def __init__(self,
state_shape,
num_actions,
epsilon=1.0,
epsilon_min=0.1,
epsilon_iter=100000,
discount=0.99,
lrate=1e-3,
batch_size=100,
q_update_iter=1000,
capacity=50000):
if not isinstance(state_shape, tuple):
raise AssertionError('state_shape must be of type <tuple>.')
elif len(state_shape) == 0:
raise AssertionError('No state space dimensions provided.')
elif num_actions == 0:
raise ValueError('Number of actions must be > 0.')
elif epsilon_min is not None:
assert epsilon_min < epsilon, 'Epsilon(min) must be < epsilon(max).'
elif capacity < batch_size:
raise ValueError('Replay capacity must be > batch_size.')
self.state_shape = state_shape
self.num_actions = num_actions
self.q_network = build_network(state_shape, num_actions)
self.q_targets = build_network(state_shape, num_actions)
self.epsilon = epsilon
self.epsilon_max = epsilon # How greedy the policy is
self.epsilon_min = epsilon_min
self.epsilon_iter = float(epsilon_iter)
self.discount = discount
self.lr = lrate
self.batch_size = batch_size # How many samples to draw from buffer
self.q_update_iter = q_update_iter # Update the q_target every C iter
self.step = 0
self.replay_buffer = ReplayBuffer(capacity, state_shape)
# Build training and sampling functions
s0_sym = nn.get_all_layers(self.q_network)[0].input_var
s1_sym = nn.get_all_layers(self.q_targets)[0].input_var
a_sym = T.icol('actions') #(n, 1)
r_sym = T.col('rewards')
t_sym = T.col('terminal_state')
sym_vars = [s0_sym, a_sym, r_sym, s1_sym, t_sym]
# Training phase uses non-deterministic mapping
loss = T.sum(self._build_loss(*sym_vars, deterministic=False))
params = nn.get_all_params(self.q_network, trainable=True)
updates = lasagne.updates.adam(loss, params, self.lr, beta1=0.9)
self.train_fn = theano.function(sym_vars, loss, updates=updates)
# Build function for sampling from DQN
pred = nn.get_output(self.q_network, deterministic=True)
self.pred_fn = theano.function([s0_sym], pred)
def __init__(self, state_shape, num_actions, action_scale, lr, tau):
self.state_shape = state_shape
self.num_actions = num_actions
self.action_scale = action_scale
self.tau = tau
# Build networks, then initialize their weights to be equal
sym_s0 = get_symbolic_var(state_shape)('s0')
sym_s1 = get_symbolic_var(state_shape)('s1')
self.network = self._build_network(sym_s0)
self.targets = self._build_network(sym_s1)
self.update_target(tau=1.0)
# For making predictions via current and target networks
a_pred = nn.get_output(self.network)
self.predict_fn = theano.function([sym_s0], a_pred)
self.target_fn = theano.function([sym_s1], nn.get_output(self.targets))
# The policy is updated by following gradients from critic network.
# In theano, this is done by specifying the 'known_gradients' parameter
# in T.grad, without giving an explicit scalar cost
action_grads = T.col('action_grads')
known_grads = {a_pred: action_grads}
params = nn.get_all_params(self.network, trainable=True)
grads = [-T.grad(None, p, known_grads=known_grads) for p in params]
updates = lasagne.updates.adam(grads, params, lr)
train = theano.function([sym_s0, action_grads], grads, updates=updates)
self.train_fn = train
def test_broadcast_arguments(self):
m = Module()
m.random = RandomStreams(utt.fetch_seed())
low = tensor.vector()
high = tensor.col()
out = m.random.uniform(low=low, high=high)
assert out.ndim == 2
m.f = Method([low, high], out)
made = m.make()
made.random.initialize()
rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
numpy_rng = numpy.random.RandomState(int(rng_seed))
low_vals = [
numpy.asarray([-5, .5, 0, 1], dtype=config.floatX),
numpy.asarray([.9], dtype=config.floatX),
numpy.asarray([-5, .5, 0, 1], dtype=config.floatX) ]
high_vals = [
numpy.asarray([[1.]], dtype=config.floatX),
numpy.asarray([[1.], [1.1], [1.5]], dtype=config.floatX),
numpy.asarray([[1.], [1.1], [1.5]], dtype=config.floatX) ]
val0 = made.f(low_vals[0], high_vals[0])
val1 = made.f(low_vals[1], high_vals[1])
val2 = made.f(low_vals[2], high_vals[2])
numpy_val0 = numpy_rng.uniform(low=low_vals[0], high=high_vals[0])
numpy_val1 = numpy_rng.uniform(low=low_vals[1], high=high_vals[1])
numpy_val2 = numpy_rng.uniform(low=low_vals[2], high=high_vals[2])
assert numpy.allclose(val0, numpy_val0)
assert numpy.allclose(val1, numpy_val1)
assert numpy.allclose(val2, numpy_val2)
def get_loss_sarsa_function(self):
#args
self.states = T.matrix('state')
self.actions = T.icol('action')
self.next_states = T.matrix('next_state')
self.next_actions = T.icol('next_action')
self.rewards = T.col('reward')
#q(s,a)
actionmask = T.eq(
T.arange(self.nactions).reshape((1, -1)),
self.actions.reshape((-1, 1))).astype(theano.config.floatX)
q_action = (get_output(self.network, self.states) *
actionmask).sum(axis=1).reshape((-1, 1))
#q(s_next,a_next)
next_actionmask = T.eq(
T.arange(self.nactions).reshape((1, -1)),
self.next_actions.reshape((-1, 1))).astype(theano.config.floatX)
next_q_action = (get_output(self.network, self.next_states) *
next_actionmask).sum(axis=1).reshape((-1, 1))
#loss = target - qvalue
loss = (self.rewards + self.discount * next_q_action - q_action)
#mse
mse = 0.5 * loss**2
#sum loss
return T.sum(mse)
def test_broadcast_arguments(self):
m = Module()
m.random = RandomStreams(utt.fetch_seed())
low = tensor.vector()
high = tensor.col()
out = m.random.uniform(low=low, high=high)
assert out.ndim == 2
m.f = Method([low, high], out)
made = m.make()
made.random.initialize()
rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
numpy_rng = numpy.random.RandomState(int(rng_seed))
low_vals = [
numpy.asarray([-5, .5, 0, 1], dtype=config.floatX),
numpy.asarray([.9], dtype=config.floatX),
numpy.asarray([-5, .5, 0, 1], dtype=config.floatX)
]
high_vals = [
numpy.asarray([[1.]], dtype=config.floatX),
numpy.asarray([[1.], [1.1], [1.5]], dtype=config.floatX),
numpy.asarray([[1.], [1.1], [1.5]], dtype=config.floatX)
]
val0 = made.f(low_vals[0], high_vals[0])
val1 = made.f(low_vals[1], high_vals[1])
val2 = made.f(low_vals[2], high_vals[2])
numpy_val0 = numpy_rng.uniform(low=low_vals[0], high=high_vals[0])
numpy_val1 = numpy_rng.uniform(low=low_vals[1], high=high_vals[1])
numpy_val2 = numpy_rng.uniform(low=low_vals[2], high=high_vals[2])
assert numpy.allclose(val0, numpy_val0)
assert numpy.allclose(val1, numpy_val1)
assert numpy.allclose(val2, numpy_val2)
def __init__(self, args):
reward = T.col('r')
action = T.icol('a')
terminal = T.icol('t')
discount = T.scalar('gamma')
learningRate = T.scalar('lr')
rho = T.scalar('rho')
epsilon = T.scalar('eps')
rng = np.random.RandomState(42)
self.batchNb = args.batchSize
#convLayers = [[(8,8),(4,4),64],
# [(4,4),(2,2),128],
# [(3,3),(1,1),256],
# [(3,3),(1,1),512]]
#fcl = [1024, 6]
convLayers = [[(8,8),(4,4),64],
[(4,4),(2,2),128],
[(3,3),(1,1),256],
[(3,3),(1,1),256]]
fcl = [1024, args.actionNb]
self.q1 = NetStruct(convLayers, fcl, (4,100,100), rng, args)
self.q2 = NetStruct(convLayers, fcl, (4,100,100), rng, args)
self.q2.setParams(self.q1)
self.states = theano.shared(np.zeros((args.batchSize,4,100,100), dtype='float32'))
self.states2 = theano.shared(np.zeros((args.batchSize,4,100,100), dtype='float32'))
self.actions = theano.shared(np.zeros((args.batchSize,1), dtype='int32'), broadcastable=(False,True))
self.rewards = theano.shared(np.zeros((args.batchSize,1), dtype='float32'), broadcastable=(False,True))
self.terminals = theano.shared(np.zeros((args.batchSize,1), dtype='int32'), broadcastable=(False,True))
self.learningRate = theano.shared(np.array(args.learningRate, dtype='float32'))
self.rho = theano.shared(np.array(args.rmsPropRho, dtype='float32'))
self.epsilon = theano.shared(np.array(args.rmsPropEpsilon, dtype='float32'))
self.discount = theano.shared(np.array(args.discountFactor, dtype='float32'))
loss = self.QLoss(self.q1.output, self.q2.output, action, reward, terminal, discount)
params = self.q1.getParams()
updates = self.rmsProp(loss, params, rho, epsilon, learningRate)
self.train_model = theano.function(
[],
loss,
updates=updates,
givens = {
self.q1.input: self.states,
self.q2.input: self.states2,
action: self.actions,
reward: self.rewards,
terminal: self.terminals,
discount: self.discount,
learningRate: self.learningRate,
rho: self.rho,
epsilon: self.epsilon
}
)
def createGradientFunctions(self):
#Create the Theano variables
W1,W2,W3,W4,W5,W6,x,eps = T.dmatrices("W1","W2","W3","W4","W5","W6","x","eps")
#Create biases as cols so they can be broadcasted for minibatches
b1,b2,b3,b4,b5,b6 = T.dcols("b1","b2","b3","b4","b5","b6")
z1 = T.col("z1")
if self.continuous:
#convolve x
# no_filters = 100, stride = 4, filter_size = 50
h_encoder = T.tanh(T.dot(W1,x) + b1)
#h_encoder = T.dot(W1,x) + b1
else:
h_encoder = T.tanh(T.dot(W1,x) + b1)
mu_encoder = T.dot(W2,h_encoder) + b2
log_sigma_encoder = 0.5*(T.dot(W3,h_encoder) + b3)
mu_encoder = T.dot(W2,h_encoder) + b2
log_sigma_encoder = 0.5*(T.dot(W3,h_encoder) + b3)
#Find the hidden variable z
z = mu_encoder + T.exp(log_sigma_encoder)*eps
prior = 0.5* T.sum(1 + 2*log_sigma_encoder - mu_encoder**2 - T.exp(2*log_sigma_encoder))
#Set up decoding layer
if self.continuous:
h_decoder = T.nnet.softplus(T.dot(W4,z) + b4)
h_dec = T.nnet.softplus(T.dot(W4,z1) + b4)
#h_decoder = T.dot(W4,z) + b4
#h_dec = T.dot(W4,z1) + b4
mu_decoder = T.tanh(T.dot(W5,h_decoder) + b5)
mu_dec = T.tanh(T.dot(W5,h_dec) + b5)
log_sigma_decoder = 0.5*(T.dot(W6,h_decoder) + b6)
logpxz = T.sum(-(0.5 * np.log(2 * np.pi) + log_sigma_decoder) - 0.5 * ((x - mu_decoder) / T.exp(log_sigma_decoder))**2)
gradvariables = [W1,W2,W3,W4,W5,W6,b1,b2,b3,b4,b5,b6]
else:
h_decoder = T.tanh(T.dot(W4,z) + b4)
y = T.nnet.sigmoid(T.dot(W5,h_decoder) + b5)
logpxz = -T.nnet.binary_crossentropy(y,x).sum()
gradvariables = [W1,W2,W3,W4,W5,b1,b2,b3,b4,b5]
logp = logpxz + prior
#Compute all the gradients
derivatives = T.grad(logp,gradvariables)
#Add the lowerbound so we can keep track of results
derivatives.append(logp)
self.get_z = th.function(gradvariables+[x,eps],z,on_unused_input='ignore')
self.generate = th.function(gradvariables+[z1,x,eps],mu_dec,on_unused_input='ignore')
self.predict = th.function(gradvariables+[x,eps],mu_decoder,on_unused_input='ignore')
self.gradientfunction = th.function(gradvariables + [x,eps], derivatives, on_unused_input='ignore')
self.lowerboundfunction = th.function(gradvariables + [x,eps], logp, on_unused_input='ignore')
def test_ndim_mismatch(self):
rng = numpy.random.RandomState(utt.fetch_seed())
data = rng.rand(5).astype(self.dtype)
x = self.shared(data)
y = tensor.col('y', self.dtype)
cond = theano.tensor.iscalar('cond')
self.assertRaises(TypeError, ifelse, cond, x, y)
self.assertRaises(TypeError, ifelse, cond, y, x)
def testDataSet(self, dataSet_, dataLabels_):
dataSet = T.matrix("dataSet")
labels = T.col("labels")
svLabels = T.col("svLabels")
gamma = T.dscalar("gamma")
svs = T.matrix("supportVectors")
svAlphas = T.matrix("svAlphas")
b = T.dscalar("b")
# we need to transpose the result because the results of the per-row actions are usually columns
errorVec = theano.scan(lambda row, realLabel : self.testDataSet_inner_(svs, row, gamma, svLabels, svAlphas, b, realLabel), sequences=[dataSet, labels])[0]
errors = T.sum(errorVec)
inputs = [dataSet, labels, svs, svLabels, gamma, svAlphas, b]
compErrors = theano.function(inputs=inputs, outputs=errors, on_unused_input='ignore')
gamma_ = 1/(-1*self.Training.UsedKernel[1]**2)
numErrors = compErrors(dataSet_, dataLabels_, self.Training.SupportVectors, self.Training.SVLabels, gamma_, self.Training.Alphas[self.Training.SVIndices], self.Training.B.item(0))
return float(numErrors) / float(dataSet_.shape[0])
def __init__(self):
self.dt=1
self.xdim=1
self.udim=1
self.r=1
self.delay=T.bscalar()
self.delay2=T.bscalar()
self.x=T.matrix()
self.u=T.col()
self.xu_flat=T.concatenate([T.flatten(self.x.T),T.flatten(self.u)])
def __init__(self, numpy_rng, theano_rng = None, first_layer_type = 'bernoulli', mean_doc_size = 1, n_ins = 784, mid_layer_sizes=[200], inner_code_length = 10):
"""This class is made to support a variable number of layers.
:type numpy_rng: numpy.random.RandomState
:param numpy_rng: numpy random number generator used to draw initial
weights
:type theano_rng: theano.tensor.shared_randomstreams.RandomStreams
:param theano_rng: Theano random generator; if None is given one is
generated based on a seed drawn from `rng`
:type n_ins: int
:param n_ins: dimension of the input (and autoencoder output, y) of the SMH
:type n_code_length: int
:param n_code_length: how many codes to squash down to in the middle layer
"""
self.first_layer_type = first_layer_type;
self.mean_doc_size = mean_doc_size;
self.sigmoid_layers = []
self.rbm_layers = []
self.params = []
self.n_ins = n_ins
self.inner_code_length = inner_code_length
self.mid_layer_sizes = list(mid_layer_sizes)
self.numpy_rng = numpy_rng
self.theano_rng = RandomStreams(numpy_rng.randint(2**30))
# allocate symbolic variables for the data
if (theano.config.floatX == "float32"):
self.x = T.matrix('x') #
self.x_sums = T.col('x_sums')
self.y = T.matrix('y') # the output (after finetuning) should /look the same as the input
else:
if (theano.config.floatX == "float64"):
self.x = T.dmatrix('x') #
self.x_sums = T.dcol('x_sums')
self.y = T.dmatrix('y') # the output (after finetuning) should look the same as the input
else:
raise Exception #not sure whats up here..
