class CriticCNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(self.value, self.loss, [
adam(self.value.parameters,
lr=[0.0001] * 600 + [0.00005] * 600 + [0.00001] * 600 +
[0.000005],
momentum=0.9)
])
def _build_network(self, pretrained_policy):
self.image_frame = C.input_variable((1, ) +
self.observation_space_shape)
self.target_current_state_value = C.input_variable((1, ))
if pretrained_policy is None:
h = C.layers.Convolution2D(filter_shape=(7, 7),
num_filters=32,
strides=(4, 4),
pad=True,
name='conv_1',
activation=C.relu)(self.image_frame)
h = C.layers.Convolution2D(filter_shape=(5, 5),
num_filters=64,
strides=(2, 2),
pad=True,
name='conv_2',
activation=C.relu)(h)
h = C.layers.Convolution2D(filter_shape=(3, 3),
num_filters=128,
strides=(1, 1),
pad=True,
name='conv_3',
activation=C.relu)(h)
h = C.layers.Dense(64, activation=C.relu, name='dense_1')(h)
self.value = C.layers.Dense(1, name='dense_2')(h)
else:
self.value = C.Function.load(pretrained_policy)(self.image_frame)
self.loss = C.squared_error(self.target_current_state_value,
self.value)
def optimise(self, image_frame, target_current_state_value):
self.trainer.train_minibatch({
self.image_frame:
image_frame,
self.target_current_state_value:
target_current_state_value
})
def predict(self, image_frame):
return self.value.eval({self.image_frame: image_frame})
class StackedFrameCNNPolicy:
def __init__(self,
name,
num_frames_to_stack,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.num_frames_to_stack = num_frames_to_stack
self.observation_space_shape = observation_space_shape
self.frame_stacker = FrameStacker(stack_size=num_frames_to_stack,
frame_shape=observation_space_shape)
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(self.q, self.loss,
[sgd(self.q.parameters, lr=0.000001)])
def _build_network(self, pretrained_policy):
self.image_frame = C.input_variable((self.num_frames_to_stack, ) +
self.observation_space_shape)
if pretrained_policy is None:
h = C.layers.Convolution2D(filter_shape=(7, 7),
num_filters=32,
strides=(4, 4),
pad=True,
name='conv_1',
activation=C.relu)(self.image_frame)
h = C.layers.Convolution2D(filter_shape=(5, 5),
num_filters=64,
strides=(2, 2),
pad=True,
name='conv_2',
activation=C.relu)(h)
h = C.layers.Convolution2D(filter_shape=(3, 3),
num_filters=128,
strides=(1, 1),
pad=True,
name='conv_3',
activation=C.relu)(h)
h = C.layers.Dense(64, activation=C.relu, name='dense_1')(h)
self.q = C.layers.Dense(self.num_actions, name='dense_1')(h)
else:
self.q = C.Function.load(pretrained_policy)(self.image_frame)
self.q_target = C.input_variable(self.num_actions)
self.loss = C.mean(C.losses.squared_error(self.q_target, self.q))
def optimise(self, image_frame, q_target):
self.trainer.train_minibatch({
self.image_frame: image_frame,
self.q_target: q_target
})
def predict(self, image_frame):
return self.q.eval({self.image_frame: image_frame})
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(self.q, self.loss,
[sgd(self.q.parameters, lr=5e-4)])
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.value, self.loss,
[adam(self.value.parameters, lr=0.001, momentum=0.9)])
class ActorNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.probabilities, self.loss,
[adam(self.probabilities.parameters, lr=0.0001, momentum=0.9)])
def _build_network(self, pretrained_policy):
self.image_frame = C.input_variable((1, ) +
self.observation_space_shape)
self.td_error = C.input_variable((1, ))
self.action_index = C.input_variable((1, ))
one_hot_action = C.one_hot(self.action_index, self.num_actions)
if pretrained_policy is None:
h = C.layers.Dense(64, activation=C.relu,
name='dense_1')(self.image_frame)
self.probabilities = C.layers.Dense(self.num_actions,
name='dense_2',
activation=C.softmax)(h)
else:
self.probabilities = C.Function.load(pretrained_policy)(
self.image_frame)
selected_action_probablity = C.ops.times_transpose(
self.probabilities, one_hot_action)
self.log_probability = C.ops.log(selected_action_probablity)
self.loss = -self.td_error * self.log_probability
# self.probabilities = C.softmax(self.logits)
# log_probability_of_action_taken = cross_entropy_with_softmax(self.logits, one_hot_action)
# self.loss = C.reduce_mean(self.td_error*log_probability_of_action_taken)
def optimise(self, image_frame, td_error, action_index):
