def build_adv(make_obs_tf, q_func, num_actions, epsilon, noisy):
with tf.variable_scope('deepq', reuse=tf.AUTO_REUSE):
obs_tf_in = U.ensure_tf_input(make_obs_tf("observation"))
stochastic_ph_adv = tf.placeholder(tf.bool, (), name="stochastic_adv")
update_eps_ph_adv = tf.placeholder(tf.float32, (),
name="update_eps_adv")
eps = tf.get_variable("eps", (),
initializer=tf.constant_initializer(0))
update_eps_expr_adv = eps.assign(
tf.cond(update_eps_ph_adv >= 0, lambda: update_eps_ph_adv,
lambda: eps))
print("==========================================")
#def wrapper(x):
# return q_func(x, num_actions, scope="q_func", reuse=True, concat_softmax=True, noisy=noisy)
adversary = FastGradientMethod(q_func(obs_tf_in.get(),
num_actions,
scope="q_func",
reuse=True,
concat_softmax=True,
noisy=noisy),
sess=U.get_session())
adv_observations = adversary.generate(
obs_tf_in.get(), eps=epsilon, clip_min=0, clip_max=1.0) * 255.0
craft_adv_obs = U.function(
inputs=[obs_tf_in, stochastic_ph_adv, update_eps_ph_adv],
outputs=adv_observations,
givens={
update_eps_ph_adv: -1.0,
stochastic_ph_adv: True
},
updates=[update_eps_expr_adv])
return craft_adv_obs
def build_adv(make_obs_tf, q_func, num_actions, epsilon, noisy, attack=None):
with tf.variable_scope('deepq', reuse=tf.AUTO_REUSE):
obs_tf_in = U.ensure_tf_input(make_obs_tf("observation"))
stochastic_ph_adv = tf.placeholder(tf.bool, (), name="stochastic_adv")
update_eps_ph_adv = tf.placeholder(tf.float32, (),
name="update_eps_adv")
eps = tf.get_variable("eps", (),
initializer=tf.constant_initializer(0))
update_eps_expr_adv = eps.assign(
tf.cond(update_eps_ph_adv >= 0, lambda: update_eps_ph_adv,
lambda: eps))
print("==========================================")
#def wrapper(x):
# return q_func(x, num_actions, scope="q_func", reuse=True, concat_softmax=True, noisy=noisy)
if attack == 'fgsm':
adversary = FastGradientMethod(q_func(obs_tf_in.get(),
num_actions,
scope="q_func",
reuse=True,
concat_softmax=True,
noisy=noisy),
sess=U.get_session())
adv_observations = adversary.generate(
obs_tf_in.get(), eps=epsilon, clip_min=0, clip_max=1.0) * 255.0
print("----")
print(adv_observations.shape)
else:
adversary = CarliniWagnerL2(q_func(obs_tf_in.get(),
num_actions,
scope="q_func",
reuse=True,
concat_softmax=True,
noisy=noisy),
sess=U.get_session())
cw_params = {
'binary_search_steps': 1,
'max_iterations': 100,
'learning_rate': 0.1,
'initial_const': 10,
'clip_min': 0,
'clip_max': 1.0
}
adv_observations = adversary.generate(obs_tf_in.get(), **
cw_params) * 255.0
# saveScreenPNG(b'test_image.png')
craft_adv_obs = U.function(
inputs=[obs_tf_in, stochastic_ph_adv, update_eps_ph_adv],
outputs=adv_observations,
givens={
update_eps_ph_adv: -1.0,
stochastic_ph_adv: True
},
updates=[update_eps_expr_adv])
return craft_adv_obs
def build_act(make_obs_ph, q_func, num_actions, noisy=False, scope="deepq", reuse=None):
"""Creates the act function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a function that take a name and creates a placeholder of input with that name
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions: int
number of actions.
scope: str or VariableScope
optional scope for variable_scope.
reuse: bool or None
whether or not the variables should be reused. To be able to reuse the scope must be given.