# The SMH is an MLP, for which all weights of intermediate layers are shared with a
# different RBM. We will first construct the SMH as a deep multilayer perceptron, and
# when constructing each sigmoidal layer we also construct an RBM that shares weights
# with that layer. During pretraining we will train these RBMs (which will lead
# to chainging the weights of the MLP as well) During finetuning we will finish
# training the SMH by doing stochastic gradient descent on the MLP.
self.init_layers()
def __init__(self, input_width, input_height, output_dim, num_frames, batch_size):
self.input_width = input_width
self.input_height = input_height
self.output_dim = output_dim
self.num_frames = num_frames
self.batch_size = batch_size
self.gamma = 0.99 # discount factor
self.rho = 0.99
self.lr = 0.00025 # learning rate
self.momentum = 0.95
self.freeze_targets = True
self.l_out = self.build_network(input_width, input_height, output_dim, num_frames, batch_size)
if self.freeze_targets:
self.next_l_out = self.build_network(input_width, input_height, output_dim, num_frames, batch_size)
self.reset_q_hat()
states = T.tensor4('states')
next_states = T.tensor4('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
# terminals = T.icol('terminals')
self.states_shared = theano.shared(np.zeros((batch_size, num_frames, input_height, input_width), dtype=theano.config.floatX))
self.next_states_shared = theano.shared(np.zeros((batch_size, num_frames, input_height, input_width), dtype=theano.config.floatX))
self.rewards_shared = theano.shared(np.zeros((batch_size, 1), dtype=theano.config.floatX), broadcastable=(False,True))
self.actions_shared = theano.shared(np.zeros((batch_size, 1), dtype='int32'), broadcastable=(False,True))
# self.terminals_shared = theano.shared(np.zeros((batch_size, 1), dtype='int32'), broadcastable=(False,True))
q_vals = self.l_out.get_output(states / 255.0)
if self.freeze_targets:
next_q_vals = self.next_l_out.get_output(next_states / 255.0)
else:
next_q_vals = self.l_out.get_output(next_states / 255.0)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
target = rewards + self.gamma * T.max(next_q_vals, axis=1, keepdims=True)
diff = target - q_vals[T.arange(batch_size), actions.reshape((-1,))].reshape((-1,1))
loss = T.mean(diff ** 2)
params = lasagne.layers.helper.get_all_params(self.l_out)
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
# terminals: self.terminals_shared
}
if self.momentum > 0:
updates = rmsprop_nesterov(loss, params, self.lr, self.rho, self.momentum, 1e-2)
else:
updates = lasagne.updates.rmsprop(loss, params, self.lr, self.rho, 1e-6)
self._train = theano.function([], [loss, q_vals], updates=updates, givens=givens)
self._q_vals = theano.function([], q_vals, givens={ states: self.states_shared })
def test_ndim_mismatch(self):
rng = np.random.RandomState(utt.fetch_seed())
data = rng.rand(5).astype(self.dtype)
x = self.shared(data)
y = tensor.col("y", self.dtype)
cond = theano.tensor.iscalar("cond")
with pytest.raises(TypeError):
ifelse(cond, x, y)
with pytest.raises(TypeError):
ifelse(cond, y, x)
def neural_tensor_network():
# tensor params
subj = T.col('e_1')
targets = T.matrix('e_2')
W = T.tensor3('W')
# neural net params
u = T.col('u')
V = T.matrix('V')
b = T.col('b')
# tensor
h = subj.T.dot(W).dot(targets)
# neural net
d = subj.shape[0]
V_subj = V[:, :d].dot(subj)
V_targ = V[:, d:].dot(targets)
activations = T.tanh(h + V_subj + V_targ + b)
score = u.T.dot(activations).reshape((-1, 1))
margins = score[0] - score[1:]
cost = T.min(T.concatenate((T.ones_like(margins), margins), axis=1), axis=1).mean()
gsubj, gtargets, gW, gu, gV, gb = T.grad(cost, [subj, targets, W, u, V, b])
print 'Compiling NTN score'
score = theano.function([subj, W, targets, u, V, b], score, name='NTN Score',
mode='FAST_RUN')
print 'Compiling NTN fprop'
fprop = theano.function([subj, W, targets, u, V, b], cost, name='NTN fprop',
mode='FAST_RUN')
print 'Compiling NTN bprop'
bprop = theano.function([subj, W, targets, u, V, b],
outputs=[gsubj, gW, gtargets, gu, gV, gb],
name='NTN bprop', mode='FAST_RUN')
return {'score': score, 'fprop': fprop, 'bprop': bprop}
def build_finetune_functions(self, batch_size, learning_rate):
'''Generates a function `train` that implements one step of finetuning, a function
`validate` that computes the error on a batch from the validation set, and a function
`test` that computes the error on a batch from the testing set
:type batch_size: int
:param batch_size: size of a minibatch
:type learning_rate: float
:param learning_rate: learning rate used during finetune stage
'''
train_set_x = T.matrix('train_set_x')
train_set_x_sums = T.col('train_set_x_sums')
valid_set_x = T.matrix('valid_set_x')
valid_set_x_sums = T.col('valid_set_x_sums')
test_set_x = T.matrix('test_set_x')
test_set_x_sums = T.col('test_set_x_sums')
# compute the gradients with respect to the model parameters
gparams = T.grad(self.finetune_cost, self.params)
# compute list of fine-tuning updates
updates = {}
for param, gparam in zip(self.params, gparams):
updates[param] = param - gparam*learning_rate
train_fn = theano.function(inputs = [train_set_x, train_set_x_sums],
outputs = self.finetune_cost,
updates = updates,
givens = { self.x : train_set_x,
self.x_sums : train_set_x_sums })
valid_score_i = theano.function([valid_set_x, valid_set_x_sums], self.finetune_cost,
givens = { self.x : valid_set_x,
self.x_sums : valid_set_x_sums })
test_score_i = theano.function([test_set_x, test_set_x_sums], self.finetune_cost,
givens = { self.x : test_set_x,
self.x_sums : test_set_x_sums })
return train_fn, valid_score_i, test_score_i
def setup_theano(self):
# for numpy optimization
oneCol = T.col("oneCol")
pi_t = T.col("pi_t")
z_t = T.col("z_t")
z_t1 = z_t.reshape((self.numKeypoints, 2))
pts = T.concatenate((z_t1, oneCol), axis=1)
A_t_ = T.matrix("A_t_")
r_t_ = T.dot(A_t_, pts.transpose()).transpose()
r_t1_ = r_t_[:, 0:2].reshape((2 * self.numKeypoints, 1))
diff_ = pi_t * (r_t1_ - self.mu)
difft_ = diff_.reshape((1, 2 * self.numKeypoints))
cost_1 = T.dot(difft_, diff_)
# cost_1 = theano.printing.Print('cost is:')(cost_1)
cost_ = T.max(cost_1)
A_t_grad_ = T.grad(cost=cost_, wrt=A_t_)
A_t_grad_ = T.basic.set_subtensor(A_t_grad_[2, :], 0)
self.cost = theano.function(inputs=[A_t_, pi_t, z_t, oneCol], outputs=[cost_, A_t_grad_])
def __init__(self, state_shape, num_actions, discount, lr, tau, l2_decay):
self.state_shape = state_shape
self.num_actions = num_actions
self.discount = discount
self.tau = tau
# Initialize some symbolic variables to interface with graphs easier
sym_s0 = get_symbolic_var(state_shape)('s0')
sym_a0 = T.col('policy_actions')
sym_s1 = get_symbolic_var(state_shape)('s1')
sym_a1 = T.col('target_actions')
sym_r = T.col('rewards')
sym_t = T.col('terminal_state')
sym_vars = [sym_s0, sym_a0, sym_s1, sym_a1, sym_r, sym_t]
self.network = self._build_network(sym_s0, sym_a0)
self.targets = self._build_network(sym_s1, sym_a1)
self.update_target(tau=1.0)
# Functions for sampling from current and target Q-functions
q_pred = nn.get_output(self.network)
q_target = nn.get_output(self.targets)
self.predict_fn = theano.function([sym_s0, sym_a0], q_pred)
self.target_fn = theano.function([sym_s1, sym_a1], q_target)
# Calculate action gradients for updating actor / policy
grads = T.grad(T.mean(q_pred), sym_a0)
self.action_grads = theano.function([sym_s0, sym_a0], grads)
# Build critic training function; loss is similar to DQN, where
# it's the mean squared error between Q and target Q values
yi = sym_r + (1. - sym_t) * self.discount * q_target
loss = T.mean(T.sqr(yi - q_pred))
loss += regularize_network_params(self.network, l2) * l2_decay
params = nn.get_all_params(self.network, trainable=True)
updates = lasagne.updates.adam(loss, params, lr)
self.train_fn = theano.function(sym_vars, loss, updates=updates)
def setup_theano(self):
#for numpy optimization
oneCol = T.col('oneCol')
pi_t = T.col('pi_t')
z_t = T.col('z_t')
z_t1 = z_t.reshape((self.numKeypoints, 2))
pts = T.concatenate((z_t1, oneCol), axis=1)
A_t_ = T.matrix('A_t_')
r_t_ = T.dot(A_t_, pts.transpose()).transpose()
r_t1_ = r_t_[:,0:2].reshape((2*self.numKeypoints,1))
diff_ = pi_t * (r_t1_ - self.mu)
difft_ = diff_.reshape((1, 2 * self.numKeypoints))
cost_1 = T.dot(difft_,diff_)
#cost_1 = theano.printing.Print('cost is:')(cost_1)
cost_ = T.max(cost_1)
A_t_grad_ = T.grad(cost=cost_, wrt=A_t_)
A_t_grad_ = T.basic.set_subtensor(A_t_grad_[2,:],0)
self.cost = theano.function(inputs=[A_t_, pi_t, z_t, oneCol],
outputs=[cost_, A_t_grad_])
def pretraining_functions(self, batch_size, method, pretrain_lr, k):
''' Generates a list of functions, for performing one step of gradient descent at a
given layer. The function will require as input a minibatch of data, and to train an
RBM you just need to iterate, calling the corresponding function on all minibatches.
:type batch_size: int
:param batch_size: size of a [mini]batch
:type method: string
:param method: type of Gibbs sampling to perform: 'cd' (default) or 'pcd'
:type k: int
:param k: number of Gibbs steps to do in CD-k / PCD-k
;type finetune_lr: float
;param finetune_lr: the 'learning rate' to use during finetuning phase
'''
learning_rate = T.scalar('lr') # learning rate to use
#learning_rate.value = pretrain_lr
# i *think* the following is equivalent to above.. doing this because i can't see where lr gets a value at all
#learning_rate = theano.shared(pretrain_lr, 'learning_rate')
train_set_x = T.matrix('train_set_x')
train_set_x_sums = T.col('train_set_x_sums')
pretrain_fns = []
for rbm in self.rbm_layers:
if method == 'pcd':
# initialize storage for the persistent chain (state = hidden layer of chain)
persistent_chain = theano.shared(numpy.zeros((batch_size,rbm.n_hidden),dtype=theano.config.floatX))
# get the cost and the gradient corresponding to one step of PCD-k
cost,updates = rbm.get_cost_updates(lr=learning_rate, persistent=persistent_chain, k=k)
else:
# default = use CD instead
cost,updates = rbm.get_cost_updates(lr=learning_rate)
# compile the theano function
fn = theano.function(inputs = [train_set_x,train_set_x_sums,
theano.Param(learning_rate, default = 0.1)],
outputs = cost,
updates = updates,
givens = {self.x:train_set_x,
self.x_sums:train_set_x_sums}
# uncomment the following line to perform debugging:
# ,mode=theano.compile.debugmode.DebugMode(stability_patience=5)
)
# append `fn` to the list of functions
pretrain_fns.append(fn)
return pretrain_fns
def transE_model():
'''
Note X_S is a column and X_T is a matrix so that broadcasting occurs
across the columns of X_T (this allows batching X_T with negatives,
for example.
'''
# construct theano expression graph
X_s = T.col('X_s')
W = T.matrix('W')
X_t = T.matrix('X_t')
rels = W[:, :, None].transpose(1, 0, 2)
# Computes x_{r_1} + x_{r_{2}} + ... + x_{r_n} - X_{t}
results, updates = theano.scan(fn=lambda rel, v: rel + v,
outputs_info=-X_t, sequences=[rels])
# score is always a column vector
score = T.sum((X_s + results[-1]) ** 2, axis=0).reshape((-1, 1))
margins = 1. + score[0] - score[1:]
# zero out negative entries
pos_parts = margins * (margins > 0)
# we are using online Maximizer, so the objective is negated
cost = -pos_parts.mean()
gX_s, gW, gX_t = T.grad(cost, [X_s, W, X_t])
print 'Compiling TransE score'
# return negative score since this is a ranking
score = theano.function([X_s, W, X_t], -score, name='transE Score',
mode='FAST_RUN')
score.trust_input = True
print 'Compiling TransE fprop'
fprop = theano.function([X_s, W, X_t], cost, name='transE fprop',
mode='FAST_RUN')
fprop.trust_input = True
print 'Compiling TransE bprop'
bprop = theano.function([X_s, W, X_t],
outputs=[gX_s, gW, gX_t],
name='transE bprop', mode='FAST_RUN')
bprop.trust_input = True
return {'score': score, 'fprop': fprop, 'bprop': bprop}
def test_wrong_broadcast(self):
a = tt.col()
increment = tt.vector()
# These symbolic graphs legitimate, as long as increment has exactly
# one element. So it should fail at runtime, not at compile time.
rng = numpy.random.RandomState(utt.fetch_seed())
def rng_randX(*shape):
return rng.rand(*shape).astype(theano.config.floatX)
for op in (tt.set_subtensor, tt.inc_subtensor):
for base in (a[:], a[0]):
out = op(base, increment)
f = theano.function([a, increment], out)
# This one should work
f(rng_randX(3, 1), rng_randX(1))
# These ones should not
self.assertRaises(ValueError, f, rng_randX(3, 1), rng_randX(2))
self.assertRaises(ValueError, f, rng_randX(3, 1), rng_randX(3))
self.assertRaises(ValueError, f, rng_randX(3, 1), rng_randX(0))
def test_wrong_broadcast(self):
a = tt.col()
increment = tt.vector()
# These symbolic graphs legitimate, as long as increment has exactly
# one element. So it should fail at runtime, not at compile time.
rng = numpy.random.RandomState(utt.fetch_seed())
def rng_randX(*shape):
return rng.rand(*shape).astype(theano.config.floatX)
for op in (tt.set_subtensor, tt.inc_subtensor):
for base in (a[:], a[0]):
out = op(base, increment)
f = theano.function([a, increment], out)
# This one should work
f(rng_randX(3, 1), rng_randX(1))
# These ones should not
self.assertRaises(ValueError, f, rng_randX(3, 1), rng_randX(2))
self.assertRaises(ValueError, f, rng_randX(3, 1), rng_randX(3))
self.assertRaises(ValueError, f, rng_randX(3, 1), rng_randX(0))
def __init__(self, env, args, rng, name = "DQNLasagne"):
""" Initializes a network based on the Lasagne Theano framework.
Args:
env (AtariEnv): The envirnoment in which the agent actuates.
args (argparse.Namespace): All settings either with a default value or set via command line arguments.
rng (mtrand.RandomState): Initialized Mersenne Twister pseudo-random number generator.
name (str): The name of the network object.
Note:
This function should always call the base class first to initialize
the common values for the networks.
"""
_logger.info("Initialize object of type " + str(type(self).__name__))
super(DQNLasagne, self).__init__(env, args, rng, name)
self.input_shape = (self.batch_size, self.sequence_length, args.frame_width, args.frame_height)
self.dummy_batch = np.zeros(self.input_shape, dtype=np.uint8)
lasagne.random.set_rng(self.rng)
self.network = self._create_layer()
# TODO: Load weights from pretrained network?!
if not self.args.load_weights == None:
self.load_weights(self.args.load_weights)
if self.target_update_frequency > 0:
self.target_network = self._create_layer()
self._copy_theta()
states = T.tensor4('states')
followup_states = T.tensor4('followup_states')
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
self.states_shared = theano.shared(
np.zeros(self.input_shape, dtype=theano.config.floatX)
)
self.followup_states_shared = theano.shared(
np.zeros(self.input_shape, dtype=theano.config.floatX)
)
self.rewards_shared = theano.shared(
np.zeros((self.batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True)
)
self.actions_shared = theano.shared(
np.zeros((self.batch_size, 1), dtype='int32'),
broadcastable=(False, True)
)
self.terminals_shared = theano.shared(
np.zeros((self.batch_size, 1), dtype='int32'),
broadcastable=(False, True)
)
qvalues = lasagne.layers.get_output(
self.network,
self._prepare_network_input(states)
)
if self.target_update_frequency > 0:
qvalues_followup_states = lasagne.layers.get_output(
self.target_network,
self._prepare_network_input(followup_states)
)
else:
qvalues_followup_states = lasagne.layers.get_output(
self.network,
self._prepare_network_input(followup_states)
)
qvalues_followup_states = theano.gradient.disconnected_grad(qvalues_followup_states)
targets = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount_rate *
T.max(qvalues_followup_states, axis=1, keepdims=True)
)
errors = targets - qvalues[
T.arange(self.batch_size),
actions.reshape((-1,))].reshape((-1, 1))
if self.clip_error > 0:
quadratic_part = T.minimum(abs(errors), self.clip_error)
linear_part = abs(errors) - quadratic_part
cost_function = T.sum(0.5 * quadratic_part ** 2 + self.clip_error * linear_part)
else:
cost_function = T.sum(0.5 * errors ** 2)
self.params = lasagne.layers.helper.get_all_params(self.network)
self.observations = {
states: self.states_shared,
followup_states: self.followup_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
self._set_optimizer(cost_function)
if self.momentum > 0:
self.optimizer = lasagne.updates.apply_momentum(
self.optimizer,
None,
self.momentum
)
_logger.debug("Compiling _theano_train")
self._theano_train = theano.function(
[],
[cost_function, qvalues],
updates=self.optimizer,
givens=self.observations)
_logger.debug("Compiling _theano_get_Q")
self._theano_get_Q = theano.function(
[],
qvalues,
givens={states: self.states_shared})
self.callback = None
_logger.debug("%s" % self)
def __init__(self, n_in, n_out, state_bounds, action_bounds, reward_bound,
settings_):
super(DeepNNDropoutCritic,
self).__init__(n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_)
self._dropout_p = settings_['dropout_p']
# data types for model
self._State = T.matrix("State")
self._State.tag.test_value = np.random.rand(self._batch_size,
self._state_length)
self._ResultState = T.matrix("ResultState")
self._ResultState.tag.test_value = np.random.rand(
self._batch_size, self._state_length)
self._Reward = T.col("Reward")
self._Reward.tag.test_value = np.random.rand(self._batch_size, 1)
self._Action = T.matrix("Action")
self._Action.tag.test_value = np.random.rand(self._batch_size,
self._action_length)
# create a small convolutional neural network
input = lasagne.layers.InputLayer((None, self._state_length),
self._State)
self._stateInputVar = input.input_var
# network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
"""
network = lasagne.layers.DenseLayer(
network, num_units=256,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
"""
network = lasagne.layers.DenseLayer(
input,
num_units=128,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network,
p=self._dropout_p,
rescale=True)
network = lasagne.layers.DenseLayer(
network,
num_units=64,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network,
p=self._dropout_p,
rescale=True)
network = lasagne.layers.DenseLayer(
network,
num_units=32,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network,
p=self._dropout_p,
rescale=True)
network = lasagne.layers.DenseLayer(
network,
num_units=16,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
self._critic = lasagne.layers.DenseLayer(
network, num_units=1, nonlinearity=lasagne.nonlinearities.linear)
# self._b_o = init_b_weights((n_out,))
# networkAct = lasagne.layers.InputLayer((None, self._state_length), self._State)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
"""
networkAct = lasagne.layers.DenseLayer(
networkAct, num_units=256,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
"""
networkAct = lasagne.layers.DenseLayer(
input,
num_units=128,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
networkAct = lasagne.layers.DenseLayer(
networkAct,
num_units=64,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
networkAct = lasagne.layers.DenseLayer(
networkAct,
num_units=32,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
self._actor = lasagne.layers.DenseLayer(
networkAct,
num_units=self._action_length,
nonlinearity=lasagne.nonlinearities.linear)
# self._b_o = init_b_weights((n_out,))
# print "Initial W " + str(self._w_o.get_value())
self._states_shared = theano.shared(
np.zeros((self._batch_size, self._state_length),
dtype=theano.config.floatX))
self._next_states_shared = theano.shared(
np.zeros((self._batch_size, self._state_length),
dtype=theano.config.floatX))
self._rewards_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self._actions_shared = theano.shared(
np.zeros((self._batch_size, self._action_length),
dtype=theano.config.floatX), )
def __init__(self, n_in, n_out):
batch_size = 32
state_length = n_in
action_length = n_out
# data types for model
State = T.dmatrix("State")
State.tag.test_value = np.random.rand(batch_size, state_length)
ResultState = T.dmatrix("ResultState")
ResultState.tag.test_value = np.random.rand(batch_size, state_length)
Reward = T.col("Reward")
Reward.tag.test_value = np.random.rand(batch_size, 1)
Action = T.dmatrix("Action")
Action.tag.test_value = np.random.rand(batch_size, action_length)
# create a small convolutional neural network
inputLayerA = lasagne.layers.InputLayer((None, state_length), State)
l_hid1A = lasagne.layers.DenseLayer(
inputLayerA,
num_units=128,
nonlinearity=lasagne.nonlinearities.rectify)
l_hid2A = lasagne.layers.DenseLayer(
l_hid1A, num_units=64, nonlinearity=lasagne.nonlinearities.rectify)
l_hid3A = lasagne.layers.DenseLayer(
l_hid2A, num_units=32, nonlinearity=lasagne.nonlinearities.rectify)
self._l_outA = lasagne.layers.DenseLayer(
l_hid3A, num_units=1, nonlinearity=lasagne.nonlinearities.linear)
# self._b_o = init_b_weights((n_out,))
inputLayerActA = lasagne.layers.InputLayer((None, state_length), State)
l_hid1ActA = lasagne.layers.DenseLayer(
inputLayerActA,
num_units=128,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
l_hid2ActA = lasagne.layers.DenseLayer(
l_hid1ActA,
num_units=64,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
l_hid3ActA = lasagne.layers.DenseLayer(
l_hid2ActA,
num_units=32,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
self._l_outActA = lasagne.layers.DenseLayer(
l_hid3ActA,
num_units=action_length,
nonlinearity=lasagne.nonlinearities.linear)
# self._b_o = init_b_weights((n_out,))
# self.updateTargetModel()
inputLayerB = lasagne.layers.InputLayer((None, state_length), State)
l_hid1B = lasagne.layers.DenseLayer(
inputLayerB,
num_units=128,
nonlinearity=lasagne.nonlinearities.rectify)
l_hid2B = lasagne.layers.DenseLayer(
l_hid1B, num_units=64, nonlinearity=lasagne.nonlinearities.rectify)
l_hid3B = lasagne.layers.DenseLayer(
l_hid2B, num_units=32, nonlinearity=lasagne.nonlinearities.rectify)
self._l_outB = lasagne.layers.DenseLayer(
l_hid3B, num_units=1, nonlinearity=lasagne.nonlinearities.linear)
inputLayerActB = lasagne.layers.InputLayer((None, state_length), State)
l_hid1ActB = lasagne.layers.DenseLayer(
inputLayerActB,
num_units=128,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
l_hid2ActB = lasagne.layers.DenseLayer(
l_hid1ActB,
num_units=64,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
l_hid3ActB = lasagne.layers.DenseLayer(
l_hid2ActB,
num_units=32,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
self._l_outActB = lasagne.layers.DenseLayer(
l_hid3ActB,
num_units=n_out,
nonlinearity=lasagne.nonlinearities.linear)
# print ("Initial W " + str(self._w_o.get_value()) )
self._learning_rate = 0.001
self._discount_factor = 0.8
self._rho = 0.95
self._rms_epsilon = 0.001
self._weight_update_steps = 5000
self._updates = 0
self._states_shared = theano.shared(
np.zeros((batch_size, state_length), dtype=theano.config.floatX))
self._next_states_shared = theano.shared(
np.zeros((batch_size, state_length), dtype=theano.config.floatX))
self._rewards_shared = theano.shared(np.zeros(
(batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self._actions_shared = theano.shared(
np.zeros((batch_size, n_out), dtype=theano.config.floatX), )
self._q_valsA = lasagne.layers.get_output(self._l_outA, State)
self._q_valsB = lasagne.layers.get_output(self._l_outB, ResultState)
self._q_valsActA = lasagne.layers.get_output(self._l_outActA, State)
self._q_valsActB = lasagne.layers.get_output(self._l_outActB, State)
self._q_func = self._q_valsA
self._q_funcAct = self._q_valsActA
# self._q_funcAct = theano.function(inputs=[State], outputs=self._q_valsActA, allow_input_downcast=True)
target = (Reward + self._discount_factor * self._q_valsB)
diff = target - self._q_valsA
loss = 0.5 * diff**2 + (
1e-6 * lasagne.regularization.regularize_network_params(
self._l_outA, lasagne.regularization.l2))
loss = T.mean(loss)
params = lasagne.layers.helper.get_all_params(self._l_outA)
actionParams = lasagne.layers.helper.get_all_params(self._l_outActA)
givens_ = {
State: self._states_shared,
ResultState: self._next_states_shared,
Reward: self._rewards_shared,
# Action: self._actions_shared,
}
actGivens = {
State: self._states_shared,
# ResultState: self._next_states_shared,
# Reward: self._rewards_shared,
Action: self._actions_shared,
}
# SGD update
#updates_ = lasagne.updates.rmsprop(loss, params, self._learning_rate, self._rho,
# self._rms_epsilon)
# TD update
updates_ = lasagne.updates.rmsprop(
T.mean(self._q_func) +
(1e-6 * lasagne.regularization.regularize_network_params(
self._l_outA, lasagne.regularization.l2)), params,
self._learning_rate * -T.mean(diff), self._rho, self._rms_epsilon)