self.trainer.train_minibatch({
self.image_frame: image_frame,
self.td_error: td_error,
self.action_index: action_index
})
def predict(self, image_frame):
return self.probabilities.eval({self.image_frame: image_frame})
def __init__(self,
name,
num_frames_to_stack,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.num_frames_to_stack = num_frames_to_stack
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.log_probability, self.loss,
[adam(self.probabilities.parameters, lr=0.00001, momentum=0.9)])
class REINFORCENNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.action_probabilities, self.loss,
[sgd(self.action_probabilities.parameters, lr=0.000001)])
def _build_network(self, pretrained_policy):
self.input = C.input_variable(self.observation_space_shape,
name='image frame')
self.target = C.input_variable((1, ), name='q_target')
self.action_index = C.input_variable((1, ))
one_hot_action = C.ops.squeeze(
C.one_hot(self.action_index, self.num_actions))
if pretrained_policy is None:
h = C.layers.Dense(64, activation=C.relu,
name='dense_1')(self.input)
h = C.layers.Dense(32, activation=C.relu, name='dense_1')(h)
self.action_probabilities = C.layers.Dense(self.num_actions,
activation=C.softmax,
name='dense_1')(h)
else:
self.action_probabilities = C.Function.load(pretrained_policy)(
self.input)
selected_action_probablity = C.ops.times_transpose(
self.action_probabilities, one_hot_action)
self.log_probability = C.ops.log(selected_action_probablity)
self.loss = C.sum(self.log_probability * self.target)
def optimise(self, state, action, target):
self.trainer.train_minibatch({
self.input: state,
self.action_index: action,
self.target: target
})
def predict(self, state):
return self.action_probabilities.eval({self.input: state})
def __init__(self,
name,
num_frames_to_stack,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.num_frames_to_stack = num_frames_to_stack
self.observation_space_shape = observation_space_shape
self.frame_stacker = FrameStacker(stack_size=num_frames_to_stack,
frame_shape=observation_space_shape)
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(self.q, self.loss,
[sgd(self.q.parameters, lr=0.000001)])
class ActorNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.log_probability, self.loss,
[adam(self.probabilities.parameters, lr=0.001, momentum=0.9)])
def _build_network(self, pretrained_policy):
self.input = C.input_variable(self.observation_space_shape)
self.td_error = C.input_variable((1, ))
self.action_index = C.input_variable((1, ))
one_hot_action = C.ops.squeeze(
C.one_hot(self.action_index, self.num_actions))
if pretrained_policy is None:
h = C.layers.Dense(64, activation=C.relu,
name='dense_1')(self.input)
h = C.layers.Dense(64, activation=C.tanh, name='dense_2')(h)
self.probabilities = C.layers.Dense(self.num_actions,
name='dense_3',
activation=C.softmax)(h)
else:
self.probabilities = C.Function.load(pretrained_policy)(self.input)
selected_action_probablity = C.ops.times_transpose(
self.probabilities, one_hot_action)
self.log_probability = C.ops.log(selected_action_probablity)
self.loss = -self.td_error * self.log_probability
def optimise(self, state, td_error, action_index):
self.trainer.train_minibatch({
self.input: state,
self.td_error: td_error,
self.action_index: action_index
})
def predict(self, state):
return self.probabilities.eval({self.input: state})
class CriticNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.value, self.loss,
[adam(self.value.parameters, lr=0.001, momentum=0.9)])
def _build_network(self, pretrained_policy):
self.input = C.input_variable(self.observation_space_shape)
self.target_current_state_value = C.input_variable((1, ))
if pretrained_policy is None:
h = C.layers.Dense(64, activation=C.relu,
name='dense_1')(self.input)
h = C.layers.Dense(64, activation=C.relu, name='dense_2')(h)
self.value = C.layers.Dense(1, name='dense_3')(h)
else:
self.value = C.Function.load(pretrained_policy)(self.input)
self.loss = C.squared_error(self.target_current_state_value,
self.value)
def optimise(self, state, target_current_state_value):
self.trainer.train_minibatch({
self.input:
state,
