetwork):
def __init__(self, scope='layerbased-network', summary_labels=()):
super(CustomNetwork, self).__init__(scope=scope, summary_labels=summary_labels)
self.layer01 = Dense(size=32, scope='state0-1')
self.add_layer(layer=self.layer01)
self.layer02 = Dense(size=32, scope='state0-2')
self.add_layer(layer=self.layer02)
self.layer11 = Dense(size=32, scope='state1-1')
self.add_layer(layer=self.layer11)
self.layer12 = Dense(size=32, scope='state1-2')
self.add_layer(layer=self.layer12)
self.layer21 = Dense(size=32, scope='state2-1')
self.add_layer(layer=self.layer21)
self.layer22 = Dense(size=32, scope='state2-2')
self.add_layer(layer=self.layer22)
self.layer31 = Dense(size=32, scope='state3-1')
self.add_layer(layer=self.layer31)
self.layer32 = Dense(size=32, scope='state3-2')
self.add_layer(layer=self.layer32)
def tf_apply(self, x, internals, update, return_internals=False):
x0 = self.layer02.apply(x=self.layer01.apply(x=x['state0'], update=update), update=update)
x1 = self.layer12.apply(x=self.layer11.apply(x=x['state1'], update=update), update=update)
x2 = self.layer22.apply(x=self.layer21.apply(x=x['state2'], update=update), update=update)
x3 = self.layer32.apply(x=self.layer31.apply(x=x['state3'], update=update), update=update)
x = x0 * x1 * x2 * x3
return (x, list()) if return_internals else x
def __init__(self, scope='ddpg-critic-network', summary_labels=(), size_t0=400, size_t1=300):
super(DDPGCriticNetwork, self).__init__(scope=scope, summary_labels=summary_labels)
self.t0l = Linear(size=size_t0, scope='linear0')
self.t0b = TFLayer(layer='batch_normalization', scope='batchnorm0', center=True, scale=True)
self.t0n = Nonlinearity(name='relu', scope='relu0')
self.t1l = Linear(size=size_t1, scope='linear1')
self.t1b = TFLayer(layer='batch_normalization', scope='batchnorm1', center=True, scale=True)
self.t1n = Nonlinearity(name='relu', scope='relu1')
self.t2d = Dense(size=1, activation='tanh', scope='dense0',
weights=tf.random_uniform_initializer(minval=-3e-3, maxval=3e-3))
self.add_layer(self.t0l)
self.add_layer(self.t0b)
self.add_layer(self.t0n)
self.add_layer(self.t1l)
self.add_layer(self.t1b)
self.add_layer(self.t1n)
self.add_layer(self.t2d)
class CustomNetwork(LayerBasedNetwork):
def __init__(self, scope='layerbased-network', summary_labels=()):
super(CustomNetwork,
self).__init__(scope=scope,
summary_labels=summary_labels)
self.layer_bool1 = Dense(size=16, scope='state-bool1')
self.add_layer(layer=self.layer_bool1)
self.layer_bool2 = Dense(size=16, scope='state-bool2')
self.add_layer(layer=self.layer_bool2)
self.layer_int1 = Dense(size=16, scope='state-int1')
self.add_layer(layer=self.layer_int1)
self.layer_int2 = Dense(size=16, scope='state-int2')
self.add_layer(layer=self.layer_int2)
self.layer_float1 = Dense(size=16, scope='state-float1')
self.add_layer(layer=self.layer_float1)
self.layer_float2 = Dense(size=16, scope='state-float2')
self.add_layer(layer=self.layer_float2)
self.layer_bounded1 = Dense(size=16, scope='state-bounded1')
self.add_layer(layer=self.layer_bounded1)
self.layer_bounded2 = Dense(size=16, scope='state-bounded2')
self.add_layer(layer=self.layer_bounded2)
def tf_apply(self, x, internals, update, return_internals=False):
x0 = self.layer_bool2.apply(x=self.layer_bool1.apply(
x=x['bool'], update=update),
update=update)
x1 = self.layer_int2.apply(x=self.layer_int1.apply(
x=x['int'], update=update),
update=update)
x2 = self.layer_float2.apply(x=self.layer_float1.apply(
x=x['float'], update=update),
update=update)
x3 = self.layer_bounded2.apply(x=self.layer_bounded1.apply(
x=x['bounded'], update=update),
update=update)
x = x0 * x1 * x2 * x3
return (x, dict()) if return_internals else x
def __init__(self, scope='layerbased-network', summary_labels=()):
super(CustomNetwork,
self).__init__(scope=scope,