# actDiff1 = (Action - self._q_valsActB) #TODO is this correct?
# actDiff = (actDiff1 - (Action - self._q_valsActA))
actDiff = ((Action - self._q_valsActA)
) # Target network does not work well here?
actLoss = 0.5 * actDiff**2 + (
1e-4 * lasagne.regularization.regularize_network_params(
self._l_outActA, lasagne.regularization.l2))
actLoss = T.sum(actLoss) / float(batch_size)
# actionUpdates = lasagne.updates.rmsprop(actLoss +
# (1e-4 * lasagne.regularization.regularize_network_params(
# self._l_outActA, lasagne.regularization.l2)), actionParams,
# self._learning_rate * 0.01 * (-actLoss), self._rho, self._rms_epsilon)
actionUpdates = lasagne.updates.rmsprop(
T.mean(self._q_funcAct) +
(1e-4 * lasagne.regularization.regularize_network_params(
self._l_outActA, lasagne.regularization.l2)), actionParams,
self._learning_rate * 0.5 * (-T.sum(actDiff) / float(batch_size)),
self._rho, self._rms_epsilon)
self._train = theano.function([], [loss, self._q_valsA],
updates=updates_,
givens=givens_)
self._trainActor = theano.function([], [actLoss, self._q_valsActA],
updates=actionUpdates,
givens=actGivens)
self._q_val = theano.function([],
self._q_valsA,
givens={State: self._states_shared})
self._q_action = theano.function([],
self._q_valsActA,
givens={State: self._states_shared})
self._bellman_error = theano.function(
inputs=[State, Reward, ResultState],
outputs=diff,
allow_input_downcast=True)
def __init__(self,
num_actions,
phi_length,
width,
height,
discount=.9,
learning_rate=.01,
batch_size=32,
approximator='none'):
self._batch_size = batch_size
self._num_input_features = phi_length
self._phi_length = phi_length
self._img_width = width
self._img_height = height
self._discount = discount
self.num_actions = num_actions
self.learning_rate = learning_rate
self.scale_input_by = 255.0
print "neural net initialization, lr is: ", self.learning_rate, approximator
# CONSTRUCT THE LAYERS
self.q_layers = []
self.q_layers.append(
layers.Input2DLayer(self._batch_size, self._num_input_features,
self._img_height, self._img_width,
self.scale_input_by))
if approximator == 'cuda_conv':
self.q_layers.append(
cc_layers.ShuffleBC01ToC01BLayer(self.q_layers[-1]))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_size=8,
stride=4,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_size=4,
stride=2,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.ShuffleC01BToBC01Layer(self.q_layers[-1]))
elif approximator == 'conv':
self.q_layers.append(
layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_width=8,
filter_height=8,
stride_x=4,
stride_y=4,
weights_std=.01,
init_bias_value=0.01))
self.q_layers.append(
layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_width=4,
filter_height=4,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01))
if approximator == 'cuda_conv' or approximator == 'conv':
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=256,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.rectify))
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.identity))
if approximator == 'none':
self.q_layers.append(\
layers.DenseLayerNoBias(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.00,
dropout=0,
nonlinearity=layers.identity))
self.q_layers.append(layers.OutputLayer(self.q_layers[-1]))
for i in range(len(self.q_layers) - 1):
print self.q_layers[i].get_output_shape()
# Now create a network (using the same weights)
# for next state q values
self.next_layers = copy_layers(self.q_layers)
self.next_layers[0] = layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_width,
self._img_height,
self.scale_input_by)
self.next_layers[1].input_layer = self.next_layers[0]
self.rewards = T.col()
self.actions = T.icol()
# Build the loss function ...
print "building loss funtion"
q_vals = self.q_layers[-1].predictions()
next_q_vals = self.next_layers[-1].predictions()
next_maxes = T.max(next_q_vals, axis=1, keepdims=True)
target = self.rewards + discount * next_maxes
target = theano.gradient.consider_constant(target)
diff = target - q_vals
# Zero out all entries for actions that were not chosen...
mask = build_mask(T.zeros_like(diff), self.actions, 1.0)
diff_masked = diff * mask
error = T.mean(diff_masked**2)
self._loss = error * diff_masked.shape[1] #
self._parameters = layers.all_parameters(self.q_layers[-1])
self._idx = T.lscalar('idx')
# CREATE VARIABLES FOR INPUT AND OUTPUT
self.states_shared = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.states_shared_next = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.rewards_shared = theano.shared(np.zeros(
(1, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(np.zeros((1, 1), dtype='int32'),
broadcastable=(False, True))
self._givens = \
{self.q_layers[0].input_var:
self.states_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.next_layers[0].input_var:
self.states_shared_next[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.rewards:
self.rewards_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :],
self.actions:
self.actions_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :]
}
self._updates = layers.gen_updates_rmsprop_and_nesterov_momentum(\
self._loss, self._parameters, learning_rate=self.learning_rate,
rho=0.9, momentum=0.9, epsilon=1e-6)
self._train = theano.function([self._idx],
self._loss,
givens=self._givens,
updates=self._updates)
self._compute_loss = theano.function([self._idx],
self._loss,
givens=self._givens)
self._compute_q_vals = \
theano.function([self.q_layers[0].input_var],
self.q_layers[-1].predictions(),
on_unused_input='ignore')
def __init__(self, num_actions):
# remember parameters
self.num_actions = num_actions
self.batch_size = BATCH_SIZE
self.discount_rate = DISCOUNT_RATE
self.history_length = HISTORY_LENGTH
self.screen_dim = DIMS
self.img_height = SCREEN_HEIGHT
self.img_width = SCREEN_WIDTH
self.clip_error = CLIP_ERROR
self.input_color_scale = COLOR_SCALE
self.target_steps = TARGET_STEPS
self.train_iterations = TRAIN_STEPS
self.train_counter = 0
self.momentum = MOMENTUM
self.update_rule = UPDATE_RULE
self.learning_rate = LEARNING_RATE
self.rms_decay = RMS_DECAY
self.rms_epsilon = RMS_EPSILON
self.rng = np.random.RandomState(RANDOM_SEED)
# set seed
lasagne.random.set_rng(self.rng)
# prepare tensors once and reuse them
states = T.tensor4('states')
next_states = T.tensor4('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
# terminals are bool for our case
terminals = T.bcol('terminals')
# create shared theano variables
self.states_shared = theano.shared(
np.zeros((self.batch_size, self.history_length, self.img_height, self.img_width),
dtype=theano.config.floatX))
self.next_states_shared = theano.shared(
np.zeros((self.batch_size, self.history_length, self.img_height, self.img_width),
dtype=theano.config.floatX))
# !broadcast ?
self.rewards_shared = theano.shared(
np.zeros((self.batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((self.batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(
#np.zeros((self.batch_size, 1), dtype='int32'),
np.zeros((self.batch_size, 1), dtype='int8'),
broadcastable=(False, True))
# can add multiple nets here
self.l_primary = self.build_network()
if self.target_steps > 0:
self.l_secondary = self.build_network()
self.copy_to_secondary()
"""
# input scale i.e. division can be applied to input directly also to normalize
"""
# define output symbols
q_vals = lasagne.layers.get_output(self.l_primary, states / self.input_color_scale)
if self.target_steps > 0:
q_vals_secondary = lasagne.layers.get_output(self.l_secondary, next_states / self.input_color_scale)
else:
# why this ?
q_vals_secondary = lasagne.layers.get_output(self.l_primary, next_states / self.input_color_scale)
q_vals_secondary = theano.gradient.disconnected_grad(q_vals_secondary)
# target = r + max
target = (rewards + (T.ones_like(terminals) - terminals) * self.discount_rate * T.max(q_vals_secondary, axis=1, keepdims=True))
"""
# check what this does
"""
diff = target - q_vals[T.arange(self.batch_size),
actions.reshape((-1,))].reshape((-1, 1))
# print shape ?
if self.clip_error > 0:
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_error)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + self.clip_error * linear_part
else:
loss = 0.5 * diff ** 2
loss = T.sum(loss)
params = lasagne.layers.helper.get_all_params(self.l_primary)
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
g_time = time.time()
logger.info("graph compiling")
if self.update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, params, self.learning_rate, self.rms_decay,
self.rms_epsilon)
elif self.update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, self.learning_rate, self.rms_decay,
self.rms_epsilon)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([], [loss, q_vals], updates=updates,
givens=givens)
self._q_vals = theano.function([], q_vals,
givens={states: self.states_shared})
logger.info("Theano Graph Compiled !! %f", time.time() - g_time)
def __init__(self, num_actions, phi_length, width, height,
discount, learning_rate, decay, momentum=0,
batch_size=32,
approximator='none'):
self._batch_size = batch_size
self._num_input_features = phi_length
self._phi_length = phi_length
self._img_width = width
self._img_height = height
self._discount = discount
self.num_actions = num_actions
self.learning_rate = learning_rate
self.decay = decay
self.momentum = momentum
self.scale_input_by = 255.0
# CONSTRUCT THE LAYERS
self.q_layers = []
self.q_layers.append(layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_height,
self._img_width,
self.scale_input_by))
if approximator == 'cuda_conv':
self.q_layers.append(cc_layers.ShuffleBC01ToC01BLayer(
self.q_layers[-1]))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_size=8,
stride=4,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(
cc_layers.CudaConvnetConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_size=4,
stride=2,
weights_std=.01,
init_bias_value=0.1))
self.q_layers.append(cc_layers.ShuffleC01BToBC01Layer(
self.q_layers[-1]))
elif approximator == 'conv':
self.q_layers.append(layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=16,
filter_width=8,
filter_height=8,
stride_x=4,
stride_y=4,
weights_std=.01,
init_bias_value=0.01))
self.q_layers.append(layers.StridedConv2DLayer(self.q_layers[-1],
n_filters=32,
filter_width=4,
filter_height=4,
stride_x=2,
stride_y=2,
weights_std=.01,
init_bias_value=0.01))
if approximator == 'cuda_conv' or approximator == 'conv':
self.q_layers.append(layers.DenseLayer(self.q_layers[-1],
n_outputs=256,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.rectify))
self.q_layers.append(
layers.DenseLayer(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.01,
init_bias_value=0.1,
dropout=0,
nonlinearity=layers.identity))
if approximator == 'none':
self.q_layers.append(\
layers.DenseLayerNoBias(self.q_layers[-1],
n_outputs=num_actions,
weights_std=0.00,
dropout=0,
nonlinearity=layers.identity))
self.q_layers.append(layers.OutputLayer(self.q_layers[-1]))
for i in range(len(self.q_layers)-1):
print self.q_layers[i].get_output_shape()
# Now create a network (using the same weights)
# for next state q values
self.next_layers = copy_layers(self.q_layers)
self.next_layers[0] = layers.Input2DLayer(self._batch_size,
self._num_input_features,
self._img_width,
self._img_height,
self.scale_input_by)
self.next_layers[1].input_layer = self.next_layers[0]
self.rewards = T.col()
self.actions = T.icol()
# Build the loss function ...
q_vals = self.q_layers[-1].predictions()
next_q_vals = self.next_layers[-1].predictions()
next_maxes = T.max(next_q_vals, axis=1, keepdims=True)
target = self.rewards + discount * next_maxes
target = theano.gradient.consider_constant(target)