self.target_current_state_value:
target_current_state_value
})
def predict(self, state):
return self.value.eval({self.input: state})
class SimpleNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(self.q, self.loss,
[sgd(self.q.parameters, lr=5e-4)])
def _build_network(self, pretrained_policy):
self.input = C.input_variable(self.observation_space_shape,
name='image frame')
if pretrained_policy is None:
h = C.layers.Dense(64, activation=C.relu,
name='dense_1')(self.input)
h = C.layers.Dense(64, activation=C.relu, name='dense_2')(h)
self.q = C.layers.Dense(self.num_actions,
activation=None,
name='output')(h)
else:
self.q = C.Function.load(pretrained_policy)(self.input)
self.q_target = C.input_variable(self.num_actions, name='q_target')
self.loss = C.losses.squared_error(self.q_target, self.q)
def optimise(self, state, q_target):
self.trainer.train_minibatch({
self.input: state,
self.q_target: q_target
})
def predict(self, state):
return self.q.eval({self.input: state})
def __init__(self, in_shape, output_shape, device_id=None,
learning_rate=0.00025, momentum=0.9,
minibatch_size=32, update_interval=10000,
n_workers=1, visualizer=None):
"""
Q Neural Network following Mnih and al. implementation and default options.
The network has the following topology:
Convolution(32, (8, 8))
Convolution(64, (4, 4))
Convolution(64, (2, 2))
Dense(512)
:param in_shape: Shape of the observations perceived by the learner (the neural net input)
:param output_shape: Size of the action space (mapped to the number of output neurons)
:param device_id: Use None to let CNTK select the best available device,
-1 for CPU, >= 0 for GPU
(default: None)
:param learning_rate: Learning rate
(default: 0.00025, as per Mnih et al.)
:param momentum: Momentum, provided as momentum value for
averaging gradients without unit gain filter
Note that CNTK does not currently provide an implementation
of Graves' RmsProp with momentum.
It uses AdamSGD optimizer instead.
(default: 0, no momentum with RProp optimizer)
:param minibatch_size: Minibatch size
(default: 32, as per Mnih et al.)
:param n_workers: Number of concurrent worker for distributed training.
(default: 1, not distributed)
:param visualizer: Optional visualizer allowing the model to save summary data
(default: None, no visualization)
Ref: Mnih et al.: "Human-level control through deep reinforcement learning."
Nature 518.7540 (2015): 529-533.
"""
assert learning_rate > 0, 'learning_rate should be > 0'
assert 0. <= momentum < 1, 'momentum should be 0 <= momentum < 1'
QModel.__init__(self, in_shape, output_shape)
CntkModel.__init__(self, device_id, False, n_workers, visualizer)
self._nb_actions = output_shape
self._steps = 0
self._target_update_interval = update_interval
self._target = None
# Input vars
self._environment = input(in_shape, name='env',
dynamic_axes=(Axis.default_batch_axis()))
self._q_targets = input(1, name='q_targets',
dynamic_axes=(Axis.default_batch_axis()))
self._actions = input(output_shape, name='actions',
dynamic_axes=(Axis.default_batch_axis()))
# Define the neural network graph
self._model = self._build_model()(self._environment)
self._target = self._model.clone(
CloneMethod.freeze, {self._environment: self._environment}
)
# Define the learning rate
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
# AdamSGD optimizer
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._model.parameters, lr_schedule,
momentum=m_schedule,
unit_gain=True,
variance_momentum=vm_schedule)
if self.distributed_training:
raise NotImplementedError('ASGD not implemented yet.')
# _actions is a sparse 1-hot encoding of the actions done by the agent
q_acted = reduce_sum(self._model * self._actions, axis=0)
# Define the trainer with Huber Loss function
criterion = huber_loss(q_acted, self._q_targets, 1.0)
self._learner = l_sgd
self._trainer = Trainer(self._model, (criterion, None), l_sgd)
class QNeuralNetwork(CntkModel, QModel):
"""
Represents a learning capable entity using CNTK
"""
def __init__(self,
in_shape,
output_shape,
device_id=None,
learning_rate=0.00025,
momentum=0.9,
minibatch_size=32,
update_interval=10000,
n_workers=1,
visualizer=None):
"""
Q Neural Network following Mnih and al. implementation and default options.
