def acquire_MLP(self):
"""
Our MLP uses a tanh to decide on the control input of the system.
"""
self._mlp = MLP(layers=[self.state_size] + self.internal_layers +
[self.policy_size],
dropout=self.dropout)
mlp_params, mlp_x, mlp_prediction, mlp_prediction_dropout = \
self._mlp.get()
for param_set in mlp_params:
self.params.extend(param_set)
self._prediction = self.boundsify(mlp_prediction)
self._prediction_dropout = self.boundsify(mlp_prediction_dropout)
self._x = mlp_x
def acquire_MLP(self):
"""
Our MLP uses a tanh to decide on the control input of the system.
"""
self._mlp = MLP(layers = [self.state_size] + self.internal_layers + [self.policy_size],
dropout=self.dropout)
mlp_params, mlp_x, mlp_prediction, mlp_prediction_dropout = \
self._mlp.get()
for param_set in mlp_params:
self.params.extend(param_set)
self._prediction = self.boundsify(mlp_prediction)
self._prediction_dropout = self.boundsify(mlp_prediction_dropout)
self._x = mlp_x
class PolicyModel(object):
"""
Use a Multi Layer Perceptron to discover a
policy for a dynamics by gradient descending
over a single timestep.
"""
@classmethod
def from_plant(cls, plant, **kwargs):
return cls(state_size=plant.num_states,
policy_size=plant.num_controls,
policy_bounds=plant.control_bounds,
dynamics=plant.theano_dynamics,
**kwargs)
def __init__(self,
state_size=2,
policy_size=1,
policy_bounds=(-40, 40),
dynamics=None,
policy_laziness=0.0000001,
internal_layers=[],
learning_rate=0.01,
dropout=0.0,
allow_input_downcast=True):
# constatants
if dynamics is None:
raise ValueError("Dynamics must be specified")
self.dynamics = dynamics
self.state_size = state_size
self.policy_size = policy_size
self.policy_bounds = policy_bounds
self.internal_layers = internal_layers
self.allow_input_downcast = allow_input_downcast
self.learning_rate = theano.shared(
np.float64(learning_rate).astype(floatX), name='learning_rate')
self.dropout = dropout
self.policy_laziness = theano.shared(
np.float64(policy_laziness).astype(floatX), name='policy_laziness')
self.predict = {}
self.teleportation = {}
self.cost = {}
self.target_value = {}
self.cost_fun = {}
# intialization functions
self.create_variables()
self.create_misc_functions('test', self._prediction)
self.create_misc_functions('train', self._prediction_dropout)
self.create_update_fun()
def boundsify(self, net_output):
zero_one = (net_output[0] + 1.0) / 2.0
u_min, u_max = self.policy_bounds
return u_min + zero_one * (u_max - u_min)
def acquire_MLP(self):
"""
Our MLP uses a tanh to decide on the control input of the system.
"""
self._mlp = MLP(layers=[self.state_size] + self.internal_layers +
[self.policy_size],
dropout=self.dropout)
mlp_params, mlp_x, mlp_prediction, mlp_prediction_dropout = \
self._mlp.get()
for param_set in mlp_params:
self.params.extend(param_set)
self._prediction = self.boundsify(mlp_prediction)
self._prediction_dropout = self.boundsify(mlp_prediction_dropout)
self._x = mlp_x
def create_variables(self):
self.params = []
self.acquire_MLP()
def create_misc_functions(self, name, prediction):
self.predict[name] = theano.function(
[self._x],
prediction,
allow_input_downcast=self.allow_input_downcast)
self.target_value[name] = T.vector()
new_x = self.dynamics(self._x, prediction)
self.teleportation[name] = theano.function(
[self._x], new_x, allow_input_downcast=self.allow_input_downcast)
self.cost[name] = T.sum((new_x - self.target_value[name])**2)
self.cost_fun[name] = theano.function(
[self._x, self.target_value[name]],
self.cost[name],
allow_input_downcast=self.allow_input_downcast)
def create_update_fun(self):
gparams = T.grad(self.cost['train'], self.params)
self.grad_fun = theano.function(
[self._x, self.target_value['train']],
gparams,
allow_input_downcast=self.allow_input_downcast)
updates = OrderedDict()
for gparam, param in zip(gparams, self.params):
updates[param] = param - gparam * self.learning_rate
self.update_fun = theano.function(
[self._x, self.target_value['train']],
self.cost['train'],
allow_input_downcast=self.allow_input_downcast,
updates=updates)
def set_learning_rate(self, rate):
self.learning_rate.set_value(np.float32(rate))
def reset_weights(self):
for param in self.params:
param.set_value((
np.random.standard_normal(param.get_value(borrow=True).shape) *
(1. / param.get_value(borrow=True).shape[0])).astype(floatX))
def controller(self):
def c(x, t):
if any(np.isnan(x)) or not all(np.abs(x) < 1e100):
return 0.0
prediction = self.predict['test'](np.array(x))
return prediction
return c
class PolicyModel(object):
"""
Use a Multi Layer Perceptron to discover a
policy for a dynamics by gradient descending
over a single timestep.