diff = target - q_vals
# Zero out all entries for actions that were not chosen...
mask = build_mask(T.zeros_like(diff), self.actions, 1.0)
diff_masked = diff * mask
error = T.mean(diff_masked ** 2)
self._loss = error * diff_masked.shape[1] #
self._parameters = layers.all_parameters(self.q_layers[-1])
self._idx = T.lscalar('idx')
# CREATE VARIABLES FOR INPUT AND OUTPUT
self.states_shared = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.states_shared_next = theano.shared(
np.zeros((1, 1, 1, 1), dtype=theano.config.floatX))
self.rewards_shared = theano.shared(
np.zeros((1, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((1, 1), dtype='int32'), broadcastable=(False, True))
self._givens = \
{self.q_layers[0].input_var:
self.states_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.next_layers[0].input_var:
self.states_shared_next[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :, :, :],
self.rewards:
self.rewards_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :],
self.actions:
self.actions_shared[self._idx*self._batch_size:
(self._idx+1)*self._batch_size, :]
}
if self.momentum != 0:
self._updates = layers.gen_updates_rmsprop_and_nesterov_momentum(\
self._loss, self._parameters, learning_rate=self.learning_rate,
rho=self.decay, momentum=self.momentum, epsilon=1e-6)
else:
self._updates = layers.gen_updates_rmsprop(self._loss,
self._parameters, learning_rate=self.learning_rate,
rho=self.decay, epsilon=1e-6)
self._train = theano.function([self._idx], self._loss,
givens=self._givens,
updates=self._updates)
self._compute_loss = theano.function([self._idx],
self._loss,
givens=self._givens)
self._compute_q_vals = \
theano.function([self.q_layers[0].input_var],
self.q_layers[-1].predictions(),
on_unused_input='ignore')
def __init__(self,
batchSize,
numFrames,
inputHeight,
inputWidth,
numActions,
discountRate,
learningRate,
rho,
rms_epsilon,
momentum,
networkUpdateDelay,
useSARSAUpdate,
kReturnLength,
networkType="conv",
updateRule="deepmind_rmsprop",
batchAccumulator="sum",
clipDelta=1.0,
inputScale=255.0):
self.batchSize = batchSize
self.numFrames = numFrames
self.inputWidth = inputWidth
self.inputHeight = inputHeight
self.inputScale = inputScale
self.numActions = numActions
self.discountRate = discountRate
self.learningRate = learningRate
self.rho = rho
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.networkUpdateDelay = networkUpdateDelay
self.useSARSAUpdate = useSARSAUpdate
self.kReturnLength = kReturnLength
self.networkType = networkType
self.updateRule = updateRule
self.batchAccumulator = batchAccumulator
self.clipDelta = clipDelta
self.updateCounter = 0
states = T.tensor4("states")
nextStates = T.tensor4("nextStates")
rewards = T.col("rewards")
actions = T.icol("actions")
nextActions = T.icol("nextActions")
terminals = T.icol("terminals")
self.statesShared = theano.shared(
np.zeros((self.batchSize, self.numFrames, self.inputHeight,
self.inputWidth),
dtype=theano.config.floatX))
self.nextStatesShared = theano.shared(
np.zeros((self.batchSize, self.numFrames, self.inputHeight,
self.inputWidth),
dtype=theano.config.floatX))
self.rewardsShared = theano.shared(np.zeros(
(self.batchSize, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actionsShared = theano.shared(np.zeros((self.batchSize, 1),
dtype='int32'),
broadcastable=(False, True))
self.nextActionsShared = theano.shared(np.zeros((self.batchSize, 1),
dtype='int32'),
broadcastable=(False, True))
self.terminalsShared = theano.shared(np.zeros((self.batchSize, 1),
dtype='int32'),
broadcastable=(False, True))
self.qValueNetwork = DeepNetworks.buildDeepQNetwork(
self.batchSize, self.numFrames, self.inputHeight, self.inputWidth,
self.numActions, self.networkType)
qValues = lasagne.layers.get_output(self.qValueNetwork,
states / self.inputScale)
if self.networkUpdateDelay > 0:
self.nextQValueNetwork = DeepNetworks.buildDeepQNetwork(
self.batchSize, self.numFrames, self.inputHeight,
self.inputWidth, self.numActions, self.networkType)
self.resetNextQValueNetwork()
nextQValues = lasagne.layers.get_output(
self.nextQValueNetwork, nextStates / self.inputScale)
else:
nextQValues = lasagne.layers.get_output(
self.qValueNetwork, nextStates / self.inputScale)
nextQValues = theano.gradient.disconnected_grad(nextQValues)
if self.useSARSAUpdate:
target = rewards + terminals * (
self.discountRate**
self.kReturnLength) * nextQValues[T.arange(self.batchSize),
nextActions.reshape(
(-1, ))].reshape((-1, 1))
else:
target = rewards + terminals * (
self.discountRate**self.kReturnLength) * T.max(
nextQValues, axis=1, keepdims=True)
targetDifference = target - qValues[T.arange(self.batchSize),
actions.reshape((-1, ))].reshape(
(-1, 1))
quadraticPart = T.minimum(abs(targetDifference), self.clipDelta)
linearPart = abs(targetDifference) - quadraticPart
# if self.clipDelta > 0:
# targetDifference = targetDifference.clip(-1.0 * self.clipDelta, self.clipDelta)
if self.batchAccumulator == "sum":
# loss = T.sum(targetDifference ** 2)
loss = T.sum(0.5 * quadraticPart**2 + self.clipDelta * linearPart)
elif self.batchAccumulator == "mean":
# loss = T.mean(targetDifference ** 2)
loss = T.mean(0.5 * quadraticPart**2 + self.clipDelta * linearPart)
else:
raise ValueError("Bad Network Accumulator. {sum, mean} expected")
networkParameters = lasagne.layers.helper.get_all_params(
self.qValueNetwork)
if self.updateRule == "deepmind_rmsprop":
updates = DeepNetworks.deepmind_rmsprop(loss, networkParameters,
self.learningRate,
self.rho, self.rms_epsilon)
elif self.updateRule == "rmsprop":
updates = lasagne.updates.rmsprop(loss, networkParameters,
self.learningRate, self.rho,
self.rms_epsilon)
elif self.updateRule == "sgd":
updates = lasagne.updates.sgd(loss, networkParameters,
self.learningRate)
else:
raise ValueError(
"Bad update rule. {deepmind_rmsprop, rmsprop, sgd} expected")
if self.momentum > 0:
updates.lasagne.updates.apply_momentum(updates, None,
self.momentum)
lossGivens = {
states: self.statesShared,
nextStates: self.nextStatesShared,
rewards: self.rewardsShared,
actions: self.actionsShared,
nextActions: self.nextActionsShared,
terminals: self.terminalsShared
}
self.__trainNetwork = theano.function([], [loss, qValues],
updates=updates,
givens=lossGivens,
on_unused_input='warn')
self.__computeQValues = theano.function(
[], qValues, givens={states: self.statesShared})
def initialize_network(self):
"""
:description: this method initializes the network, updates, and theano functions for training and
retrieving q values. Here's an outline:
1. build the q network and target q network
2. initialize theano symbolic variables used for compiling functions
3. initialize the theano numeric variables used as input to functions
4. formulate the symbolic loss
5. formulate the symbolic updates
6. compile theano functions for training and for getting q_values
"""
batch_size, input_shape = self.batch_size, self.input_shape
lasagne.random.set_rng(self.rng)
# 1. build the q network and target q network
self.l_out = self.build_network(input_shape, self.num_actions, batch_size)
self.next_l_out = self.build_network(input_shape, self.num_actions, batch_size)
self.reset_target_network()
# 2. initialize theano symbolic variables used for compiling functions
states = T.tensor4('states')
actions = T.icol('actions')
rewards = T.col('rewards')
next_states = T.tensor4('next_states')
# terminals are used to indicate a terminal state in the episode and hence a mask over the future
# q values i.e., Q(s',a')
terminals = T.icol('terminals')
# 3. initialize the theano numeric variables used as input to functions
self.states_shape = (batch_size,) + (1,) + input_shape
self.states_shared = theano.shared(np.zeros(self.states_shape, dtype=theano.config.floatX))
self.next_states_shared = theano.shared(np.zeros(self.states_shape, dtype=theano.config.floatX))
self.rewards_shared = theano.shared(np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
# 4. formulate the symbolic loss
q_vals = lasagne.layers.get_output(self.l_out, states)
next_q_vals = lasagne.layers.get_output(self.next_l_out, next_states)
target = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
# reshape((-1,)) == 'make a row vector', reshape((-1, 1) == 'make a column vector'
diff = target - q_vals[T.arange(batch_size), actions.reshape((-1,))].reshape((-1, 1))
# a lot of the deepmind work clips the td error at 1 so we do that here
# the problem is that gradient backpropagating through this minimum node
# will be zero if diff is larger then 1.0 (because changing params before
# the minimum does not impact the output of the minimum). To account for
# this we take the part of the td error (magnitude) greater than 1.0 and simply
# add it to the loss, which allows gradient to backprop but just linearly
# in the td error rather than quadratically
quadratic_part = T.minimum(abs(diff), 1.0)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + linear_part
loss = T.mean(loss) + self.regularization * regularize_network_params(self.l_out, l2)
# 5. formulate the symbolic updates
params = lasagne.layers.helper.get_all_params(self.l_out)
updates = self.initialize_updates(self.update_rule, loss, params, self.learning_rate)
# 6. compile theano functions for training and for getting q_values
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
self._train = theano.function([], [loss, q_vals], updates=updates, givens=givens)
self._get_q_values = theano.function([], q_vals, givens={states: self.states_shared})
def __init__(self, model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_):
super(QPropKeras,
self).__init__(model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_)
## primary network
self._model = model
## Target network for DPG
self._modelTarget = copy.deepcopy(model)
## Target network for PPO
self._modelTarget2 = copy.deepcopy(model)
# self._modelTarget = model
self._learning_rate = self.getSettings()['learning_rate']
self._discount_factor = self.getSettings()['discount_factor']
self._rho = self.getSettings()['rho']
self._rms_epsilon = self.getSettings()['rms_epsilon']
self._q_valsActA = self._model.getActorNetwork()(
self._model._stateInput)[:, :self._action_length]
self._q_valsActASTD = self._model.getActorNetwork()(
self._model._stateInput)[:, self._action_length:]
self._q_valsActTarget_State = self._modelTarget2.getActorNetwork()(
self._model._stateInput)[:, :self._action_length]
# self._q_valsActTarget_State = self._modelTarget.getActorNetwork()(self._model._stateInput)[:,:self._action_length]
# self._q_valsActTargetSTD = self._modelTarget.getActorNetwork()(self._model._stateInput)[:,self._action_length:]
self._q_valsActASTD = (T.ones_like(
self._q_valsActA)) * self.getSettings()['exploration_rate']
self._q_valsActTargetSTD = (T.ones_like(self._q_valsActTarget_State)
) * self.getSettings()['exploration_rate']
self._Advantage = T.col("Advantage")
self._Advantage.tag.test_value = np.zeros(
(self._batch_size, 1),
dtype=np.dtype(self.getSettings()['float_type']))
self._advantage_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=self.getSettings()['float_type']),
broadcastable=(False, True))
self._LEARNING_PHASE = T.scalar(
dtype='uint8', name='keras_learning_phase') # 0 = test, 1 = train
self._QProp_N = T.col("QProp_N")
self._QProp_N.tag.test_value = np.zeros(
(self._batch_size, 1),
dtype=np.dtype(self.getSettings()['float_type']))
self._QProp_N_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=self.getSettings()['float_type']),
broadcastable=(False, True))
self._q_function = self._model.getCriticNetwork()(
[self._model._stateInput, self._q_valsActA])
self._q_function_Target = self._model.getCriticNetwork()(
[self._model._stateInput, self._model._actionInput])
# self._value = self._model.getCriticNetwork()([self._model._stateInput, K.learning_phase()])
self._value_Target = self._modelTarget2.getValueFunction()(
[self._model._stateInput])
self._value = self._model.getValueFunction()([self._model._stateInput])
# self._value = self._model.getCriticNetwork()([self._model._stateInput])
self._actor_entropy = 0.5 * T.mean((2 * np.pi * self._q_valsActASTD))
## Compute on-policy policy gradient
self._prob = likelihood(self._model._actionInput, self._q_valsActA,
self._q_valsActASTD, self._action_length)
### How should this work if the target network is very odd, as in not a slightly outdated copy.
self._prob_target = likelihood(self._model._actionInput,
self._q_valsActTarget_State,
self._q_valsActTargetSTD,
self._action_length)
## This does the sum already
self._r = (self._prob / self._prob_target)
self._actLoss_ = theano.tensor.elemwise.Elemwise(theano.scalar.mul)(
(self._r), self._Advantage)
ppo_epsilon = self.getSettings()['kl_divergence_threshold']
self._actLoss_2 = theano.tensor.elemwise.Elemwise(theano.scalar.mul)(
(theano.tensor.clip(self._r, 1.0 - ppo_epsilon,
1 + ppo_epsilon), self._Advantage))
self._actLoss_ = theano.tensor.minimum((self._actLoss_),
(self._actLoss_2))
# self._actLoss = ((T.mean(self._actLoss_) )) + -self._actor_regularization
# self._actLoss = (-1.0 * (T.mean(self._actLoss_) + (self.getSettings()['std_entropy_weight'] * self._actor_entropy )))
self._actLoss = -1.0 * (T.mean(self._actLoss_) +
T.mean(self._QProp_N * self._q_function))
self._actLoss_PPO = -1.0 * (T.mean(self._actLoss_))
# self._policy_grad = T.grad(self._actLoss , self._actionParams)
QPropKeras.compile(self)
def __init__(self, mean_doc_size, input, input_sums,
n_visible=784, n_hidden=500, W = None, hbias = None, vbias = None,
numpy_rng = None, theano_rng = None):
"""
RBM constructor. Defines the parameters of the model along with
basic operations for inferring hidden from visible (and vice-versa),
as well as for performing CD updates.
:param input: None for standalone RBMs or symbolic variable if RBM is
part of a larger graph.
:param n_visible: number of visible units
:param n_hidden: number of hidden units
:param W: None for standalone RBMs or symbolic variable pointing to a
shared weight matrix in case RBM is part of a DBN network in a DBN,
the weights are shared between RBMs and layers of a MLP
:param hbias: None for standalone RBMs or symbolic variable pointing
to a shared hidden units bias vector in case RBM is part of a
different network
:param vbias: None for standalone RBMs or a symbolic variable
pointing to a shared visible units bias
"""
self.mean_doc_size = mean_doc_size
self.n_visible = n_visible
self.n_hidden = n_hidden
if numpy_rng is None:
# create a number generator
numpy_rng = numpy.random.RandomState(1234)
if theano_rng is None :
theano_rng = RandomStreams(numpy_rng.randint(2**30))
if W is None :
# W is initialized with `initial_W` which is uniformely sampled
# from -4*sqrt(6./(n_visible+n_hidden)) and 4*sqrt(6./(n_hidden+n_visible))
# the output of uniform if converted using asarray to dtype
# theano.config.floatX so that the code is runable on GPU
initial_W = numpy.asarray( numpy_rng.uniform(
low = -4.*numpy.sqrt(6./(n_hidden+n_visible)),
high = 4.*numpy.sqrt(6./(n_hidden+n_visible)),
size = (n_visible, n_hidden)),
dtype = theano.config.floatX)
initial_W *= 1/self.mean_doc_size
# theano shared variables for weights and biases
W = theano.shared(value = initial_W, name = 'W')
if hbias is None :
# create shared variable for hidden units bias
hbias = theano.shared(value = numpy.zeros(n_hidden,
dtype = theano.config.floatX), name='hbias')
if vbias is None :
# create shared variable for visible units bias
vbias = theano.shared(value =numpy.zeros(n_visible,
dtype = theano.config.floatX),name='vbias')
# initialize input layer for standalone RBM or layer0 of DBN
self.input = input
self.input_sums = input_sums
if not input:
self.input = T.matrix('input')
self.input_sums = T.col('input_sums')
self.binomial_approx_val = theano.shared(value = float(100000), name = 'binomial_approx_val')
self.W = W
self.hbias = hbias
self.vbias = vbias
self.theano_rng = theano_rng
# **** WARNING: It is not a good idea to put things in this list
# other than shared variables created in this function.
self.params = [self.W, self.hbias, self.vbias]
def col(name):
return T.col(name)
from __future__ import print_function __author__ = 'frankhe' import theano.tensor as T from theano.compile import function import numpy as np num_actions = 3 batch_size = None q_s = T.matrix() a = T.col() # batch_size = q_s.shape[0] # out = q_s[range(batch_size), a.reshape(batch_size)] mask = T.eq(T.arange(num_actions).reshape((1, -1)), a.reshape((-1, 1))) out_t = q_s * mask out = T.sum(out_t, axis=1, keepdims=True) f = function([q_s, a], out) q_s_ = np.random.rand(5, num_actions) a_ = np.array([1, 0, 2, 1, 2]).reshape((5, 1)) print(f(q_s_, a_))
def __init__(self, state_length, action_length, state_bounds, action_bounds, settings_):
super(ForwardDynamicsDenseNetworkDropoutTesting,self).__init__(state_length, action_length, state_bounds, action_bounds, 0, settings_)
batch_size=32
# data types for model
self._State = T.matrix("State")
self._State.tag.test_value = np.random.rand(batch_size,self._state_length)
self._ResultState = T.matrix("ResultState")
self._ResultState.tag.test_value = np.random.rand(batch_size,self._state_length)
self._Reward = T.col("Reward")
self._Reward.tag.test_value = np.random.rand(self._batch_size,1)
self._Action = T.matrix("Action")
self._Action.tag.test_value = np.random.rand(batch_size, self._action_length)
# create a small convolutional neural network
input = lasagne.layers.InputLayer((None, self._state_length), self._State)
self._stateInputVar = input.input_var
actionInput = lasagne.layers.InputLayer((None, self._action_length), self._Action)
self._actionInputVar = actionInput.input_var
insert_action_later = True
double_insert_action = False
add_layers_after_action = False
if (not insert_action_later or (double_insert_action)):
input = lasagne.layers.ConcatLayer([input, actionInput])
## Activation types
# activation_type = elu_mine
# activation_type=lasagne.nonlinearities.tanh
activation_type=lasagne.nonlinearities.leaky_rectify
# activation_type=lasagne.nonlinearities.rectify
# network = lasagne.layers.DropoutLayer(input, p=self._dropout_p, rescale=True)
"""
network = lasagne.layers.DenseLayer(
input, num_units=128,
nonlinearity=activation_type)
network = weight_norm(network)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
layersAct = [network]
network = lasagne.layers.DenseLayer(
network, num_units=64,
nonlinearity=activation_type)
network = weight_norm(network)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
layersAct.append(network)
network = lasagne.layers.ConcatLayer([layersAct[1], layersAct[0]])
network = lasagne.layers.DenseLayer(
network, num_units=32,
nonlinearity=activation_type)
network = weight_norm(network)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
layersAct.append(network)
network = lasagne.layers.ConcatLayer([layersAct[2], layersAct[1], layersAct[0]])
## This can be used to model the reward function
self._reward_net = lasagne.layers.DenseLayer(
network, num_units=1,
nonlinearity=lasagne.nonlinearities.linear)
# print ("Initial W " + str(self._w_o.get_value()) )
"""
network = lasagne.layers.DenseLayer(
input, num_units=128,
nonlinearity=activation_type)
# network = weight_norm(network)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
# layersAct = [network]
if ( insert_action_later ):
### Lets try adding the action input later on in the network
if ( add_layers_after_action ):
networkA = lasagne.layers.DenseLayer(
actionInput, num_units=32,
nonlinearity=activation_type)
network = lasagne.layers.ConcatLayer([network, networkA])
else:
network = lasagne.layers.ConcatLayer([network, actionInput])
network = lasagne.layers.DenseLayer(
network, num_units=64,
nonlinearity=activation_type)
# network = weight_norm(network)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
# layersAct.append(network)
# network = lasagne.layers.ConcatLayer([layersAct[1], layersAct[0]])
network = lasagne.layers.DenseLayer(
network, num_units=32,
nonlinearity=activation_type)
# network = weight_norm(network)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
# layersAct.append(network)
# network = lasagne.layers.ConcatLayer([layersAct[2], layersAct[1], layersAct[0]])
# network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
network = lasagne.layers.DenseLayer(
network, num_units=8,
nonlinearity=activation_type)
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
"""
network = lasagne.layers.DenseLayer(
network, num_units=8,
nonlinearity=activation_type)
"""
## This can be used to model the reward function
self._reward_net = lasagne.layers.DenseLayer(
network, num_units=1,
nonlinearity=lasagne.nonlinearities.linear)
# print ("Initial W " + str(self._w_o.get_value()) )
# networkAct = lasagne.layers.DropoutLayer(input, p=self._dropout_p, rescale=True)
networkAct = lasagne.layers.DenseLayer(
input, num_units=256,
nonlinearity=activation_type)
networkAct = weight_norm(networkAct)
networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
layersAct = [networkAct]
networkAct = lasagne.layers.DenseLayer(
networkAct, num_units=128,
nonlinearity=activation_type)
networkAct = weight_norm(networkAct)
networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
if ( insert_action_later ):
### Lets try adding the action input later on in the network
if ( add_layers_after_action ):
networkActA = lasagne.layers.DenseLayer(
actionInput, num_units=64,
nonlinearity=activation_type)
networkAct = lasagne.layers.ConcatLayer([networkAct, networkActA])
else:
networkAct = lasagne.layers.ConcatLayer([networkAct, actionInput])
layersAct.append(networkAct)
networkAct = lasagne.layers.ConcatLayer([layersAct[1], layersAct[0]])
networkAct = lasagne.layers.DenseLayer(
networkAct, num_units=128,
nonlinearity=activation_type)
networkAct = weight_norm(networkAct)
networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
layersAct.append(networkAct)
networkAct = lasagne.layers.ConcatLayer([layersAct[2], layersAct[1], layersAct[0]])
self._forward_dynamics_net = lasagne.layers.DenseLayer(
networkAct, num_units=self._state_length,
nonlinearity=lasagne.nonlinearities.linear)
# print ("Initial W " + str(self._w_o.get_value()) )
if (('use_stochastic_forward_dynamics' in self._settings) and
self._settings['use_stochastic_forward_dynamics']):
with_std = lasagne.layers.DenseLayer(
networkAct, num_units=self._state_length,
nonlinearity=theano.tensor.nnet.softplus)
self._forward_dynamics_net = lasagne.layers.ConcatLayer([self._forward_dynamics_net, with_std], axis=1)
self._states_shared = theano.shared(
np.zeros((batch_size, self._state_length),
dtype=theano.config.floatX))
self._next_states_shared = theano.shared(
np.zeros((batch_size, self._state_length),
dtype=theano.config.floatX))
self._actions_shared = theano.shared(
np.zeros((batch_size, self._action_length), dtype=theano.config.floatX),
)
self._rewards_shared = theano.shared(
np.zeros((self._batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
def __init__(self, environment, rho, rms_epsilon, momentum, clip_delta, freeze_interval, batchSize, network_type,
update_rule, batch_accumulator, randomState, frame_scale=255.0):
""" Initialize environment
Arguments:
environment - the environment (class Env)
num_elements_in_batch - list of k integers for the number of each element kept as belief state
num_actions - int
discount - float
learning_rate - float
rho, rms_epsilon, momentum - float, float, float
...
network_type - string
...