The network has the following topology:
Convolution(32, (8, 8))
Convolution(64, (4, 4))
Convolution(64, (2, 2))
Dense(512)
:param in_shape: Shape of the observations perceived by the learner (the neural net input)
:param output_shape: Size of the action space (mapped to the number of output neurons)
:param device_id: Use None to let CNTK select the best available device,
-1 for CPU, >= 0 for GPU
(default: None)
:param learning_rate: Learning rate
(default: 0.00025, as per Mnih et al.)
:param momentum: Momentum, provided as momentum value for
averaging gradients without unit gain filter
Note that CNTK does not currently provide an implementation
of Graves' RmsProp with momentum.
It uses AdamSGD optimizer instead.
(default: 0, no momentum with RProp optimizer)
:param minibatch_size: Minibatch size
(default: 32, as per Mnih et al.)
:param n_workers: Number of concurrent worker for distributed training.
(default: 1, not distributed)
:param visualizer: Optional visualizer allowing the model to save summary data
(default: None, no visualization)
Ref: Mnih et al.: "Human-level control through deep reinforcement learning."
Nature 518.7540 (2015): 529-533.
"""
assert learning_rate > 0, 'learning_rate should be > 0'
assert 0. <= momentum < 1, 'momentum should be 0 <= momentum < 1'
QModel.__init__(self, in_shape, output_shape)
CntkModel.__init__(self, device_id, False, n_workers, visualizer)
self._nb_actions = output_shape
self._steps = 0
self._target_update_interval = update_interval
self._target = None
# Input vars
self._environment = input(in_shape,
name='env',
dynamic_axes=(Axis.default_batch_axis()))
self._q_targets = input(1,
name='q_targets',
dynamic_axes=(Axis.default_batch_axis()))
self._actions = input(output_shape,
name='actions',
dynamic_axes=(Axis.default_batch_axis()))
# Define the neural network graph
self._model = self._build_model()(self._environment)
self._target = self._model.clone(
CloneMethod.freeze, {self._environment: self._environment})
# Define the learning rate
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
# AdamSGD optimizer
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._model.parameters,
lr_schedule,
momentum=m_schedule,
unit_gain=True,
variance_momentum=vm_schedule)
if self.distributed_training:
raise NotImplementedError('ASGD not implemented yet.')
# _actions is a sparse 1-hot encoding of the actions done by the agent
q_acted = reduce_sum(self._model * self._actions, axis=0)
# Define the trainer with Huber Loss function
criterion = huber_loss(q_acted, self._q_targets, 1.0)
self._learner = l_sgd
self._trainer = Trainer(self._model, (criterion, None), l_sgd)
@property
def loss_val(self):
return self._trainer.previous_minibatch_loss_average
def _build_model(self):
with default_options(init=he_uniform(), activation=relu, bias=True):
model = Sequential([
Convolution((8, 8), 32, strides=(4, 4)),
Convolution((4, 4), 64, strides=(2, 2)),
Convolution((3, 3), 64, strides=(1, 1)),
Dense(512, init=he_normal(0.01)),
Dense(self._nb_actions, activation=None, init=he_normal(0.01))
])
return model
def train(self, x, q_value_targets, actions=None):
assert actions is not None, 'actions cannot be None'
# We need to add extra dimensions to shape [N, 1] => [N, 1]
if check_rank(q_value_targets.shape, 1):
q_value_targets = q_value_targets.reshape((-1, 1))
# Add extra dimensions to match shape [N, 1] required by one_hot
if check_rank(actions.shape, 1):
actions = actions.reshape((-1, 1))
# We need batch axis
if check_rank(x.shape, len(self._environment.shape)):
x = prepend_batch_axis(x)
self._trainer.train_minibatch({
self._environment:
x,
self._actions:
Value.one_hot(actions, self._nb_actions),
self._q_targets:
q_value_targets
})
# Counter number of train calls
self._steps += 1
# Update the model with the target one
if (self._steps % self._target_update_interval) == 0:
self._target = self._model.clone(
CloneMethod.freeze, {self._environment: self._environment})
def evaluate(self, data, model=QModel.ACTION_VALUE_NETWORK):