"""
@classmethod
def from_plant(cls, plant, **kwargs):
return cls(state_size=plant.num_states,
policy_size=plant.num_controls,
policy_bounds=plant.control_bounds,
dynamics=plant.theano_dynamics,
**kwargs)
def __init__(self, state_size = 2,
policy_size = 1,
policy_bounds = (-40,40),
dynamics = None,
policy_laziness = 0.0000001,
internal_layers = [],
learning_rate = 0.01,
dropout = 0.0,
allow_input_downcast=True):
# constatants
if dynamics is None:
raise ValueError("Dynamics must be specified")
self.dynamics = dynamics
self.state_size = state_size
self.policy_size = policy_size
self.policy_bounds = policy_bounds
self.internal_layers = internal_layers
self.allow_input_downcast = allow_input_downcast
self.learning_rate = theano.shared(np.float64(learning_rate).astype(floatX), name='learning_rate')
self.dropout = dropout
self.policy_laziness = theano.shared(np.float64(policy_laziness).astype(floatX), name='policy_laziness')
self.predict = {}
self.teleportation = {}
self.cost = {}
self.target_value = {}
self.cost_fun = {}
# intialization functions
self.create_variables()
self.create_misc_functions('test', self._prediction)
self.create_misc_functions('train', self._prediction_dropout)
self.create_update_fun()
def boundsify(self, net_output):
zero_one = (net_output[0] + 1.0) / 2.0
u_min, u_max = self.policy_bounds
return u_min + zero_one * (u_max-u_min)
def acquire_MLP(self):
"""
Our MLP uses a tanh to decide on the control input of the system.
"""
self._mlp = MLP(layers = [self.state_size] + self.internal_layers + [self.policy_size],
dropout=self.dropout)
mlp_params, mlp_x, mlp_prediction, mlp_prediction_dropout = \
self._mlp.get()
for param_set in mlp_params:
self.params.extend(param_set)
self._prediction = self.boundsify(mlp_prediction)
self._prediction_dropout = self.boundsify(mlp_prediction_dropout)
self._x = mlp_x
def create_variables(self):
self.params = []
self.acquire_MLP()
def create_misc_functions(self, name, prediction):
self.predict[name] = theano.function([self._x],
prediction,
allow_input_downcast=self.allow_input_downcast)
self.target_value[name] = T.vector()
new_x = self.dynamics( self._x, prediction )
self.teleportation[name] = theano.function([self._x],
new_x,
allow_input_downcast=self.allow_input_downcast)
self.cost[name] = T.sum((new_x - self.target_value[name])**2)
self.cost_fun[name] = theano.function([self._x, self.target_value[name]],
self.cost[name],
allow_input_downcast=self.allow_input_downcast)
def create_update_fun(self):
gparams = T.grad(self.cost['train'], self.params)
self.grad_fun = theano.function([self._x, self.target_value['train']],
gparams,
allow_input_downcast=self.allow_input_downcast)
updates=OrderedDict()
for gparam, param in zip(gparams, self.params):
updates[param] = param - gparam * self.learning_rate
self.update_fun = theano.function([self._x, self.target_value['train']],
self.cost['train'],
allow_input_downcast=self.allow_input_downcast,
updates = updates)
def set_learning_rate(self, rate):
self.learning_rate.set_value(np.float32(rate))
def reset_weights(self):
for param in self.params:
param.set_value(
(np.random.standard_normal(param.get_value(borrow=True).shape) *
(1./param.get_value(borrow=True).shape[0])).astype(floatX))
def controller(self):
def c(x,t):
if any(np.isnan(x)) or not all(np.abs(x) < 1e100):
return 0.0
prediction = self.predict['test'](np.array(x))
return prediction
return c