"""
self._environment = environment
self._batchSize = batchSize
self._inputDimensions = self._environment.inputDimensions()
self._nActions = self._environment.nActions()
self._df = 0
self.rho = rho
self._lr = 0
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self._randomState = randomState
lasagne.random.set_rng(self._randomState)
self.update_counter = 0
states=[] # list of symbolic variables for each of the k element in the belief state
# --> [ T.tensor4 if observation of element=matrix, T.tensor3 if vector, T.tensor 2 if scalar ]
next_states=[] # idem than states at t+1
self.states_shared=[] # list of shared variable for each of the k element in the belief state
self.next_states_shared=[] # idem that self.states_shared at t+1
for i, dim in enumerate(self._inputDimensions):
if len(dim) == 3:
states.append(T.tensor4("%s_%s" % ("state", i)))
next_states.append(T.tensor4("%s_%s" % ("next_state", i)))
elif len(dim) == 2:
states.append(T.tensor3("%s_%s" % ("state", i)))
next_states.append(T.tensor3("%s_%s" % ("next_state", i)))
elif len(dim) == 1:
states.append( T.matrix("%s_%s" % ("state", i)) )
next_states.append( T.matrix("%s_%s" % ("next_state", i)) )
self.states_shared.append(theano.shared(np.zeros((batchSize,) + dim, dtype=theano.config.floatX) , borrow=False))
self.next_states_shared.append(theano.shared(np.zeros((batchSize,) + dim, dtype=theano.config.floatX) , borrow=False))
print("Number of observations per state: {}".format(len(self.states_shared)))
print("For each observation, historySize + ponctualObs_i.shape: {}".format(self._inputDimensions))
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
thediscount = T.scalar(name='thediscount', dtype=theano.config.floatX)
thelr = T.scalar(name='thelr', dtype=theano.config.floatX)
self.l_out, self.l_outs_conv, shape_after_conv = self._build(network_type, states)
print("Number of neurons after spatial and temporal convolution layers: {}".format(shape_after_conv))
self.next_l_out, self.next_l_outs_conv, shape_after_conv = self._build(network_type, next_states)
self._resetQHat()
self.rewards_shared = theano.shared(
np.zeros((batchSize, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((batchSize, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(
np.zeros((batchSize, 1), dtype='int32'),
broadcastable=(False, True))
q_vals = lasagne.layers.get_output(self.l_out)
next_q_vals = lasagne.layers.get_output(self.next_l_out)
max_next_q_vals=T.max(next_q_vals, axis=1, keepdims=True)
T_ones_like=T.ones_like(T.ones_like(terminals) - terminals)
target = rewards + T_ones_like * thediscount * max_next_q_vals
q_val=q_vals[T.arange(batchSize), actions.reshape((-1,))].reshape((-1, 1))
diff = target - q_val
if self.clip_delta > 0:
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_delta)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + self.clip_delta * linear_part
else:
loss = 0.5 * diff ** 2
if batch_accumulator == 'sum':
loss = T.sum(loss)
elif batch_accumulator == 'mean':
loss = T.mean(loss)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
params = lasagne.layers.helper.get_all_params(self.l_out)
for conv_param in self.l_outs_conv:
for p in lasagne.layers.helper.get_all_params(conv_param):
params.append(p)
givens = {
rewards: self.rewards_shared,
actions: self.actions_shared, ## actions not needed!
terminals: self.terminals_shared
}
for i, x in enumerate(self.states_shared):
givens[ states[i] ] = x
for i, x in enumerate(self.next_states_shared):
givens[ next_states[i] ] = x
if update_rule == 'deepmind_rmsprop':
grads = get_or_compute_grads(loss, params)
updates = deepmind_rmsprop(loss, params, grads, thelr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, thelr, self.rho,
self.rms_epsilon)
elif update_rule == 'sgd':
updates = lasagne.updates.sgd(loss, params, thelr)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([thediscount, thelr], [loss, q_vals], updates=updates,
givens=givens,
on_unused_input='warn')
givens2={}
for i, x in enumerate(self.states_shared):
givens2[ states[i] ] = x
self._q_vals = theano.function([], q_vals,
givens=givens2,
on_unused_input='warn')
def __init__(self, input_width, input_height, num_actions,
num_frames, discount, learning_rate, rho,
rms_epsilon, momentum, clip_delta, freeze_interval,
batch_size, network_type, update_rule,
batch_accumulator, rng, input_scale=255.0):
self.input_width = input_width
self.input_height = input_height
self.num_actions = num_actions
self.num_frames = num_frames
self.batch_size = batch_size
self.discount = discount
self.rho = rho
self.lr = learning_rate
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self.rng = rng
# print "NETWORK---------------------------"
# print "input width ", self.input_width
# print "input height", self.input_height
# print "num actiuons", self.num_actions
# print "num frames", self.num_frames
# print "batch size", self.batch_size
# print "discount", self.discount
# print "rho", self.rho
# print "lr", self.lr
# print "rms_epsilon", self.rms_epsilon
# print "momentum", self.momentum
# print "clip_delta", self.clip_delta
# print "freeze_ intercal", self.freeze_interval
# print "rng", self.rng
lasagne.random.set_rng(self.rng)
self.update_counter = 0
self.l_out = self.build_network(network_type, input_width, input_height,
num_actions, num_frames, batch_size)
if self.freeze_interval > 0:
self.next_l_out = self.build_network(network_type, input_width,
input_height, num_actions,
num_frames, batch_size)
self.reset_q_hat()
states = T.tensor4('states')
next_states = T.tensor4('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
# Shared variables for training from a minibatch of replayed state transitions,
# each consisting of num_frames + 1 (due to overlap) images, along with
# the chosen action and resulting reward and termnial status.
self.imgs_shared = theano.shared(
np.zeros((batch_size, num_frames + 1, input_height, input_width),
dtype=theano.config.floatX))
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
# Shared variable for a single state, to calculate q_vals
self.state_shared = theano.shared(
np.zeros((num_frames, input_height, input_width),
dtype=theano.config.floatX))
q_vals = lasagne.layers.get_output(self.l_out, states / input_scale)
if self.freeze_interval > 0:
next_q_vals = lasagne.layers.get_output(self.next_l_out,
next_states / input_scale)
else:
next_q_vals = lasagne.layers.get_output(self.l_out,
next_states / input_scale)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
terminalsX = terminals.astype(theano.config.floatX)
actionmask = T.eq(T.arange(num_actions).reshape((1, -1)),
actions.reshape((-1, 1))).astype(theano.config.floatX)
target = (rewards +
(T.ones_like(terminalsX) - terminalsX) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
output = (q_vals * actionmask).sum(axis=1).reshape((-1, 1))
diff = target - output
if self.clip_delta > 0:
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_delta)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + self.clip_delta * linear_part
else:
loss = 0.5 * diff ** 2
if batch_accumulator == 'sum':
loss = T.sum(loss)
elif batch_accumulator == 'mean':
loss = T.mean(loss)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
params = lasagne.layers.helper.get_all_params(self.l_out)
train_givens = {
states: self.imgs_shared[:, :-1],
next_states: self.imgs_shared[:, 1:],
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
if update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'sgd':
updates = lasagne.updates.sgd(loss, params, self.lr)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([], [loss], updates=updates,
givens=train_givens)
q_givens = {
states: self.state_shared.reshape((1,
self.num_frames,
self.input_height,
self.input_width))
}
self._q_vals = theano.function([], q_vals[0], givens=q_givens)
def __init__(self, input_width, input_height, num_channels, num_actions,
num_frames, discount, learning_rate, rho,
rms_epsilon, momentum, clip_delta, freeze_interval,
batch_size, network_type, update_rule,
batch_accumulator, rng, network_params, input_scale=255.0):
self.input_width = input_width
self.input_height = input_height
self.num_channels = num_channels
self.num_actions = num_actions
self.num_frames = num_frames
self.batch_size = batch_size
self.discount = discount
self.rho = rho
self.lr = learning_rate
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self.rng = rng
self.lstm = None
self.next_lstm = None
logging.debug('network parameters', network_params)
self.network_params = network_params
lasagne.random.set_rng(self.rng)
self.update_counter = 0
networks = self.build_network(network_type, num_channels, input_width, input_height,
num_actions, num_frames, None)
if isinstance(networks, tuple):
self.l_out = networks[0]
self.lstm = networks[1]
else:
self.l_out = networks
# theano.compile.function_dump('network.dump', self.l_out)
if self.freeze_interval > 0:
next_networks = self.build_network(network_type, num_channels, input_width,
input_height, num_actions,
num_frames, None)
if isinstance(next_networks, tuple):
self.next_l_out = next_networks[0]
self.next_lstm = next_networks[1]
else:
self.next_l_out = next_networks
self.reset_q_hat()
# This really really needs to be floats for now.
# It makes sense if they use it for computations
btensor5 = T.TensorType(theano.config.floatX, (False,) * 5)
states = btensor5('states')
next_states = btensor5('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
# Apparently needed for some layers with a variable input size
# Weird, because the others just allow a None batch size,
# but let's just play safe for now
# For now, it should always look exactly like states
# (n_batch, n_time_steps)
# mask = T.imatrix('mask')
self.states_shared = theano.shared(
np.zeros((batch_size, num_frames, num_channels, input_height, input_width),
dtype=theano.config.floatX), name='states')
self.next_states_shared = theano.shared(
np.zeros((batch_size, num_frames, num_channels, input_height, input_width),
dtype=theano.config.floatX), name='next_states')
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True), name='rewards')
self.actions_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True), name='actions')
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
# self.mask_shared = theano.shared(np.ones((batch_size, num_frames),
# dtype='int32'))
# lstmout = lasagne.layers.get_output(self.lstm, states / input_scale)
q_vals = lasagne.layers.get_output(self.l_out, states / input_scale)
# mask_input=mask)
if self.freeze_interval > 0:
next_q_vals = lasagne.layers.get_output(self.next_l_out,
next_states / input_scale
)
# mask_input=mask)
else:
next_q_vals = lasagne.layers.get_output(self.l_out,
next_states / input_scale
)
# mask_input=mask)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
target = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
diff = target - q_vals[T.arange(target.shape[0]),
actions.reshape((-1,))].reshape((-1, 1))
if self.clip_delta > 0:
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_delta)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + self.clip_delta * linear_part
else:
loss = 0.5 * diff ** 2
if batch_accumulator == 'sum':
loss = T.sum(loss)
elif batch_accumulator == 'mean':
loss = T.mean(loss)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
params = lasagne.layers.helper.get_all_params(self.l_out)
# print params
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
if update_rule == 'deepmind_rmsprop':
update_for = lambda params: deepmind_rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
update_for = lambda params: lasagne.updates.rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'sgd':
update_for = lambda params: lasagne.updates.sgd(loss, params, self.lr)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
updates = update_for(params)
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
# # Super mega shady stuff
# # Somehow an update sneaks in for cell and hid. Kill it with fire
if self.lstm:
delete_keys = [k for k, v in updates.items() if k.name in ['cell', 'hid']]
# print delete_keys
for key in delete_keys:
del updates[key]
self._train = theano.function([], [loss, q_vals], updates=updates,
givens=givens)
self._q_vals = theano.function([], q_vals,
givens={states: self.states_shared})
def __init__(self, batchSize, numFrames, inputHeight, inputWidth, numActions,
discountRate, learningRate, rho, rms_epsilon, momentum, networkUpdateDelay, useSARSAUpdate, kReturnLength,
networkType = "conv", updateRule = "deepmind_rmsprop", batchAccumulator = "sum", clipDelta = 1.0, inputScale = 255.0):
self.batchSize = batchSize
self.numFrames = numFrames
self.inputWidth = inputWidth
self.inputHeight = inputHeight
self.inputScale = inputScale
self.numActions = numActions
self.discountRate = discountRate
self.learningRate = learningRate
self.rho = rho
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.networkUpdateDelay = networkUpdateDelay
self.useSARSAUpdate = useSARSAUpdate
self.kReturnLength = kReturnLength
self.networkType = networkType
self.updateRule = updateRule
self.batchAccumulator = batchAccumulator
self.clipDelta = clipDelta
self.updateCounter = 0
states = T.tensor4("states")
nextStates = T.tensor4("nextStates")
rewards = T.col("rewards")
actions = T.icol("actions")
nextActions= T.icol("nextActions")
terminals = T.icol("terminals")
self.statesShared = theano.shared(np.zeros((self.batchSize, self.numFrames, self.inputHeight, self.inputWidth), dtype=theano.config.floatX))
self.nextStatesShared = theano.shared(np.zeros((self.batchSize, self.numFrames, self.inputHeight, self.inputWidth), dtype=theano.config.floatX))
self.rewardsShared = theano.shared(np.zeros((self.batchSize, 1), dtype=theano.config.floatX), broadcastable=(False, True))
self.actionsShared = theano.shared(np.zeros((self.batchSize, 1), dtype='int32'), broadcastable=(False, True))
self.nextActionsShared = theano.shared(np.zeros((self.batchSize, 1), dtype='int32'), broadcastable=(False, True))
self.terminalsShared = theano.shared(np.zeros((self.batchSize, 1), dtype='int32'), broadcastable=(False, True))
self.qValueNetwork = DeepNetworks.buildDeepQNetwork(
self.batchSize, self.numFrames, self.inputHeight, self.inputWidth, self.numActions, self.networkType)
qValues = lasagne.layers.get_output(self.qValueNetwork, states / self.inputScale)
if self.networkUpdateDelay > 0:
self.nextQValueNetwork = DeepNetworks.buildDeepQNetwork(
self.batchSize, self.numFrames, self.inputHeight, self.inputWidth, self.numActions, self.networkType)
self.resetNextQValueNetwork()
nextQValues = lasagne.layers.get_output(self.nextQValueNetwork, nextStates / self.inputScale)
else:
nextQValues = lasagne.layers.get_output(self.qValueNetwork, nextStates / self.inputScale)
nextQValues = theano.gradient.disconnected_grad(nextQValues)
if self.useSARSAUpdate:
target = rewards + terminals * (self.discountRate ** self.kReturnLength) * nextQValues[T.arange(self.batchSize), nextActions.reshape((-1,))].reshape((-1, 1))
else:
target = rewards + terminals * (self.discountRate ** self.kReturnLength) * T.max(nextQValues, axis = 1, keepdims = True)
targetDifference = target - qValues[T.arange(self.batchSize), actions.reshape((-1,))].reshape((-1, 1))
quadraticPart = T.minimum(abs(targetDifference), self.clipDelta)
linearPart = abs(targetDifference) - quadraticPart
# if self.clipDelta > 0:
# targetDifference = targetDifference.clip(-1.0 * self.clipDelta, self.clipDelta)
if self.batchAccumulator == "sum":
# loss = T.sum(targetDifference ** 2)
loss = T.sum(0.5 * quadraticPart ** 2 + self.clipDelta * linearPart)
elif self.batchAccumulator == "mean":
# loss = T.mean(targetDifference ** 2)
loss = T.mean(0.5 * quadraticPart ** 2 + self.clipDelta * linearPart)
else:
raise ValueError("Bad Network Accumulator. {sum, mean} expected")
networkParameters = lasagne.layers.helper.get_all_params(self.qValueNetwork)
if self.updateRule == "deepmind_rmsprop":
updates = DeepNetworks.deepmind_rmsprop(loss, networkParameters, self.learningRate, self.rho, self.rms_epsilon)
elif self.updateRule == "rmsprop":
updates = lasagne.updates.rmsprop(loss, networkParameters, self.learningRate, self.rho, self.rms_epsilon)
elif self.updateRule == "sgd":
updates = lasagne.updates.sgd(loss, networkParameters, self.learningRate)
else:
raise ValueError("Bad update rule. {deepmind_rmsprop, rmsprop, sgd} expected")
if self.momentum > 0:
updates.lasagne.updates.apply_momentum(updates, None, self.momentum)
lossGivens = {
states: self.statesShared,
nextStates: self.nextStatesShared,
rewards:self.rewardsShared,
actions: self.actionsShared,
nextActions: self.nextActionsShared,
terminals: self.terminalsShared
}
self.__trainNetwork = theano.function([], [loss, qValues], updates=updates, givens=lossGivens, on_unused_input='warn')
self.__computeQValues = theano.function([], qValues, givens={states: self.statesShared})
def __init__(self, input, n_in, n_out):
hidden_size = 36
batch_size = 32
self._w_h = init_weights((n_in, hidden_size))
self._b_h = init_b_weights((1, hidden_size))
# self._b_h = init_b_weights((hidden_size,))
self._w_h2 = init_weights((hidden_size, hidden_size))
self._b_h2 = init_b_weights((1, hidden_size))
# self._b_h2 = init_b_weights((hidden_size,))
# self._w_o = init_tanh(hidden_size, n_out)
self._w_o = init_weights((hidden_size, n_out))
self._b_o = init_b_weights((1, n_out))
# self._b_o = init_b_weights((n_out,))
self.updateTargetModel()
self._w_h_old = init_weights((n_in, hidden_size))
self._w_h2_old = init_weights((hidden_size, hidden_size))
self._w_o_old = init_tanh(hidden_size, n_out)
# print ("Initial W " + str(self._w_o.get_value()) )
self._learning_rate = 0.00025
self._discount_factor = 0.99
self._weight_update_steps = 5000
self._updates = 0
# data types for model
State = T.dmatrix("State")
State.tag.test_value = np.random.rand(batch_size, 2)
ResultState = T.dmatrix("ResultState")
ResultState.tag.test_value = np.random.rand(batch_size, 2)
Reward = T.col("Reward")
Reward.tag.test_value = np.random.rand(batch_size, 1)
Action = T.icol("Action")
Action.tag.test_value = np.zeros((batch_size, 1),
dtype=np.dtype('int32'))
# Q_val = T.fmatrix()
# model = T.nnet.sigmoid(T.dot(State, self._w) + self._b.reshape((1, -1)))
# self._model = theano.function(inputs=[State], outputs=model, allow_input_downcast=True)
_py_xA = self.model(State, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.0, 0.0)
_py_xB = self.model(State, self._w_h_old, self._b_h_old,
self._w_h2_old, self._b_h2_old, self._w_o_old,
self._b_o_old, 0.0, 0.0)
self._y_predA = T.argmax(_py_xA, axis=1)
self._y_predB = T.argmax(_py_xB, axis=1)
self._q_funcA = T.mean(
(self.model(State, self._w_h, self._b_h, self._w_h2, self._b_h2,
self._w_o, self._b_o, 0.0,
0.0))[T.arange(batch_size),
Action.reshape((-1, ))].reshape((-1, 1)))
self._q_funcB = T.mean(
(self.model(State, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.0,
0.0))[T.arange(batch_size),
Action.reshape((-1, ))].reshape((-1, 1)))
# q_val = py_x
# noisey_q_val = self.model(ResultState, self._w_h, self._b_h, self._w_h2, self._b_h2, self._w_o, self._b_o, 0.2, 0.5)
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
self._L1_A = (abs(self._w_h).sum() + abs(self._w_h2).sum() +
abs(self._w_o).sum())
self._L1_B = (abs(self._w_h_old).sum() + abs(self._w_h2_old).sum() +
abs(self._w_o_old).sum())
self._L1_reg = 0.0
self._L2_reg = 0.001
# L2 norm ; one regularization option is to enforce
# L2 norm to be small
self._L2_A = ((self._w_h**2).sum() + (self._w_h2**2).sum() +
(self._w_o**2).sum())
self._L2_B = ((self._w_h_old**2).sum() + (self._w_h2_old**2).sum() +
(self._w_o_old**2).sum())
# cost = T.mean(T.nnet.categorical_crossentropy(py_x, Y))
# delta = ((Reward.reshape((-1, 1)) + (self._discount_factor * T.max(self.model(ResultState), axis=1, keepdims=True)) ) - self.model(State))
deltaA = ((Reward + (self._discount_factor * T.max(self.model(
ResultState, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.2, 0.5),
axis=1,
keepdims=True))) -
(self.model(State, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.2,
0.5))[T.arange(Action.shape[0]),
Action.reshape((-1, ))].reshape((-1, 1)))
deltaB = (
(Reward +
(self._discount_factor *
T.max(self.model(ResultState, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.2, 0.5),
axis=1,
keepdims=True))) -
(self.model(State, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.2,
0.5))[T.arange(Action.shape[0]),
Action.reshape((-1, ))].reshape((-1, 1)))
# bellman_cost = T.mean( 0.5 * ((delta) ** 2 ))
bellman_costA = T.mean(0.5 * ((deltaA)**2)) + (
self._L2_reg * self._L2_A) + (self._L1_reg * self._L1_A)
bellman_costB = T.mean(0.5 * ((deltaB)**2)) + (
self._L2_reg * self._L2_B) + (self._L1_reg * self._L1_B)
paramsA = [
self._w_h, self._b_h, self._w_h2, self._b_h2, self._w_o, self._b_o
]
paramsB = [
self._w_h_old, self._b_h_old, self._w_h2_old, self._b_h2_old,
self._w_o_old, self._b_o_old
]
# updates = sgd(bellman_cost, params, lr=self._learning_rate)
updatesA = rlTDSGD(self._q_funcA,
T.mean(deltaA),
paramsA,
lr=self._learning_rate)
updatesB = rlTDSGD(self._q_funcB,
T.mean(deltaB),
paramsB,
lr=self._learning_rate)
# updates = RMSprop(bellman_cost, params, lr=self._learning_rate)
# updates = RMSpropRL(q_func, T.mean(delta), params, lr=self._learning_rate)
# updates = lasagne.updates.rmsprop(bellman_cost, params, self._learning_rate, 0.95, 0.01)
# updatesA = lasagne.updates.rmsprop(self._q_funcA, paramsA, self._learning_rate * -T.mean(deltaA), 0.95, 0.01)
# updatesB = lasagne.updates.rmsprop(self._q_funcB, paramsB, self._learning_rate * -T.mean(deltaB), 0.95, 0.01)
self._trainA = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=bellman_costA,
updates=updatesA,
allow_input_downcast=True)
self._trainB = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=bellman_costB,
updates=updatesB,
allow_input_downcast=True)
self._bellman_errorA = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=deltaA,
allow_input_downcast=True)
self._bellman_errorB = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=deltaB,
allow_input_downcast=True)
self._q_valuesA = theano.function(inputs=[State],
outputs=_py_xA,
allow_input_downcast=True)
self._q_valuesB = theano.function(inputs=[State],
outputs=_py_xB,
allow_input_downcast=True)
self._py_xA = theano.function(inputs=[State],
outputs=_py_xA,
allow_input_downcast=True)
self._py_xB = theano.function(inputs=[State],
outputs=_py_xB,
allow_input_downcast=True)
x, y = T.matrices('x', 'y')
z_lazy = ifelse(T.gt(T.max(x, axis=1)[0],
T.max(y, axis=1)[0]), T.argmax(x, axis=1),
T.argmax(y, axis=1))
self._f_lazyifelse = theano.function([x, y],
z_lazy,
mode=theano.Mode(linker='vm'))
def _update_classifier(self, data, labels, w, classes):
"""Update the classifier parameters theta and bias
Parameters
----------
data : list of 2D arrays, element i has shape=[voxels_i, samples_i]
Each element in the list contains the fMRI data of one subject for
the classification task.