# If evaluating a single sample, expand the minibatch axis
# (minibatch = 1, input_shape...)
if len(data.shape) == len(self.input_shape):
data = prepend_batch_axis(data) # Append minibatch dim
if model == QModel.TARGET_NETWORK:
predictions = self._target.eval({self._environment: data})
else:
predictions = self._model.eval({self._environment: data})
return predictions.squeeze()
class ActorStackedFrameCNNPolicy:
def __init__(self,
name,
num_frames_to_stack,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.num_frames_to_stack = num_frames_to_stack
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.log_probability, self.loss,
[adam(self.probabilities.parameters, lr=0.00001, momentum=0.9)])
def _build_network(self, pretrained_policy):
self.image_frame = C.input_variable((self.num_frames_to_stack, ) +
self.observation_space_shape)
self.td_error = C.input_variable((1, ))
self.action_index = C.input_variable((1, ))
one_hot_action = C.ops.squeeze(
C.one_hot(self.action_index, self.num_actions))
if pretrained_policy is None:
h = C.layers.Convolution2D(filter_shape=(7, 7),
num_filters=32,
strides=(4, 4),
pad=True,
name='conv_1',
activation=C.relu)(self.image_frame)
h = C.layers.Convolution2D(filter_shape=(5, 5),
num_filters=64,
strides=(2, 2),
pad=True,
name='conv_2',
activation=C.relu)(h)
h = C.layers.Convolution2D(filter_shape=(3, 3),
num_filters=128,
strides=(1, 1),
pad=True,
name='conv_3',
activation=C.relu)(h)
h = C.layers.Dense(64, activation=C.relu, name='dense_1')(h)
self.probabilities = C.layers.Dense(self.num_actions,
name='dense_2',
activation=C.softmax)(h)
else:
self.probabilities = C.Function.load(pretrained_policy)(
self.image_frame)
selected_action_probablity = C.ops.times_transpose(
self.probabilities, one_hot_action)
self.log_probability = C.ops.log(selected_action_probablity)
self.loss = -self.td_error * self.log_probability
def optimise(self, image_frame, td_error, action_index):
self.trainer.train_minibatch({
self.image_frame: image_frame,
self.td_error: td_error,
self.action_index: action_index
})
def predict(self, image_frame):
return self.probabilities.eval({self.image_frame: image_frame})
class CriticCNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.value, self.loss,
[adam(self.value.parameters, lr=0.0001, momentum=0.9)])
def _build_network(self, pretrained_policy):
self.image_frame = C.input_variable((1, ) +
self.observation_space_shape)
self.next_image_frame = C.input_variable((1, ) +
self.observation_space_shape)
self.reward = C.input_variable((1, ))
if pretrained_policy is None:
h = C.layers.Convolution2D(filter_shape=(7, 7),
num_filters=32,
strides=(4, 4),
pad=True,
name='conv_1',
activation=C.relu)
h = C.layers.Convolution2D(filter_shape=(5, 5),
num_filters=64,
strides=(2, 2),
pad=True,
name='conv_2',
activation=C.relu)(h)
h = C.layers.Convolution2D(filter_shape=(3, 3),
num_filters=128,
strides=(1, 1),
pad=True,
name='conv_3',
activation=C.relu)(h)
h = C.layers.Dense(64, activation=C.relu, name='dense_1')(h)
v = C.layers.Dense(1, name='dense_2')(h)
self.value = v(self.image_frame)
self.next_value = v(self.next_image_frame)
self.output = C.combine([self.value, self.next_value])
else:
self.output = C.Function.load(pretrained_policy)(
self.image_frame, self.next_image_frame)
[self.value, self.next_value] = self.output[
self.value.output], self.output[self.next_value.output]
target = DISCOUNT_FACTOR * self.next_value + self.reward
self.loss = C.squared_error(target, self.value)
def optimise(self, image_frame, next_image_frame, rewards):
self.trainer.train_minibatch({
self.image_frame: image_frame,
self.next_image_frame: next_image_frame,
self.reward: rewards
})
def predict(self, image_frame):
return self.value.eval({self.image_frame: image_frame})
class ActorCriticCNNPolicy:
def __init__(self,
name,
observation_space_shape,
num_actions,
pretrained_policy=None,
*args,
**kwargs):
self.name = name
self.observation_space_shape = observation_space_shape
self.num_actions = num_actions
self._build_network(pretrained_policy)
self.trainer = Trainer(
self.output, self.loss,
[adam(self.output.parameters, lr=0.00003, momentum=0.9)])
def _build_network(self, pretrained_policy):