Returns
-------
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
` See the top of the file for details.
"""
with tf.variable_scope(scope, reuse=reuse):
observations_ph = U.ensure_tf_input(make_obs_ph("observation"))
stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
q_values = q_func(observations_ph.get(), num_actions, scope="q_func", noisy=noisy)
q_values = q_values.get_logits(observations_ph.get())
deterministic_actions = tf.argmax(q_values, axis=1)
batch_size = tf.shape(observations_ph.get())[0]
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
outputs=output_actions,
givens={update_eps_ph: -1.0, stochastic_ph: True},
updates=[update_eps_expr])
return act
def build_act_enjoy (make_obs_ph, q_func, num_actions, noisy=False, scope="deepq", reuse=None, attack=None, model_path=''):
with tf.variable_scope(scope, reuse=reuse):
observations_ph = U.ensure_tf_input(make_obs_ph("observation"))
stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
q_values = q_func(observations_ph.get(), num_actions, scope="q_func", noisy=noisy)
deterministic_actions = tf.argmax(q_values, axis=1)
batch_size = tf.shape(observations_ph.get())[0]
random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
outputs=output_actions,
givens={update_eps_ph: -1.0, stochastic_ph: True},
updates=[update_eps_expr])
# Load model before attacks graph construction so that TF won't
# complain can't load parameters for attack
try:
U.load_state(model_path)
except:
pass
if attack != None:
if attack == 'fgsm':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True, concat_softmax=True, noisy=noisy)
adversary = FastGradientMethod(CallableModelWrapper(wrapper, 'probs'), sess=U.get_session())
adv_observations = adversary.generate(observations_ph.get(), eps=1.0/255.0,
clip_min=0, clip_max=1.0) * 255.0
elif attack == 'iterative':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True, concat_softmax=True)
adversary = BasicIterativeMethod(CallableModelWrapper(wrapper, 'probs'), sess=U.get_session())
adv_observations = adversary.generate(observations_ph.get(), eps=1.0/255.0,
clip_min=0, clip_max=1.0) * 255.0
elif attack == 'cwl2':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True)
adversary = CarliniWagnerL2(CallableModelWrapper(wrapper, 'logits'), sess=U.get_session())
cw_params = {'binary_search_steps': 1,
'max_iterations': 100,
'learning_rate': 0.1,
'initial_const': 10,
'clip_min': 0,
'clip_max': 1.0}
adv_observations = adversary.generate(observations_ph.get(), **cw_params) * 255.0
craft_adv_obs = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
outputs=adv_observations,
givens={update_eps_ph: -1.0, stochastic_ph: True},
updates=[update_eps_expr])
if attack == None:
craft_adv_obs = None
return act
else:
return act, craft_adv_obs
def build_train(make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0, double_q=True, noisy=False, scope="deepq", reuse=None, attack=None):
"""Creates the train function:
Parameters
----------
make_obs_ph: str -> tf.placeholder or TfInput
a function that takes a name and creates a placeholder of input with that name
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
num_actions: int
number of actions
reuse: bool
whether or not to reuse the graph variables
optimizer: tf.train.Optimizer
optimizer to use for the Q-learning objective.
grad_norm_clipping: float or None
clip gradient norms to this value. If None no clipping is performed.
gamma: float
discount rate.
double_q: bool
if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
In general it is a good idea to keep it enabled.
scope: str or VariableScope
optional scope for variable_scope.
reuse: bool or None
whether or not the variables should be reused. To be able to reuse the scope must be given.
Returns
-------
act: (tf.Variable, bool, float) -> tf.Variable
function to select and action given observation.
` See the top of the file for details.
train: (object, np.array, np.array, object, np.array, np.array) -> np.array
optimize the error in Bellman's equation.
` See the top of the file for details.
update_target: () -> ()
copy the parameters from optimized Q function to the target Q function.
` See the top of the file for details.
debug: {str: function}
a bunch of functions to print debug data like q_values.