labels : list of arrays of int, element i has shape=[samples_i]
Each element in the list contains the labels for the data samples
in data_sup.
w : list of 2D array, element i has shape=[voxels_i, features]
The orthogonal transforms (mappings) :math:`W_i` for each subject.
classes : int
The number of classes in the classifier.
Returns
-------
theta : array, shape=[features, classes]
The MLR parameter for the class planes.
bias : array shape=[classes,]
The MLR parameter for class biases.
"""
# Stack the data and labels for training the classifier
data_stacked, labels_stacked, weights = \
SSSRM._stack_list(data, labels, w)
features = w[0].shape[1]
total_samples = weights.size
data_th = S.shared(data_stacked.astype(theano.config.floatX))
val_ = S.shared(labels_stacked)
total_samples_S = S.shared(total_samples)
theta_th = T.matrix(name='theta', dtype=theano.config.floatX)
bias_th = T.col(name='bias', dtype=theano.config.floatX)
constf2 = S.shared(self.alpha / self.gamma, allow_downcast=True)
weights_th = S.shared(weights)
log_p_y_given_x = \
T.log(T.nnet.softmax((theta_th.T.dot(data_th.T)).T + bias_th.T))
f = -constf2 * T.sum(
(log_p_y_given_x[T.arange(total_samples_S), val_]) /
weights_th) + 0.5 * T.sum(theta_th**2)
manifold = Product((Euclidean(features,
classes), Euclidean(classes, 1)))
problem = Problem(manifold=manifold,
cost=f,
arg=[theta_th, bias_th],
verbosity=0)
solver = ConjugateGradient(mingradnorm=1e-6)
solution = solver.solve(problem)
theta = solution[0]
bias = solution[1]
del constf2
del theta_th
del bias_th
del data_th
del val_
del solver
del solution
return theta, bias
theano_dot = theano.function([theano_matrix1, theano_matrix2],
T.dot(theano_matrix1, theano_matrix2),
name='theano_dot')
theano_scalar = T.fscalar(name='theano_scalar')
theano_scale = theano.function([theano_matrix1, theano_scalar],
theano_matrix1 * theano_scalar,
name='scale')
# elementwise product
theano_multiply = theano.function([theano_matrix1, theano_matrix2],
theano_matrix1 * theano_matrix2,
name='theano_multiply')
theano_row_vector = T.row(name='theano_row_vector')
theano_col_vector = T.col(name='theano_col_vector')
theano_subtract_row = theano.function([theano_matrix1, theano_row_vector],
theano_matrix1 - theano_row_vector,
name='theano_subtract_row')
theano_divide_row = theano.function([theano_matrix1, theano_row_vector],
theano_matrix1 / theano_row_vector,
name='theano_divide_row')
theano_subtract_col = theano.function([theano_matrix1, theano_col_vector],
theano_matrix1 - theano_col_vector,
name='theano_subtract_col')
theano_divide_col = theano.function([theano_matrix1, theano_col_vector],
theano_matrix1 / theano_col_vector,
name='theano_divide_col')
theano_var1 = theano.function([theano_matrix1],
def __init__(self, input_width, input_height, avail_actions, num_actions,
num_frames, discount, learning_rate, rho,
rms_epsilon, momentum, clip_delta, freeze_interval,
batch_size, network_type, update_rule,
batch_accumulator, rng, train_all, input_scale=255.0):
self.input_width = input_width
self.input_height = input_height
self.avail_actions = avail_actions
self.num_actions = num_actions
self.num_frames = num_frames
self.batch_size = batch_size
self.discount = discount
self.rho = rho
self.lr = learning_rate
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self.rng = rng
self.train_all = train_all
lasagne.random.set_rng(self.rng)
self.update_counter = 0
print "num_actions: " + str(num_actions)
self.l_out = self.build_network(network_type, input_width, input_height,
num_actions, num_frames, batch_size)
if self.freeze_interval > 0:
self.next_l_out = self.build_network(network_type, input_width,
input_height, num_actions,
num_frames, batch_size)
self.reset_q_hat()
states = T.tensor4('states')
next_states = T.tensor4('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
self.states_shared = theano.shared(
np.zeros((batch_size, num_frames, input_height, input_width),
dtype=theano.config.floatX))
self.next_states_shared = theano.shared(
np.zeros((batch_size, num_frames, input_height, input_width),
dtype=theano.config.floatX))
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
q_vals = lasagne.layers.get_output(self.l_out, states / input_scale)
if self.freeze_interval > 0:
next_q_vals = lasagne.layers.get_output(self.next_l_out,
next_states / input_scale)
else:
next_q_vals = lasagne.layers.get_output(self.l_out,
next_states / input_scale)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
target = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
diff = target - q_vals[T.arange(batch_size),
actions.reshape((-1,))].reshape((-1, 1))
if self.clip_delta > 0:
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_delta)
linear_part = abs(diff) - quadratic_part
loss = 0.5 * quadratic_part ** 2 + self.clip_delta * linear_part
else:
loss = 0.5 * diff ** 2
if batch_accumulator == 'sum':
loss = T.sum(loss)
elif batch_accumulator == 'mean':
loss = T.mean(loss)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
params = lasagne.layers.helper.get_all_params(self.l_out)
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
if update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'sgd':
updates = lasagne.updates.sgd(loss, params, self.lr)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([], [loss, q_vals], updates=updates,
givens=givens)
self._q_vals = theano.function([], q_vals,
givens={states: self.states_shared})
def __init__(self, datasetPaths=None, keyPoints=None):
self.xMax = 1024.0
self.yMax = 576.0
self.numKeyPoints = 68
self.loadPicasaTubePickle()
loadPrev = 1
if loadPrev == 1:
pkl_file = open('faceAlign2.pkl', 'rb')
self.pose, self.landmarks, self.poseDict, self.images, self.poseCat = pickle.load(
pkl_file)
pkl_file.close()
else:
self.loadData()
output = open('faceAlign2.pkl', 'wb')
data = (self.pose, self.landmarks, self.poseDict, self.images,
self.poseCat)
pickle.dump(data, output)
output.close()
return
self.eeta = 0.0000001
self.mu = theano.shared(10 * numpy.random.random(
(2 * self.numKeyPoints, 1)))
self.S = theano.shared(numpy.eye(2 * self.numKeyPoints))
self.alpha = theano.shared(0.1 * numpy.ones(
(2 * self.numKeyPoints, 1)))
theano.config.compute_test_value = 'warn'
oneCol = T.col('oneCol')
oneCol.tag.test_value = numpy.ones((self.numKeyPoints, 1))
pi_t = T.col('pi_t')
pi_t.tag.test_value = numpy.random.random((2 * self.numKeyPoints, 1))
temp = numpy.random.random((3, 3))
#temp = numpy.zeros((3,3))
temp[2, :] = [0, 0, 1]
self.A_t = theano.shared(temp, name='A_t')
#print_A_t = theano.printing.Print('r_t1')(A_t)
z_t = T.col('z_t')
z_t.tag.test_value = numpy.random.random((2 * self.numKeyPoints, 1))
z_t1 = z_t.reshape((self.numKeyPoints, 2))
pts = T.concatenate((z_t1, oneCol), axis=1)
# pts = theano.printing.Print('pts')(pts)
r_t = T.dot(self.A_t, pts.transpose()).transpose()
r_t1 = r_t[:, 0:2].reshape((2 * self.numKeyPoints, 1))
#pi_tt = theano.printing.Print('pi_t before')(pi_t)
diff = pi_t * (r_t1 - self.mu)
difft = diff.reshape((1, 2 * self.numKeyPoints))
#diff = theano.printing.Print('diff:')(diff)
cost = T.max(T.dot(T.dot(difft, self.S), diff))
#cost = theano.printing.Print('cost:')(cost)
A_t_grad = T.grad(cost=cost, wrt=self.A_t)
A_t_grad = T.basic.set_subtensor(A_t_grad[2, :], 0)
#A_t_grad = theano.printing.Print('r_t1')(A_t_grad)
update = (self.A_t, self.A_t - self.eeta * A_t_grad)
self.align = theano.function(inputs=[pi_t, z_t, oneCol],
outputs=[self.A_t, cost],
updates=[update],
on_unused_input='warn',
allow_input_downcast=True)
#for numpy optimization
A_t_ = T.matrix('A_t_')
#A_t_.tag.test_value = temp
#A_t_ = A_t_.reshape((3,3))
A_t_.tag.test_value = temp
#print_A_t = theano.printing.Print('r_t1')(A_t)
r_t_ = T.dot(A_t_, pts.transpose()).transpose()
r_t1_ = r_t_[:, 0:2].reshape((2 * self.numKeyPoints, 1))
#pi_tt = theano.printing.Print('pi_t before')(pi_t)
diff_ = pi_t * (r_t1_ - self.mu)
difft_ = diff_.reshape((1, 2 * self.numKeyPoints))
#diff = theano.printing.Print('diff:')(diff)
cost_1 = T.dot(T.dot(difft_, self.S), diff_)
#cost_1 = theano.printing.Print('cost is:')(cost_1)
cost_ = T.max(cost_1)
A_t_grad_ = T.grad(cost=cost_, wrt=A_t_)
A_t_grad_ = T.basic.set_subtensor(A_t_grad_[2, :], 0)
#A_t_grad_ = A_t_grad_.reshape((9,1))
self.cost = theano.function(inputs=[A_t_, pi_t, z_t, oneCol],
outputs=[cost_, A_t_grad_])
i = T.iscalar('index')
i.tag.test_value = 0
subS = self.S[2 * i:2 * i + 2, 2 * i:2 * i + 2]
#subS = theano.printing.Print('subS:')(self.S[2*i:2*i+2, 2*i:2*i+2])
det = T.abs_(subS[0, 0] * subS[1, 1] - subS[0, 1] * subS[1, 0])
subDiff = diff[(2 * i):((2 * i) + 2)]
subDifft = difft[0][(2 * i):(2 * i + 2)]
#intermed = theano.printing.Print('dotProd1:')(T.dot(subDifft,subS))
intermed = T.dot(subDifft, subS)
#intermed2 = theano.printing.Print('dotProd2:')(T.dot(intermed,subDiff))
intermed2 = T.dot(intermed, subDiff)
numrtr = T.exp(-0.5 * intermed2)
k = 2
dnmntr = T.sqrt((2**k) * det)
q = numrtr / dnmntr
temp = ((1 - self.alpha[2 * i:2 * i + 2]) *
q) / (self.alpha[2 * i:2 * i + 2] +
(1 - self.alpha[2 * i:2 * i + 2]) * q)
pi_t_out = T.basic.set_subtensor(pi_t[2 * i:2 * i + 2], temp)
self.q_pi_update = theano.function(inputs=[i, oneCol, pi_t, z_t],
outputs=[q, pi_t_out, r_t1],
allow_input_downcast=True)
self.train('12')
def __init__(self, input, n_in, n_out):
hidden_size = 36
batch_size = 32
self._w_h = init_weights((n_in, hidden_size))
self._b_h = init_b_weights((1, hidden_size))
# self._b_h = init_b_weights((hidden_size,))
self._w_h2 = init_weights((hidden_size, hidden_size))
self._b_h2 = init_b_weights((1, hidden_size))
# self._b_h2 = init_b_weights((hidden_size,))
# self._w_o = init_tanh(hidden_size, n_out)
self._w_o = init_weights((hidden_size, n_out))
self._b_o = init_b_weights((1, n_out))
# self._b_o = init_b_weights((n_out,))
self.updateTargetModel()
self._w_h_old = init_weights((n_in, hidden_size))
self._w_h2_old = init_weights((hidden_size, hidden_size))
self._w_o_old = init_tanh(hidden_size, n_out)
# print ("Initial W " + str(self._w_o.get_value()) )
self._learning_rate = 0.00025
self._discount_factor = 0.99
self._weight_update_steps = 5000
self._updates = 0
# data types for model
State = T.dmatrix("State")
State.tag.test_value = np.random.rand(batch_size, 2)
ResultState = T.dmatrix("ResultState")
ResultState.tag.test_value = np.random.rand(batch_size, 2)
Reward = T.col("Reward")
Reward.tag.test_value = np.random.rand(batch_size, 1)
Action = T.icol("Action")
Action.tag.test_value = np.zeros((batch_size, 1),
dtype=np.dtype('int32'))
# Q_val = T.fmatrix()
# model = T.nnet.sigmoid(T.dot(State, self._w) + self._b.reshape((1, -1)))
# self._model = theano.function(inputs=[State], outputs=model, allow_input_downcast=True)
py_x = self.model(State, self._w_h, self._b_h, self._w_h2, self._b_h2,
self._w_o, self._b_o, 0.0, 0.0)
y_pred = T.argmax(py_x, axis=1)
q_func = T.mean((self.model(State, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.0,
0.0))[T.arange(batch_size),
Action.reshape((-1, ))].reshape(
(-1, 1)))
# q_val = py_x
# noisey_q_val = self.model(ResultState, self._w_h, self._b_h, self._w_h2, self._b_h2, self._w_o, self._b_o, 0.2, 0.5)
# L1 norm ; one regularization option is to enforce L1 norm to
# be small
self._L1 = (abs(self._w_h).sum() + abs(self._w_h2).sum() +
abs(self._w_o).sum())
self._L1_reg = 0.0
self._L2_reg = 0.001
# L2 norm ; one regularization option is to enforce
# L2 norm to be small
self._L2 = ((self._w_h**2).sum() + (self._w_h2**2).sum() +
(self._w_o**2).sum())
# cost = T.mean(T.nnet.categorical_crossentropy(py_x, Y))
# delta = ((Reward.reshape((-1, 1)) + (self._discount_factor * T.max(self.model(ResultState), axis=1, keepdims=True)) ) - self.model(State))
delta = ((Reward + (self._discount_factor * T.max(self.model(
ResultState, self._w_h_old, self._b_h_old, self._w_h2_old,
self._b_h2_old, self._w_o_old, self._b_o_old, 0.2, 0.5),
axis=1,
keepdims=True))) -
(self.model(State, self._w_h, self._b_h, self._w_h2,
self._b_h2, self._w_o, self._b_o, 0.2,
0.5))[T.arange(Action.shape[0]),
Action.reshape((-1, ))].reshape((-1, 1)))
# bellman_cost = T.mean( 0.5 * ((delta) ** 2 ))
bellman_cost = T.mean(0.5 * ((delta)**2)) + (
self._L2_reg * self._L2) + (self._L1_reg * self._L1)
params = [
self._w_h, self._b_h, self._w_h2, self._b_h2, self._w_o, self._b_o
]
# updates = sgd(bellman_cost, params, lr=self._learning_rate)
# updates = rlTDSGD(q_func, T.mean(delta), params, lr=self._learning_rate)
# updates = RMSprop(bellman_cost, params, lr=self._learning_rate)
# updates = RMSpropRL(q_func, T.mean(delta), params, lr=self._learning_rate)
# updates = lasagne.updates.rmsprop(bellman_cost, params, self._learning_rate, 0.95, 0.01)
updates = lasagne.updates.rmsprop(q_func, params,
self._learning_rate * -T.mean(delta),
0.95, 0.01)
self._train = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=bellman_cost,
updates=updates,
allow_input_downcast=True)
self._predict = theano.function(inputs=[State],
outputs=y_pred,
allow_input_downcast=True)
self._q_values = theano.function(inputs=[State],
outputs=py_x,
allow_input_downcast=True)
self._bellman_error = theano.function(
inputs=[State, Action, Reward, ResultState],
outputs=delta,
allow_input_downcast=True)
def _compile_train_function(self):
state = T.tensor4(dtype = theano.config.floatX)
action = T.col(dtype = 'uint8')
reward = T.col(dtype = theano.config.floatX)
terminal = T.col(dtype = 'int8')
next_state = T.tensor4(dtype = theano.config.floatX)
current_values_matrix = lasagne.layers.get_output(self.net, state)
action_mask = T.eq(T.arange(self.num_action).reshape((1, -1))
, action.reshape((-1, 1))).astype(theano.config.floatX)
current_values = T.sum(current_values_matrix * action_mask
, axis = 1).reshape((-1, 1))
if self.algorithm == 'q_learning':
if self.tnet is not None:
target_values = lasagne.layers.get_output(self.tnet, next_state)
else:
target_values = lasagne.layers.get_output(self.net, next_state)
bootstrap_values = T.max(target_values, axis = 1, keepdims = True)
elif self.algorithm == 'double_q_learning':
if self.network_type == 'duel':
# Get argmax actions from advantage values
select_actions = self._get_action_var(self.adv_net, next_state)
else:
# Get argmax actions from Q values
select_actions = self._get_action_var(self.net, next_state)
select_mask = T.eq(T.arange(self.num_action).reshape((1, -1))
, select_actions.astype(theano.config.floatX))
if self.tnet is not None:
# Evaluate argmax actions on target network
eval_values = lasagne.layers.get_output(self.tnet, next_state)
else:
# Evaluate argmax actions on online network
# (the same as q_learning but slower)
eval_values = lasagne.layers.get_output(self.net, next_state)
bootstrap_values = T.sum(eval_values * select_mask
, axis = 1, keepdims = True)
terminal_floatX = terminal.astype(theano.config.floatX)
target_values = reward + self.discount * \
(T.ones_like(terminal_floatX) - terminal_floatX) * bootstrap_values
if self.tnet is None:
target_values = theano.gradient.disconnected_grad(target_values)
error = target_values - current_values
if self.max_error > 0:
# From https://github.com/spragunr/deep_q_rl/issues/46
quadratic_term = T.minimum(abs(error), self.max_error)
linear_term = abs(error) - quadratic_term
loss = T.sum(0.5 * quadratic_term ** 2 + linear_term * self.max_error)
else:
loss = T.sum(0.5 * error ** 2)
net_params = lasagne.layers.get_all_params(self.net)
updates = self._get_rmsprop_updates(loss, net_params
, lr = Network.LEARNING_RATE, grad_momentum = Network.GRAD_MOMENTUM
, sqr_momentum = Network.SGRAD_MOMENTUM
, min_grad = Network.MIN_SGRAD)
train_givens = {
state : self.shared_states[:, :-1, :, :] / Network.INPUT_SCALE,
action : self.shared_action,
reward : self.shared_reward,
terminal : self.shared_terminal,
next_state : self.shared_states[:, 1:, :, :] / Network.INPUT_SCALE,
}
return theano.function([], loss, updates = updates, givens = train_givens)
if param_name in exp_params.keys():
param_value = exp_params[param_name]
break
return param_value
########################################################################
if __name__ == "__main__":
### this block is for testing functions
W = T.matrix('W')
x = T.col('x')
b = T.col('b')
quadratic_form = energy_function(W, b, x)
compute_quad_form = theano.function([W,x,b],quadratic_form)
print(compute_quad_form([[1,2],[3,4]], [[1],[1]], [[1],[1]])[0][0] == 12)
grad_W, grad_b = T.grad(quadratic_form[0][0], [W,b])
comp_grad_W = theano.function([W,b,x], grad_W)
comp_grad_b = theano.function([W,b,x], grad_b)
def __init__(self, n_in, n_out, state_bounds, action_bounds, reward_bound,
settings_):
super(DeepCNNDropoutCritic,
self).__init__(n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_)
# data types for model
self._dropout_p = settings_['dropout_p']
# data types for model
self._State = T.matrix("State")
self._State.tag.test_value = np.random.rand(self._batch_size,
self._state_length)
self._ResultState = T.matrix("ResultState")
self._ResultState.tag.test_value = np.random.rand(
self._batch_size, self._state_length)
self._Reward = T.col("Reward")
self._Reward.tag.test_value = np.random.rand(self._batch_size, 1)
self._Target = T.col("Target")
self._Target.tag.test_value = np.random.rand(self._batch_size, 1)
self._Action = T.matrix("Action")
self._Action.tag.test_value = np.random.rand(self._batch_size,
self._action_length)
# create a small convolutional neural network
input = lasagne.layers.InputLayer((None, self._state_length),
self._State)
self._stateInputVar = input.input_var
inputAction = lasagne.layers.InputLayer((None, self._action_length),
self._Action)
self._actionInputVar = inputAction.input_var
taskFeatures = lasagne.layers.SliceLayer(
input,
indices=slice(0, self._settings['num_terrain_features']),
axis=1)
characterFeatures = lasagne.layers.SliceLayer(
input,
indices=slice(self._settings['num_terrain_features'],
self._state_length),
axis=1)
print("taskFeatures Shape:",
lasagne.layers.get_output_shape(taskFeatures))
print("characterFeatures Shape:",
lasagne.layers.get_output_shape(characterFeatures))
print("State length: ", self._state_length)
networkAct = lasagne.layers.InputLayer((None, self._state_length),
self._State)