self.image_frame = C.input_variable((1, ) +
self.observation_space_shape)
self.next_image_frame = C.input_variable((1, ) +
self.observation_space_shape)
self.advantage = C.input_variable((1, ))
self.action_index = C.input_variable((1, ))
self.target_value = C.input_variable((1, ))
one_hot_action = C.one_hot(self.action_index, self.num_actions)
if pretrained_policy is None:
h = C.layers.Convolution2D(filter_shape=(7, 7),
num_filters=32,
strides=(4, 4),
pad=True,
name='conv_1',
activation=C.relu)
h = C.layers.Convolution2D(filter_shape=(5, 5),
num_filters=64,
strides=(2, 2),
pad=True,
name='conv_2',
activation=C.relu)(h)
h = C.layers.Convolution2D(filter_shape=(3, 3),
num_filters=128,
strides=(1, 1),
pad=True,
name='conv_3',
activation=C.relu)(h)
h = C.layers.Dense(64, activation=C.relu, name='dense_1')(h)
self.probabilities = C.layers.Dense(self.num_actions,
name='dense_2',
activation=C.softmax)(h(
self.image_frame))
v = C.layers.Dense(1, name='dense_3')(h)
self.value = v(self.image_frame)
self.next_value = v(self.next_image_frame)
self.output = C.combine(
[self.probabilities, self.value, self.next_value])
else:
[self.probabilities, self.value, self.next_value] = list(
C.Function.load(pretrained_policy)(self.image_frame,
self.next_image_frame))
self.values_output = C.combine([self.value, self.next_value])
selected_action_probablity = C.ops.times_transpose(
self.probabilities, one_hot_action)
self.log_probability = C.ops.log(selected_action_probablity)
self.actor_loss = -self.advantage * self.log_probability
self.critic_loss = C.squared_error(self.target_value, self.value)
self.loss = 0.5 * self.actor_loss + 0.5 * self.critic_loss
# self.probabilities = C.softmax(self.logits)
# log_probability_of_action_taken = cross_entropy_with_softmax(self.logits, one_hot_action)
# self.loss = C.reduce_mean(self.td_error*log_probability_of_action_taken)
def optimise(self, image_frame, action_index, advantage, targets):
self.trainer.train_minibatch({
self.image_frame: image_frame,
self.advantage: advantage,
self.action_index: action_index,
self.target_value: targets
})
def predict(self, image_frame):
return self.probabilities.eval({self.image_frame: image_frame})
def values(self, image_frame, next_image_frame):
output = self.values_output.eval({
self.image_frame:
image_frame,
self.next_image_frame:
next_image_frame
})
return output[self.value.output], output[self.next_value.output]
classificationError = classification_error(outputLayer, labelsShape)
input_map = {
featuresShape: reader.streams.features,
labelsShape: reader.streams.labels
}
numOfEpochs = 10
printer = [ProgressPrinter(
tag = 'Training',
num_epochs = numOfEpochs)]
learningRate = learning_rate_schedule([0.1, 0.01, 0.001], UnitType.sample, 700)
trainer = Trainer(outputLayer,(crossEntropy, classificationError), [adadelta(outputLayer.parameters, learningRate)], printer)
minibatchSize = 50
numberOfSamples = 2208
numberOfSweepsForTraining = 10
trainingSession = training_session(
trainer=trainer,
mb_source=reader,
mb_size=minibatchSize,
model_inputs_to_streams=input_map,
max_samples=numberOfSamples * numberOfSweepsForTraining,
progress_frequency=numberOfSamples
)
trainingSession.train()
classificationError = classification_error(outputLayer, labelsShape)
input_map = {
featuresShape: reader.streams.features,
labelsShape: reader.streams.labels
}
numOfEpochs = 10
printer = [ProgressPrinter(
tag = 'Training',
num_epochs = numOfEpochs)]
learningRate = learning_rate_schedule([0.1, 0.01, 0.001], UnitType.sample, 700)
trainer = Trainer(outputLayer,(crossEntropy, classificationError), [adadelta(outputLayer.parameters, learningRate)], printer)
minibatchSize = 50
numberOfSamples = 2208
numberOfSweepsForTraining = 10
trainingSession = training_session(
trainer=trainer,
mb_source=reader,
mb_size=minibatchSize,
model_inputs_to_streams=input_map,
max_samples=numberOfSamples * numberOfSweepsForTraining,
progress_frequency=numberOfSamples
)
trainingSession.train()
def __init__(self,
in_shape,
output_shape,
device_id=None,
learning_rate=0.00025,
momentum=0.9,
minibatch_size=32,
update_interval=10000,
n_workers=1,
visualizer=None):
"""
Q Neural Network following Mnih and al. implementation and default options.