summary_labels=summary_labels)
self.layer01 = Dense(size=32, scope='state0-1')
self.add_layer(layer=self.layer01)
self.layer02 = Dense(size=32, scope='state0-2')
self.add_layer(layer=self.layer02)
self.layer11 = Dense(size=32, scope='state1-1')
self.add_layer(layer=self.layer11)
self.layer12 = Dense(size=32, scope='state1-2')
self.add_layer(layer=self.layer12)
self.layer21 = Dense(size=32, scope='state2-1')
self.add_layer(layer=self.layer21)
self.layer22 = Dense(size=32, scope='state2-2')
self.add_layer(layer=self.layer22)
self.layer31 = Dense(size=32, scope='state3-1')
self.add_layer(layer=self.layer31)
self.layer32 = Dense(size=32, scope='state3-2')
self.add_layer(layer=self.layer32)
def __init__(self, scope='layerbased-network', summary_labels=()):
super(CustomNetwork,
self).__init__(scope=scope,
summary_labels=summary_labels)
self.layer_bool1 = Dense(size=16, scope='state-bool1')
self.add_layer(layer=self.layer_bool1)
self.layer_bool2 = Dense(size=16, scope='state-bool2')
self.add_layer(layer=self.layer_bool2)
self.layer_int1 = Dense(size=16, scope='state-int1')
self.add_layer(layer=self.layer_int1)
self.layer_int2 = Dense(size=16, scope='state-int2')
self.add_layer(layer=self.layer_int2)
self.layer_float1 = Dense(size=16, scope='state-float1')
self.add_layer(layer=self.layer_float1)
self.layer_float2 = Dense(size=16, scope='state-float2')
self.add_layer(layer=self.layer_float2)
self.layer_bounded1 = Dense(size=16, scope='state-bounded1')
self.add_layer(layer=self.layer_bounded1)
self.layer_bounded2 = Dense(size=16, scope='state-bounded2')
self.add_layer(layer=self.layer_bounded2)
class CustomNetwork(LayerBasedNetwork):
def __init__(self, scope='layerbased-network', summary_labels=()):
super(CustomNetwork,
self).__init__(scope=scope,
summary_labels=summary_labels)
if not exclude_bool:
self.layer_bool1 = Dense(size=16, scope='state-bool1')
self.add_layer(layer=self.layer_bool1)
self.layer_bool2 = Dense(size=16, scope='state-bool2')
self.add_layer(layer=self.layer_bool2)
if not exclude_int:
self.layer_int1 = Dense(size=16, scope='state-int1')
self.add_layer(layer=self.layer_int1)
self.layer_int2 = Dense(size=16, scope='state-int2')
self.add_layer(layer=self.layer_int2)
if not exclude_float:
self.layer_float1 = Dense(size=16, scope='state-float1')
self.add_layer(layer=self.layer_float1)
self.layer_float2 = Dense(size=16, scope='state-float2')
self.add_layer(layer=self.layer_float2)
if not exclude_bounded:
self.layer_bounded1 = Dense(size=16,
scope='state-bounded1')
self.add_layer(layer=self.layer_bounded1)
self.layer_bounded2 = Dense(size=16,
scope='state-bounded2')
self.add_layer(layer=self.layer_bounded2)
def tf_apply(self, x, internals, update, return_internals=False):
xs = list()
if not exclude_bool:
xs.append(
self.layer_bool2.apply(x=self.layer_bool1.apply(
x=x['bool'], update=update),
update=update))
if not exclude_int:
xs.append(
self.layer_int2.apply(x=self.layer_int1.apply(
x=x['int'], update=update),
update=update))
if not exclude_float:
xs.append(
self.layer_float2.apply(x=self.layer_float1.apply(
x=x['float'], update=update),
update=update))
if not exclude_bounded:
xs.append(
self.layer_bounded2.apply(x=self.layer_bounded1.apply(
x=x['bounded-float'], update=update),
update=update))
x = xs[0]
for y in xs[1:]:
x *= y
# import tensorflow as tf
# x = tf.concat(values=xs, axis=1)
return (x, list()) if return_internals else x
def tf_apply(self, x, internals, return_internals=False):
layer01 = Dense(size=32, scope='state0-1')
self.add_layer(layer=layer01)
layer02 = Dense(size=32, scope='state0-2')
self.add_layer(layer=layer02)
x0 = layer02.apply(x=layer01.apply(x=x['state0']))
layer11 = Dense(size=32, scope='state1-1')
self.add_layer(layer=layer11)
layer12 = Dense(size=32, scope='state1-2')