# taskFeaturesAct = lasagne.layers.SliceLayer(networkAct, indices=slice(0, self._settings['num_terrain_features']), axis=1)
# characterFeaturesAct = lasagne.layers.SliceLayer(networkAct, indices=slice(self._settings['num_terrain_features'],self._state_length), axis=1)
# taskFeaturesAct = lasagne.layers.DropoutLayer(taskFeaturesAct, p=self._dropout_p, rescale=True)
networkAct = lasagne.layers.ReshapeLayer(
taskFeatures, (-1, 1, self._settings['num_terrain_features']))
networkAct = lasagne.layers.Conv1DLayer(
networkAct,
num_filters=16,
filter_size=8,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
# network = lasagne.layers.MaxPool1DLayer(network, pool_size=3)
"""
networkAct = lasagne.layers.Conv1DLayer(
networkAct, num_filters=32, filter_size=4,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
"""
networkAct = lasagne.layers.Conv1DLayer(
networkAct,
num_filters=8,
filter_size=4,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
# network = lasagne.layers.MaxPool1DLayer(network, pool_size=3)
self._actor_task_part = networkAct
"""
networkAct = lasagne.layers.Conv1DLayer(
networkAct, num_filters=32, filter_size=4,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
networkAct = lasagne.layers.DenseLayer(
networkAct, num_units=128,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
"""
networkAct = lasagne.layers.FlattenLayer(networkAct, outdim=2)
networkAct = lasagne.layers.ConcatLayer(
[networkAct, characterFeatures], axis=1)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
networkAct = lasagne.layers.DenseLayer(
networkAct,
num_units=64,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
networkAct = lasagne.layers.DenseLayer(
networkAct,
num_units=32,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# networkAct = lasagne.layers.DropoutLayer(networkAct, p=self._dropout_p, rescale=True)
self._actor = lasagne.layers.DenseLayer(
networkAct,
num_units=self._action_length,
nonlinearity=lasagne.nonlinearities.linear)
if (self._settings['use_stocastic_policy']):
with_std = lasagne.layers.DenseLayer(
networkAct,
num_units=self._action_length,
nonlinearity=theano.tensor.nnet.softplus)
self._actor = lasagne.layers.ConcatLayer([self._actor, with_std],
axis=1)
if (settings_['agent_name'] == 'algorithm.DPG.DPG'):
characterFeatures = lasagne.layers.ConcatLayer(
[characterFeatures, inputAction])
# taskFeatures = lasagne.layers.DropoutLayer(taskFeatures, p=self._dropout_p, rescale=True)
network = lasagne.layers.ReshapeLayer(
taskFeatures, (-1, 1, self._settings['num_terrain_features']))
network = lasagne.layers.Conv1DLayer(
network,
num_filters=16,
filter_size=8,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
network = lasagne.layers.DropoutLayer(network,
p=self._dropout_p,
rescale=True)
# network = lasagne.layers.MaxPool1DLayer(network, pool_size=3)
"""
network = lasagne.layers.Conv1DLayer(
network, num_filters=32, filter_size=4,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
"""
network = lasagne.layers.Conv1DLayer(
network,
num_filters=8,
filter_size=4,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
self._critic_task_part = network
"""
# network = lasagne.layers.MaxPool1DLayer(network, pool_size=3)
network = lasagne.layers.Conv1DLayer(
network, num_filters=32, filter_size=4,
nonlinearity=lasagne.nonlinearities.leaky_rectify,
W=lasagne.init.GlorotUniform())
network = lasagne.layers.DenseLayer(
network, num_units=128,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
"""
network = lasagne.layers.FlattenLayer(network, outdim=2)
network = lasagne.layers.ConcatLayer([network, characterFeatures],
axis=1)
# network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
network = lasagne.layers.DenseLayer(
network,
num_units=64,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network,
p=self._dropout_p,
rescale=True)
network = lasagne.layers.DenseLayer(
network,
num_units=32,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
network = lasagne.layers.DropoutLayer(network,
p=self._dropout_p,
rescale=True)
network = lasagne.layers.DenseLayer(
network,
num_units=16,
nonlinearity=lasagne.nonlinearities.leaky_rectify)
# network = lasagne.layers.DropoutLayer(network, p=self._dropout_p, rescale=True)
self._critic = lasagne.layers.DenseLayer(
network, num_units=1, nonlinearity=lasagne.nonlinearities.linear)
self._states_shared = theano.shared(
np.zeros((self._batch_size, self._state_length),
dtype=theano.config.floatX))
self._next_states_shared = theano.shared(
np.zeros((self._batch_size, self._state_length),
dtype=theano.config.floatX))
self._rewards_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self._target_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self._actions_shared = theano.shared(
np.zeros((self._batch_size, self._action_length),
dtype=theano.config.floatX), )
def __init__(self, input_width, input_height, num_actions,
num_frames, discount, learning_rate, rho,
rms_epsilon, momentum, clip_delta, freeze_interval,
batch_size, network_type, update_rule,
batch_accumulator, input_scale=255.0, reward_bias=0.):
self.input_width = input_width
self.input_height = input_height
self.num_actions = num_actions
self.num_frames = num_frames
self.batch_size = batch_size
self.discount = discount
self.rho = rho
self.lr = learning_rate
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self.update_counter = 0
self.l_out = self.build_network(network_type, input_width, input_height,
num_actions, num_frames, batch_size)
if self.freeze_interval > 0:
self.next_l_out = self.build_network(network_type, input_width,
input_height, num_actions,
num_frames, batch_size)
self.reset_q_hat()
states = T.tensor4('states')
next_states = T.tensor4('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
self.states_shared = theano.shared(
np.zeros((batch_size, num_frames, input_height, input_width),
dtype=theano.config.floatX))
self.next_states_shared = theano.shared(
np.zeros((batch_size, num_frames, input_height, input_width),
dtype=theano.config.floatX))
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
q_vals = lasagne.layers.get_output(self.l_out, states / input_scale)
if self.freeze_interval > 0:
next_q_vals = lasagne.layers.get_output(self.next_l_out,
next_states / input_scale)
else:
next_q_vals = lasagne.layers.get_output(self.l_out,
next_states / input_scale)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
target = (rewards + reward_bias +
(T.ones_like(terminals) - terminals) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
diff = target - q_vals[T.arange(batch_size),
actions.reshape((-1,))].reshape((-1, 1))
if self.clip_delta > 0:
diff = diff.clip(-self.clip_delta, self.clip_delta)
if batch_accumulator == 'sum':
loss = T.sum(diff ** 2)
elif batch_accumulator == 'mean':
loss = T.mean(diff ** 2)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
params = lasagne.layers.helper.get_all_params(self.l_out)
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
if update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'sgd':
updates = lasagne.updates.sgd(loss, params, self.lr)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([], [loss, q_vals], updates=updates,
givens=givens)
self._q_vals = theano.function([], q_vals,
givens={states: self.states_shared})
def __init__(self, model, n_in, n_out, state_bounds, action_bounds,
reward_bound, settings_):
"""
In order to get this to work we need to be careful not to update the actor parameters
when updating the critic. This can be an issue when the Concatenating networks together.
The first first network becomes a part of the second. However you can still access the first
network by itself but an updates on the second network will effect the first network.
Care needs to be taken to make sure only the parameters of the second network are updated.
"""
super(DPG, self).__init__(model, n_in, n_out, state_bounds,
action_bounds, reward_bound, settings_)
self._Fallen = T.bcol("Fallen")
## because float64 <= float32 * int32, need to use int16 or int8
self._Fallen.tag.test_value = np.zeros((self._batch_size, 1),
dtype=np.dtype('int8'))
self._fallen_shared = theano.shared(np.zeros((self._batch_size, 1),
dtype='int8'),
broadcastable=(False, True))
self._Action = T.matrix("Action2")
self._Action.tag.test_value = np.random.rand(self._batch_size,
self._action_length)
self._Tmp_Target = T.col("Tmp_Target")
self._Tmp_Target.tag.test_value = np.zeros(
(self._batch_size, 1),
dtype=np.dtype(self.getSettings()['float_type']))
self._tmp_target_shared = theano.shared(np.zeros(
(self._batch_size, 1), dtype=self.getSettings()['float_type']),
broadcastable=(False, True))
self._modelTarget = copy.deepcopy(model)
# print ("Initial W " + str(self._w_o.get_value()) )
self._learning_rate = self.getSettings()['learning_rate']
self._discount_factor = self.getSettings()['discount_factor']
self._rho = self.getSettings()['rho']
self._rms_epsilon = self.getSettings()['rms_epsilon']
self._weight_update_steps = self.getSettings(
)['steps_until_target_network_update']
self._updates = 0
self._decay_weight = self.getSettings()['regularization_weight']
self._critic_regularization_weight = self.getSettings(
)["critic_regularization_weight"]
self._critic_learning_rate = self.getSettings()["critic_learning_rate"]
# self._q_valsA = lasagne.layers.get_output(self._model.getCriticNetwork(), self._model.getStateSymbolicVariable(), deterministic=True)
# self._q_valsA_drop = lasagne.layers.get_output(self._model.getCriticNetwork(), self._model.getStateSymbolicVariable(), deterministic=False)
# self._q_valsNextState = lasagne.layers.get_output(self._model.getCriticNetwork(), self._model.getResultStateSymbolicVariable(), deterministic=True)
# self._q_valsTargetNextState = lasagne.layers.get_output(self._modelTarget.getCriticNetwork(), self._model.getResultStateSymbolicVariable(), deterministic=True)
# self._q_valsTarget = lasagne.layers.get_output(self._modelTarget.getCriticNetwork(), self._model.getStateSymbolicVariable(), deterministic=True)
# self._q_valsTarget_drop = lasagne.layers.get_output(self._modelTarget.getCriticNetwork(), self._model.getStateSymbolicVariable(), deterministic=False)
self._q_valsActA = lasagne.layers.get_output(
self._model.getActorNetwork(),
self._model.getStateSymbolicVariable(),
deterministic=True)
self._q_valsActTarget = lasagne.layers.get_output(
self._modelTarget.getActorNetwork(),
self._model.getResultStateSymbolicVariable(),
deterministic=True)
# self._q_valsActA_drop = lasagne.layers.get_output(self._model.getActorNetwork(), self._model.getStateSymbolicVariable(), deterministic=False)
inputs_1 = {
self._model.getStateSymbolicVariable(): self._model.getStates(),
self._model.getActionSymbolicVariable(): self._model.getActions()
}
self._q_valsA = lasagne.layers.get_output(
self._model.getCriticNetwork(), inputs_1)
inputs_1_policy = {
self._model.getStateSymbolicVariable(): self._model.getStates(),
self._model.getActionSymbolicVariable(): self._q_valsActA
}
self._q_vals_train_policy = lasagne.layers.get_output(
self._model.getCriticNetwork(), inputs_1_policy)
inputs_2 = {
self._modelTarget.getStateSymbolicVariable():
self._model.getResultStates(),
self._modelTarget.getActionSymbolicVariable():
self._model.getActions()
}
self._q_valsB_ = lasagne.layers.get_output(
self._modelTarget.getCriticNetwork(), inputs_2, deterministic=True)
self._q_func = self._q_valsA
self._q_funcB = self._q_valsB_
# self._q_funcTarget = self._q_valsTarget
# self._q_func_drop = self._q_valsA_drop
# self._q_funcTarget_drop = self._q_valsTarget_drop
self._q_funcAct = self._q_valsActA
# self._q_funcAct_drop = self._q_valsActA_drop
# self._q_funcAct = theano.function(inputs=[State], outputs=self._q_valsActA, allow_input_downcast=True)
# self._target = T.mul(T.add(self._model.getRewardSymbolicVariable(), T.mul(self._discount_factor, self._q_valsB )), self._Fallen)
self._diff = self._Tmp_Target - self._q_func
# self._diff_drop = self._target - self._q_func_drop
# loss = 0.5 * self._diff ** 2
loss = T.pow(self._diff, 2)
self._loss = T.mean(loss)
# self._loss_drop = T.mean(0.5 * self._diff_drop ** 2)
# assert len(lasagne.layers.helper.get_all_params(self._l_outA)) == 16
# Need to remove the action layers from these params
self._params = lasagne.layers.helper.get_all_params(
self._model.getCriticNetwork())
print("******Number of Layers is: " + str(
len(
lasagne.layers.helper.get_all_params(
self._model.getCriticNetwork()))))
print("******Number of Action Layers is: " + str(
len(
lasagne.layers.helper.get_all_params(
self._model.getActorNetwork()))))
self._actionParams = lasagne.layers.helper.get_all_params(
self._model.getActorNetwork())
self._givens_ = {
self._model.getStateSymbolicVariable():
self._model.getStates(),
self._model.getActionSymbolicVariable():
self._model.getActions(),
# self._Action: self._q_valsActTarget,
# self._model.getResultStateSymbolicVariable(): self._model.getResultStates(),
# self._model.getRewardSymbolicVariable(): self._model.getRewards(),
# self._Fallen: self._fallen_shared
self._Tmp_Target:
self._tmp_target_shared
}
self._actGivens = {
self._model.getStateSymbolicVariable(): self._model.getStates(),
# self._model.getResultStateSymbolicVariable(): self._model.getResultStates(),
# self._model.getRewardSymbolicVariable(): self._model.getRewards(),
# self._model.getActionSymbolicVariable(): self._model.getActions(),
# self._Fallen: self._fallen_shared
# self._tmp_diff: self._tmp_diff_shared
}
self._critic_regularization = (
self._critic_regularization_weight *
lasagne.regularization.regularize_network_params(
self._model.getCriticNetwork(), lasagne.regularization.l2))
## MSE update
self._value_grad = T.grad(self._loss + self._critic_regularization,
self._params)
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_ = lasagne.updates.adam(self._value_grad,
self._params,
self._critic_learning_rate,
beta1=0.9,
beta2=0.9,
epsilon=self._rms_epsilon)
self._givens_grad = {
self._model.getStateSymbolicVariable():
self._model.getStates(),
# self._model.getResultStateSymbolicVariable(): self._model.getResultStates(),
# self._model.getRewardSymbolicVariable(): self._model.getRewards(),
self._model.getActionSymbolicVariable():
self._model.getActions(),
}
## Some cool stuff to backprop action gradients
self._action_grad = T.matrix("Action_Grad")
self._action_grad.tag.test_value = np.zeros(
(self._batch_size, self._action_length),
dtype=np.dtype(self.getSettings()['float_type']))
self._action_grad_shared = theano.shared(
np.zeros((self._batch_size, self._action_length),
dtype=self.getSettings()['float_type']))
### Maximize wrt q function
self._action_mean_grads = T.grad(
cost=None,
wrt=self._actionParams,
known_grads={self._q_valsActA: self._action_grad_shared}),
print("Action grads: ", self._action_mean_grads[0])
## When passing in gradients it needs to be a proper list of gradient expressions
self._action_mean_grads = list(self._action_mean_grads[0])
# print ("isinstance(self._action_mean_grads, list): ", isinstance(self._action_mean_grads, list))
# print ("Action grads: ", self._action_mean_grads)
self._actionGRADUpdates = lasagne.updates.adam(
self._action_mean_grads,
self._actionParams,
self._learning_rate,
beta1=0.9,
beta2=0.9,
epsilon=self._rms_epsilon)
self._actGradGivens = {
self._model.getStateSymbolicVariable(): self._model.getStates(),
# self._model.getResultStateSymbolicVariable(): self._model.getResultStates(),
# self._model.getRewardSymbolicVariable(): self._model.getRewards(),
# self._model.getActionSymbolicVariable(): self._model.getActions(),
# self._Fallen: self._fallen_shared,
# self._advantage: self._advantage_shared,
# self._KL_Weight: self._kl_weight_shared
}
# theano.gradient.grad_clip(x, lower_bound, upper_bound) # // TODO
# self._actionUpdates = lasagne.updates.adam(-T.mean(self._q_vals_train_policy) +
# (self._decay_weight * lasagne.regularization.regularize_network_params(
# self._model.getActorNetwork(), lasagne.regularization.l2)), self._actionParams,
# self._learning_rate, beta1=0.9, beta2=0.9, epsilon=self._rms_epsilon)
if ('train_extra_value_function' in self.getSettings() and
(self.getSettings()['train_extra_value_function'] == True)):
self._valsA = lasagne.layers.get_output(
self._model._value_function,
self._model.getStateSymbolicVariable(),
deterministic=True)
self._valsA_drop = lasagne.layers.get_output(
self._model._value_function,
self._model.getStateSymbolicVariable(),
deterministic=False)
self._valsNextState = lasagne.layers.get_output(
self._model._value_function,
self._model.getResultStateSymbolicVariable(),
deterministic=True)
self._valsTargetNextState = lasagne.layers.get_output(
self._modelTarget._value_function,
self._model.getResultStateSymbolicVariable(),
deterministic=True)
self._valsTarget = lasagne.layers.get_output(
self._modelTarget._value_function,
self._model.getStateSymbolicVariable(),
deterministic=True)
# self._target = T.mul(T.add(self._model.getRewardSymbolicVariable(), T.mul(self._discount_factor, self._q_valsB )), self._Fallen)
# self._target = self._model.getRewardSymbolicVariable() + ((self._discount_factor * self._q_valsTargetNextState ) * self._NotFallen) + (self._NotFallen - 1)
self._v_target = self._model.getRewardSymbolicVariable() + (
self._discount_factor * self._valsTargetNextState)
self._v_diff = self._v_target - self._valsA
# loss = 0.5 * self._diff ** 2
loss_v = T.pow(self._v_diff, 2)
self._v_loss = T.mean(loss_v)
self._params_value = lasagne.layers.helper.get_all_params(
self._model._value_function)
self._givens_value = {
self._model.getStateSymbolicVariable():
self._model.getStates(),
self._model.getResultStateSymbolicVariable():
self._model.getResultStates(),
self._model.getRewardSymbolicVariable():
self._model.getRewards(),
# self._NotFallen: self._NotFallen_shared
# self._model.getActionSymbolicVariable(): self._actions_shared,
}
self._value_regularization = (
self._critic_regularization_weight *
lasagne.regularization.regularize_network_params(
self._model._value_function, lasagne.regularization.l2))
self._value_grad = T.grad(
self._v_loss + self._value_regularization, self._params_value)
print("Optimizing Value Function with ",
self.getSettings()['optimizer'], " method")
self._updates_value = lasagne.updates.adam(
self._value_grad,
self._params_value,
self._critic_learning_rate,
beta1=0.9,
beta2=0.9,
epsilon=self._rms_epsilon)
## TD update
DPG.compile(self)
d_rewards_var = TT.vector('d_rewards')