The network has the following topology:
Convolution(32, (8, 8))
Convolution(64, (4, 4))
Convolution(64, (2, 2))
Dense(512)
:param in_shape: Shape of the observations perceived by the learner (the neural net input)
:param output_shape: Size of the action space (mapped to the number of output neurons)
:param device_id: Use None to let CNTK select the best available device,
-1 for CPU, >= 0 for GPU
(default: None)
:param learning_rate: Learning rate
(default: 0.00025, as per Mnih et al.)
:param momentum: Momentum, provided as momentum value for
averaging gradients without unit gain filter
Note that CNTK does not currently provide an implementation
of Graves' RmsProp with momentum.
It uses AdamSGD optimizer instead.
(default: 0, no momentum with RProp optimizer)
:param minibatch_size: Minibatch size
(default: 32, as per Mnih et al.)
:param n_workers: Number of concurrent worker for distributed training.
(default: 1, not distributed)
:param visualizer: Optional visualizer allowing the model to save summary data
(default: None, no visualization)
Ref: Mnih et al.: "Human-level control through deep reinforcement learning."
Nature 518.7540 (2015): 529-533.
"""
assert learning_rate > 0, 'learning_rate should be > 0'
assert 0. <= momentum < 1, 'momentum should be 0 <= momentum < 1'
QModel.__init__(self, in_shape, output_shape)
CntkModel.__init__(self, device_id, False, n_workers, visualizer)
self._nb_actions = output_shape
self._steps = 0
self._target_update_interval = update_interval
self._target = None
# Input vars
self._environment = input(in_shape,
name='env',
dynamic_axes=(Axis.default_batch_axis()))
self._q_targets = input(1,
name='q_targets',
dynamic_axes=(Axis.default_batch_axis()))
self._actions = input(output_shape,
name='actions',
dynamic_axes=(Axis.default_batch_axis()))
# Define the neural network graph
self._model = self._build_model()(self._environment)
self._target = self._model.clone(
CloneMethod.freeze, {self._environment: self._environment})
# Define the learning rate
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
# AdamSGD optimizer
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._model.parameters,
lr_schedule,
momentum=m_schedule,
unit_gain=True,
variance_momentum=vm_schedule)
if self.distributed_training:
raise NotImplementedError('ASGD not implemented yet.')
# _actions is a sparse 1-hot encoding of the actions done by the agent
q_acted = reduce_sum(self._model * self._actions, axis=0)
# Define the trainer with Huber Loss function
criterion = huber_loss(q_acted, self._q_targets, 1.0)
self._learner = l_sgd
self._trainer = Trainer(self._model, (criterion, None), l_sgd)
class QNeuralNetwork(CntkModel, QModel):
"""
Represents a learning capable entity using CNTK
"""
def __init__(self, in_shape, output_shape, device_id=None,
learning_rate=0.00025, momentum=0.9,
minibatch_size=32, update_interval=10000,
n_workers=1, visualizer=None):
"""
Q Neural Network following Mnih and al. implementation and default options.
The network has the following topology:
Convolution(32, (8, 8))
Convolution(64, (4, 4))
Convolution(64, (2, 2))
Dense(512)
:param in_shape: Shape of the observations perceived by the learner (the neural net input)
:param output_shape: Size of the action space (mapped to the number of output neurons)
:param device_id: Use None to let CNTK select the best available device,
-1 for CPU, >= 0 for GPU
(default: None)
:param learning_rate: Learning rate
(default: 0.00025, as per Mnih et al.)