self.add_layer(layer=layer12)
x1 = layer12.apply(x=layer11.apply(x=x['state1']))
layer21 = Dense(size=32, scope='state2-1')
self.add_layer(layer=layer21)
layer22 = Dense(size=32, scope='state2-2')
self.add_layer(layer=layer22)
x2 = layer22.apply(x=layer21.apply(x=x['state2']))
layer31 = Dense(size=32, scope='state3-1')
self.add_layer(layer=layer31)
layer32 = Dense(size=32, scope='state3-2')
self.add_layer(layer=layer32)
x3 = layer32.apply(x=layer31.apply(x=x['state3']))
x = x0 * x1 * x2 * x3
return (x, list()) if return_internals else x
class DDPGCriticNetwork(LayerBasedNetwork):
def __init__(self,
scope='ddpg-critic-network',
summary_labels=(),
size_t0=400,
size_t1=300):
super(DDPGCriticNetwork, self).__init__(scope=scope,
summary_labels=summary_labels)
self.t0l = Linear(size=size_t0, scope='linear0')
self.t0b = TFLayer(layer='batch_normalization',
scope='batchnorm0',
center=True,
scale=True)
self.t0n = Nonlinearity(name='relu', scope='relu0')
self.t1l = Linear(size=size_t1, scope='linear1')
self.t1b = TFLayer(layer='batch_normalization',
scope='batchnorm1',
center=True,
scale=True)
self.t1n = Nonlinearity(name='relu', scope='relu1')
self.t2d = Dense(size=1,
activation='tanh',
scope='dense0',
weights=tf.random_uniform_initializer(minval=-3e-3,
maxval=3e-3))
self.add_layer(self.t0l)
self.add_layer(self.t0b)
self.add_layer(self.t0n)
self.add_layer(self.t1l)
self.add_layer(self.t1b)
self.add_layer(self.t1n)
self.add_layer(self.t2d)
def tf_apply(self, x, internals, update, return_internals=False):
assert x['states'], x['actions']
if isinstance(x['states'], dict):
if len(x['states']) != 1:
raise TensorForceError(
'DDPG critic network must have only one state input, but {} given.'
.format(len(x['states'])))
x_states = x['states'][next(iter(sorted(x['states'])))]
else:
x_states = x['states']
if isinstance(x['actions'], dict):
if len(x['actions']) != 1:
raise TensorForceError(
'DDPG critic network must have only one action input, but {} given.'
.format(len(x['actions'])))
x_actions = x['actions'][next(iter(sorted(x['actions'])))]
else:
x_actions = x['actions']
out = self.t0l.apply(x=x_states, update=update)
out = self.t0b.apply(x=out, update=update)
out = self.t0n.apply(x=out, update=update)
out = self.t1l.apply(x=tf.concat([out, x_actions], axis=1),
update=update)
out = self.t1b.apply(x=out, update=update)
out = self.t1n.apply(x=out, update=update)
out = self.t2d.apply(x=out, update=update)
# Remove last dimension because we only return Q values for one state and action
# out = tf.squeeze(out)
if return_internals:
# Todo: Internals management
return out, None
else:
return out
def tf_apply(self, x, internals, update, return_internals=False):
if exclude_bool:
x0 = 1.0
else:
layer01 = Dense(size=32, scope='state0-1')
self.add_layer(layer=layer01)
layer02 = Dense(size=32, scope='state0-2')
self.add_layer(layer=layer02)
x0 = layer02.apply(x=layer01.apply(x=x['state0'], update=update), update=update)
if exclude_int:
x1 = 1.0
else:
layer11 = Dense(size=32, scope='state1-1')
self.add_layer(layer=layer11)
layer12 = Dense(size=32, scope='state1-2')
self.add_layer(layer=layer12)
x1 = layer12.apply(x=layer11.apply(x=x['state1'], update=update), update=update)
if exclude_float:
x2 = 1.0
else:
layer21 = Dense(size=32, scope='state2-1')
self.add_layer(layer=layer21)
layer22 = Dense(size=32, scope='state2-2')
self.add_layer(layer=layer22)
x2 = layer22.apply(x=layer21.apply(x=x['state2'], update=update), update=update)
if exclude_bounded:
x3 = 1.0
else:
layer31 = Dense(size=32, scope='state3-1')
self.add_layer(layer=layer31)
layer32 = Dense(size=32, scope='state3-2')
self.add_layer(layer=layer32)
x3 = layer32.apply(x=layer31.apply(x=x['state3'], update=update), update=update)
x = x0 * x1 * x2 * x3
return (x, list()) if return_internals else x