# policy.dist_info_sym returns a dictionary, whose values are symbolic expressions for quantities related to the
# distribution of the actions. For a Gaussian policy, it contains the mean and (log) standard deviation.
dist_info_vars = policy.dist_info_sym(observations_var)
surr = TT.sum(
-dist.log_likelihood_sym_1traj_GPOMDP(actions_var, dist_info_vars) *
d_rewards_var)
params = policy.get_params(trainable=True)
grad = theano.grad(surr, params)
eval_grad1 = TT.matrix('eval_grad0', dtype=grad[0].dtype)
eval_grad2 = TT.vector('eval_grad1', dtype=grad[1].dtype)
eval_grad3 = TT.col('eval_grad3', dtype=grad[2].dtype)
eval_grad4 = TT.vector('eval_grad4', dtype=grad[3].dtype)
eval_grad5 = TT.vector('eval_grad5', dtype=grad[4].dtype)
f_train = theano.function(
inputs=[observations_var, actions_var, d_rewards_var], outputs=grad)
f_update = theano.function(
inputs=[eval_grad1, eval_grad2, eval_grad3, eval_grad4, eval_grad5],
outputs=None,
updates=sgd([eval_grad1, eval_grad2, eval_grad3, eval_grad4, eval_grad5],
params,
learning_rate=learning_rate))
alla = []
for i in range(10):
if (load_policy):
def __init__(self, environment, rho, rms_epsilon, momentum, clip_delta, freeze_interval, batch_size, network_type,
update_rule, batch_accumulator, randomState, DoubleQ=False, TheQNet=NN):
""" Initialize environment
"""
QNetwork.__init__(self,environment, batch_size)
self.rho = rho
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self._DoubleQ = DoubleQ
self._randomState = randomState
QNet=TheQNet(self._batch_size, self._input_dimensions, self._n_actions, self._randomState)
self.update_counter = 0
states=[] # list of symbolic variables for each of the k element in the belief state
# --> [ T.tensor4 if observation of element=matrix, T.tensor3 if vector, T.tensor 2 if scalar ]
next_states=[] # idem than states at t+1
self.states_shared=[] # list of shared variable for each of the k element in the belief state
self.next_states_shared=[] # idem that self.states_shared at t+1
for i, dim in enumerate(self._input_dimensions):
if len(dim) == 3:
states.append(T.tensor4("%s_%s" % ("state", i)))
next_states.append(T.tensor4("%s_%s" % ("next_state", i)))
elif len(dim) == 2:
states.append(T.tensor3("%s_%s" % ("state", i)))
next_states.append(T.tensor3("%s_%s" % ("next_state", i)))
elif len(dim) == 1:
states.append( T.matrix("%s_%s" % ("state", i)) )
next_states.append( T.matrix("%s_%s" % ("next_state", i)) )
self.states_shared.append(theano.shared(np.zeros((batch_size,) + dim, dtype=theano.config.floatX) , borrow=False))
self.next_states_shared.append(theano.shared(np.zeros((batch_size,) + dim, dtype=theano.config.floatX) , borrow=False))
print("Number of observations per state: {}".format(len(self.states_shared)))
print("For each observation, historySize + ponctualObs_i.shape: {}".format(self._input_dimensions))
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
thediscount = T.scalar(name='thediscount', dtype=theano.config.floatX)
thelr = T.scalar(name='thelr', dtype=theano.config.floatX)
QNet=TheQNet(self._batch_size, self._input_dimensions, self._n_actions, self._randomState)
self.q_vals, self.params, shape_after_conv = QNet._buildDQN(states)
print("Number of neurons after spatial and temporal convolution layers: {}".format(shape_after_conv))
self.next_q_vals, self.next_params, shape_after_conv = QNet._buildDQN(next_states)
self._resetQHat()
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
self.actions_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
if(self._DoubleQ==True):
givens_next={}
for i, x in enumerate(self.next_states_shared):
givens_next[ states[i] ] = x
self.next_q_vals_current_qnet=theano.function([], self.q_vals,
givens=givens_next)
next_q_curr_qnet = theano.clone(self.next_q_vals)
argmax_next_q_vals=T.argmax(next_q_curr_qnet, axis=1, keepdims=True)
max_next_q_vals=self.next_q_vals[T.arange(batch_size),argmax_next_q_vals.reshape((-1,))].reshape((-1, 1))
else:
max_next_q_vals=T.max(self.next_q_vals, axis=1, keepdims=True)
T_ones_like=T.ones_like(T.ones_like(terminals) - terminals)
target = rewards + T_ones_like * thediscount * max_next_q_vals
q_val=self.q_vals[T.arange(batch_size), actions.reshape((-1,))].reshape((-1, 1))
# Note : Strangely (target - q_val) lead to problems with python 3.5, theano 0.8.0rc and floatX=float32...
diff = - q_val + target
if self.clip_delta > 0:
# This loss function implementation is taken from
# https://github.com/spragunr/deep_q_rl
# If we simply take the squared clipped diff as our loss,
# then the gradient will be zero whenever the diff exceeds
# the clip bounds. To avoid this, we extend the loss
# linearly past the clip point to keep the gradient constant
# in that regime.
#
# This is equivalent to declaring d loss/d q_vals to be
# equal to the clipped diff, then backpropagating from
# there, which is what the DeepMind implementation does.
quadratic_part = T.minimum(abs(diff), self.clip_delta)
linear_part = abs(diff) - quadratic_part
loss_ind = 0.5 * quadratic_part ** 2 + self.clip_delta * linear_part
else:
loss_ind = 0.5 * diff ** 2
if batch_accumulator == 'sum':
loss = T.sum(loss_ind)
elif batch_accumulator == 'mean':
loss = T.mean(loss_ind)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
givens = {
rewards: self.rewards_shared,
actions: self.actions_shared, ## actions not needed!
terminals: self.terminals_shared
}
for i, x in enumerate(self.states_shared):
givens[ states[i] ] = x
for i, x in enumerate(self.next_states_shared):
givens[ next_states[i] ] = x
gparams=[]
for p in self.params:
gparam = T.grad(loss, p)
gparams.append(gparam)
updates = []
if update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, self.params, gparams, thelr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
for i,(p, g) in enumerate(zip(self.params, gparams)):
acc = theano.shared(p.get_value() * 0.)
acc_new = rho * acc + (1 - self.rho) * g ** 2
gradient_scaling = T.sqrt(acc_new + self.rms_epsilon)
g = g / gradient_scaling
updates.append((acc, acc_new))
updates.append((p, p - thelr * g))
elif update_rule == 'sgd':
for i, (param, gparam) in enumerate(zip(self.params, gparams)):
updates.append((param, param - thelr * gparam))
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if(self._DoubleQ==True):
self._train = theano.function([thediscount, thelr, next_q_curr_qnet], [loss, loss_ind, self.q_vals], updates=updates,
givens=givens,
on_unused_input='warn')
else:
self._train = theano.function([thediscount, thelr], [loss, loss_ind, self.q_vals], updates=updates,
givens=givens,
on_unused_input='warn')
givens2={}
for i, x in enumerate(self.states_shared):
givens2[ states[i] ] = x
self._q_vals = theano.function([], self.q_vals,
givens=givens2,
on_unused_input='warn')
def __init__(self, datasetPaths = None, keyPoints = None):
self.xMax = 1024.0
self.yMax = 576.0
self.numKeyPoints = 68
self.loadPicasaTubePickle()
loadPrev = 1
if loadPrev == 1:
pkl_file = open('faceAlign2.pkl', 'rb')
self.pose, self.landmarks, self.poseDict, self.images, self.poseCat = pickle.load(pkl_file)
pkl_file.close()
else:
self.loadData()
output = open('faceAlign2.pkl', 'wb')
data = (self.pose, self.landmarks, self.poseDict, self.images, self.poseCat)
pickle.dump(data, output)
output.close()
return
self.eeta = 0.0000001
self.mu = theano.shared(10 * numpy.random.random((2*self.numKeyPoints, 1)))
self.S = theano.shared(numpy.eye(2 * self.numKeyPoints))
self.alpha = theano.shared(0.1 * numpy.ones((2 * self.numKeyPoints,1)))
theano.config.compute_test_value = 'warn'
oneCol = T.col('oneCol')
oneCol.tag.test_value = numpy.ones((self.numKeyPoints,1))
pi_t = T.col('pi_t')
pi_t.tag.test_value = numpy.random.random((2*self.numKeyPoints,1))
temp = numpy.random.random((3,3))
#temp = numpy.zeros((3,3))
temp[2,:] = [0,0,1]
self.A_t = theano.shared(temp, name='A_t')
#print_A_t = theano.printing.Print('r_t1')(A_t)
z_t = T.col('z_t')
z_t.tag.test_value = numpy.random.random((2*self.numKeyPoints,1))
z_t1 = z_t.reshape((self.numKeyPoints, 2))
pts = T.concatenate((z_t1, oneCol), axis=1)
# pts = theano.printing.Print('pts')(pts)
r_t = T.dot(self.A_t, pts.transpose()).transpose()
r_t1 = r_t[:,0:2].reshape((2*self.numKeyPoints,1))
#pi_tt = theano.printing.Print('pi_t before')(pi_t)
diff = pi_t * (r_t1 - self.mu)
difft = diff.reshape((1, 2 * self.numKeyPoints))
#diff = theano.printing.Print('diff:')(diff)
cost = T.max(T.dot(T.dot(difft,self.S),diff))
#cost = theano.printing.Print('cost:')(cost)
A_t_grad = T.grad(cost=cost, wrt=self.A_t)
A_t_grad = T.basic.set_subtensor(A_t_grad[2,:],0)
#A_t_grad = theano.printing.Print('r_t1')(A_t_grad)
update = (self.A_t, self.A_t - self.eeta * A_t_grad)
self.align = theano.function(inputs=[pi_t,z_t, oneCol],
outputs=[self.A_t, cost],
updates=[update],
on_unused_input='warn',
allow_input_downcast=True)
#for numpy optimization
A_t_ = T.matrix('A_t_')
#A_t_.tag.test_value = temp
#A_t_ = A_t_.reshape((3,3))
A_t_.tag.test_value = temp
#print_A_t = theano.printing.Print('r_t1')(A_t)
r_t_ = T.dot(A_t_, pts.transpose()).transpose()
r_t1_ = r_t_[:,0:2].reshape((2*self.numKeyPoints,1))
#pi_tt = theano.printing.Print('pi_t before')(pi_t)
diff_ = pi_t * (r_t1_ - self.mu)
difft_ = diff_.reshape((1, 2 * self.numKeyPoints))
#diff = theano.printing.Print('diff:')(diff)
cost_1 = T.dot(T.dot(difft_,self.S),diff_)
#cost_1 = theano.printing.Print('cost is:')(cost_1)
cost_ = T.max(cost_1)
A_t_grad_ = T.grad(cost=cost_, wrt=A_t_)
A_t_grad_ = T.basic.set_subtensor(A_t_grad_[2,:],0)
#A_t_grad_ = A_t_grad_.reshape((9,1))
self.cost = theano.function(inputs=[A_t_, pi_t, z_t, oneCol],
outputs=[cost_, A_t_grad_])
i = T.iscalar('index')
i.tag.test_value = 0
subS = self.S[2*i:2*i+2, 2*i:2*i+2]
#subS = theano.printing.Print('subS:')(self.S[2*i:2*i+2, 2*i:2*i+2])
det = T.abs_(subS[0,0]*subS[1,1] - subS[0,1]*subS[1,0])
subDiff = diff[(2*i):((2*i)+2)]
subDifft = difft[0][(2*i):(2*i+2)]
#intermed = theano.printing.Print('dotProd1:')(T.dot(subDifft,subS))
intermed = T.dot(subDifft,subS)
#intermed2 = theano.printing.Print('dotProd2:')(T.dot(intermed,subDiff))
intermed2 = T.dot(intermed,subDiff)
numrtr = T.exp(-0.5 * intermed2)
k = 2
dnmntr = T.sqrt((2**k) * det)
q = numrtr/dnmntr
temp = ((1 - self.alpha[2*i:2*i+2]) * q)/(self.alpha[2*i:2*i+2] + (1 - self.alpha[2*i:2*i+2]) * q)
pi_t_out = T.basic.set_subtensor(pi_t[2*i:2*i+2], temp)
self.q_pi_update = theano.function(inputs = [i, oneCol, pi_t, z_t],
outputs = [q,pi_t_out, r_t1],
allow_input_downcast=True)
self.train('12')
def __init__(self, input_width, input_height, num_actions,
num_frames, discount, learning_rate, rho,
rms_epsilon, momentum, clip_delta, freeze_interval,
batch_size, update_rule,
batch_accumulator, state_count, input_scale=255.0):
self.state_count=state_count
self.input_width = input_width
self.input_height = input_height
self.num_actions = num_actions
self.num_frames = num_frames
self.batch_size = batch_size
self.discount = discount
self.rho = rho
self.lr = learning_rate
self.rms_epsilon = rms_epsilon
self.momentum = momentum
self.clip_delta = clip_delta
self.freeze_interval = freeze_interval
self.update_counter = 0
self.l_out = self.build_nature_network_dnn(input_width, input_height,
num_actions, num_frames, batch_size)
if self.freeze_interval > 0:
self.next_l_out = self.build_nature_network_dnn(input_width,
input_height, num_actions,
num_frames, batch_size)
self.reset_q_hat()
states = T.matrix('states')
next_states = T.matrix('next_states')
rewards = T.col('rewards')
actions = T.icol('actions')
terminals = T.icol('terminals')
#buferis inputu viso batch
self.states_shared = theano.shared(
np.zeros((batch_size, state_count),
dtype=theano.config.floatX))
#buferis i koki state patenka visiem
self.next_states_shared = theano.shared(
np.zeros((batch_size, state_count),
dtype=theano.config.floatX))
#po 1 reward kiekvienam episode, tai kaip del atskiru veiksmu?
self.rewards_shared = theano.shared(
np.zeros((batch_size, 1), dtype=theano.config.floatX),
broadcastable=(False, True))
#po 1 priimta action kiekvienam episode
self.actions_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
#?? turbut 0 ir 1, ar paskutine verte ar ne
self.terminals_shared = theano.shared(
np.zeros((batch_size, 1), dtype='int32'),
broadcastable=(False, True))
#paima qvals ir nexxt qvals ir grazina skirtumus batchui, viskas tik pirmam kartui
q_vals = lasagne.layers.get_output(self.l_out, states / input_scale)
if self.freeze_interval > 0:
next_q_vals = lasagne.layers.get_output(self.next_l_out,
next_states / input_scale)
else:
next_q_vals = lasagne.layers.get_output(self.l_out,
next_states / input_scale)
next_q_vals = theano.gradient.disconnected_grad(next_q_vals)
target = (rewards +
(T.ones_like(terminals) - terminals) *
self.discount * T.max(next_q_vals, axis=1, keepdims=True))
diff = target - q_vals[T.arange(batch_size),
actions.reshape((-1,))].reshape((-1, 1))
#neaisku
if self.clip_delta > 0:
diff = diff.clip(-self.clip_delta, self.clip_delta)
if batch_accumulator == 'sum':
loss = T.sum(diff ** 2)
elif batch_accumulator == 'mean':
loss = T.mean(diff ** 2)
else:
raise ValueError("Bad accumulator: {}".format(batch_accumulator))
#
params = lasagne.layers.helper.get_all_params(self.l_out)
givens = {
states: self.states_shared,
next_states: self.next_states_shared,
rewards: self.rewards_shared,
actions: self.actions_shared,
terminals: self.terminals_shared
}
if update_rule == 'deepmind_rmsprop':
updates = deepmind_rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'rmsprop':
updates = lasagne.updates.rmsprop(loss, params, self.lr, self.rho,
self.rms_epsilon)
elif update_rule == 'adam':
updates = lasagne.updates.adam(loss, params, self.lr, self.rho, self.rho, self.rms_epsilon)
elif update_rule == 'adagrad':
updates = lasagne.updates.adagrad(loss, params, self.lr,
self.rms_epsilon)
elif update_rule == 'sgd':
updates = lasagne.updates.sgd(loss, params, self.lr)
elif update_rule == 'momentum':
updates = lasagne.updates.momentum(loss, params, self.lr, self.momentum)
else:
raise ValueError("Unrecognized update: {}".format(update_rule))
if self.momentum > 0:
updates = lasagne.updates.apply_momentum(updates, None,
self.momentum)
self._train = theano.function([], [loss, q_vals], updates=updates,
givens=givens)
self._q_vals = theano.function([], q_vals,
givens={states: self.states_shared})