:param momentum: Momentum, provided as momentum value for
averaging gradients without unit gain filter
Note that CNTK does not currently provide an implementation
of Graves' RmsProp with momentum.
It uses AdamSGD optimizer instead.
(default: 0, no momentum with RProp optimizer)
:param minibatch_size: Minibatch size
(default: 32, as per Mnih et al.)
:param n_workers: Number of concurrent worker for distributed training.
(default: 1, not distributed)
:param visualizer: Optional visualizer allowing the model to save summary data
(default: None, no visualization)
Ref: Mnih et al.: "Human-level control through deep reinforcement learning."
Nature 518.7540 (2015): 529-533.
"""
assert learning_rate > 0, 'learning_rate should be > 0'
assert 0. <= momentum < 1, 'momentum should be 0 <= momentum < 1'
QModel.__init__(self, in_shape, output_shape)
CntkModel.__init__(self, device_id, False, n_workers, visualizer)
self._nb_actions = output_shape
self._steps = 0
self._target_update_interval = update_interval
self._target = None
# Input vars
self._environment = input(in_shape, name='env',
dynamic_axes=(Axis.default_batch_axis()))
self._q_targets = input(1, name='q_targets',
dynamic_axes=(Axis.default_batch_axis()))
self._actions = input(output_shape, name='actions',
dynamic_axes=(Axis.default_batch_axis()))
# Define the neural network graph
self._model = self._build_model()(self._environment)
self._target = self._model.clone(
CloneMethod.freeze, {self._environment: self._environment}
)
# Define the learning rate
lr_schedule = learning_rate_schedule(learning_rate, UnitType.minibatch)
# AdamSGD optimizer
m_schedule = momentum_schedule(momentum)
vm_schedule = momentum_schedule(0.999)
l_sgd = adam(self._model.parameters, lr_schedule,
momentum=m_schedule,
unit_gain=True,
variance_momentum=vm_schedule)
if self.distributed_training:
raise NotImplementedError('ASGD not implemented yet.')
# _actions is a sparse 1-hot encoding of the actions done by the agent
q_acted = reduce_sum(self._model * self._actions, axis=0)
# Define the trainer with Huber Loss function
criterion = huber_loss(q_acted, self._q_targets, 1.0)
self._learner = l_sgd
self._trainer = Trainer(self._model, (criterion, None), l_sgd)
@property
def loss_val(self):
return self._trainer.previous_minibatch_loss_average
def _build_model(self):
with default_options(init=he_uniform(), activation=relu, bias=True):
model = Sequential([
Convolution((8, 8), 32, strides=(4, 4)),
Convolution((4, 4), 64, strides=(2, 2)),
Convolution((3, 3), 64, strides=(1, 1)),
Dense(512, init=he_normal(0.01)),
Dense(self._nb_actions, activation=None, init=he_normal(0.01))
])
return model
def train(self, x, q_value_targets, actions=None):
assert actions is not None, 'actions cannot be None'
# We need to add extra dimensions to shape [N, 1] => [N, 1]
if check_rank(q_value_targets.shape, 1):
q_value_targets = q_value_targets.reshape((-1, 1))
# Add extra dimensions to match shape [N, 1] required by one_hot
if check_rank(actions.shape, 1):
actions = actions.reshape((-1, 1))
# We need batch axis
if check_rank(x.shape, len(self._environment.shape)):
x = prepend_batch_axis(x)
self._trainer.train_minibatch({
self._environment: x,
self._actions: Value.one_hot(actions, self._nb_actions),
self._q_targets: q_value_targets
})
# Counter number of train calls
self._steps += 1
# Update the model with the target one
if (self._steps % self._target_update_interval) == 0:
self._target = self._model.clone(
CloneMethod.freeze, {self._environment: self._environment}
)
def evaluate(self, data, model=QModel.ACTION_VALUE_NETWORK):
# If evaluating a single sample, expand the minibatch axis
# (minibatch = 1, input_shape...)
if len(data.shape) == len(self.input_shape):
data = prepend_batch_axis(data) # Append minibatch dim
if model == QModel.TARGET_NETWORK:
predictions = self._target.eval({self._environment: data})
else:
predictions = self._model.eval({self._environment: data})
return predictions.squeeze()