"""
act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, noisy=noisy, reuse=reuse)
with tf.variable_scope(scope, reuse=reuse):
# set up placeholders
obs_t_input = U.ensure_tf_input(make_obs_ph("obs_t"))
act_t_ph = tf.placeholder(tf.int32, [None], name="action")
rew_t_ph = tf.placeholder(tf.float32, [None], name="reward")
obs_tp1_input = U.ensure_tf_input(make_obs_ph("obs_tp1"))
done_mask_ph = tf.placeholder(tf.float32, [None], name="done")
importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")
# q network evaluation
q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", noisy=noisy, reuse=True) # reuse parameters from act
q_func_vars = U.scope_vars(U.absolute_scope_name("q_func"))
# target q network evalution
q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func", noisy=noisy)
target_q_func_vars = U.scope_vars(U.absolute_scope_name("target_q_func"))
# q scores for actions which we know were selected in the given state.
q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)
# compute estimate of best possible value starting from state at t + 1
if double_q:
q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", noisy=noisy, reuse=True)
q_tp1_best_using_online_net = tf.arg_max(q_tp1_using_online_net, 1)
q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
else:
q_tp1_best = tf.reduce_max(q_tp1, 1)
q_tp1_best_masked = (1.0 - done_mask_ph) * q_tp1_best
# compute RHS of bellman equation
q_t_selected_target = rew_t_ph + gamma * q_tp1_best_masked
# compute the error (potentially clipped)
td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
errors = U.huber_loss(td_error)
weighted_error = tf.reduce_mean(importance_weights_ph * errors)
# compute optimization op (potentially with gradient clipping)
if grad_norm_clipping is not None:
optimize_expr = U.minimize_and_clip(optimizer,
weighted_error,
var_list=q_func_vars,
clip_val=grad_norm_clipping)
else:
optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)
# update_target_fn will be called periodically to copy Q network to target Q network
update_target_expr = []
for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
sorted(target_q_func_vars, key=lambda v: v.name)):
update_target_expr.append(var_target.assign(var))
update_target_expr = tf.group(*update_target_expr)
# Create callable functions
train = U.function(
inputs=[
obs_t_input,
act_t_ph,
rew_t_ph,
obs_tp1_input,
done_mask_ph,
importance_weights_ph
],
outputs=[td_error, errors],
updates=[optimize_expr]
)
update_target = U.function([], [], updates=[update_target_expr])
q_values = U.function([obs_t_input], q_t)
################## Vahid's Work ###################
#U.load_state(model_path)
if attack != None:
if attack == 'fgsm':
def wrapper(x):
return q_func(x, num_actions, scope="target_q_func", reuse=True, concat_softmax=True, noisy=noisy)
adversary = FastGradientMethod(CallableModelWrapper(wrapper, 'probs'), sess=U.get_session())
adv_observations = adversary.generate(obs_tp1_input.get(), eps=1.0/255.0,
clip_min=0, clip_max=1.0) * 255.0
elif attack == 'iterative':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True, concat_softmax=True)
adversary = BasicIterativeMethod(CallableModelWrapper(wrapper, 'probs'), sess=U.get_session())
adv_observations = adversary.generate(observations_ph.get(), eps=1.0/255.0,
clip_min=0, clip_max=1.0) * 255.0
elif attack == 'cwl2':
def wrapper(x):
return q_func(x, num_actions, scope="q_func", reuse=True)
adversary = CarliniWagnerL2(CallableModelWrapper(wrapper, 'logits'), sess=U.get_session())
cw_params = {'binary_search_steps': 1,
'max_iterations': 100,
'learning_rate': 0.1,
'initial_const': 10,
'clip_min': 0,
'clip_max': 1.0}
adv_observations = adversary.generate(observations_ph.get(), **cw_params) * 255.0
craft_adv_obs = U.function(inputs=[obs_tp1_input],
outputs=adv_observations,
updates=[update_target_expr])
if attack == None:
craft_adv_obs = None
return act_f, train, update_target, {'q_values': q_values}, craft_adv_obs