def __init__(self,
environment,
policy,
max_it=1000,
num_workers=1,
rate=0.1,
discount=0.1,
log_file=False):
"""Initialize A3C.
Parameters
----------
environment:
Environment we want to optimize the policy on.
policy:
The policy we want to optimize. The policy needs be defined by a
tensorflow neural network and define certain attributes.
max_it: int
Maximum number of iterations.
num_workers: int
Number of workers.
rate: float
Update rate passed to the optimizer.
discount: float
Discount for the computation of the discounted reward.
log_file: string
Indicate the relative path to directory where a summary event file
should be generated. If `None` no tensorflow logs will be stored.
"""
add_dependency(tf, 'TensorFlow')
if policy.is_set_up:
raise (ValueError('Policy should not be set up.'))
super(A3C, self).__init__(environment, policy, max_it)
self.num_workers = num_workers
self.rate = rate
self.discount = discount
self.done = False
self.log_file = log_file
self.policy = policy
# init networks
with tf.device("/cpu:0"):
with tf.variable_scope('global'):
self.p_net = _PolicyNet(self.policy, rate)
self.v_net = _ValueNet(self.policy, rate)
self.workers = []
self.threads = []
self.global_counter = 0
self.sess = None
self.coord = None
def __init__(self, env, horizon=100, render=False):
"""Initialize attributes.
Parameters
----------
env : gym environment
Instance of the gym environment that should be optimized on.
horizon : integer
Horizon for rollout.
render : boolean
Default: False ; If True simulation will be rendered during
rollouts on this instance.
"""
add_dependency(gym, 'Gym')
EnvironmentBase.__init__(self, env.observation_space, env.action_space,
horizon)
self.environment = env.unwrapped
self.render = render
self.done = False
self.environment.reset()
def __init__(self,
layers,
weights=None,
init_weights=None,
activation=None,
dtype='float',
scope='global',
do_setup=False):
"""Initialize Neural Network wrapper."""
add_dependency(tf, 'TensorFlow')
if (len(layers) < 2):
raise ValueError('At least two layers needed.')
# determine state and action space
state_space = RdSpace((layers[0], ))
action_space = RdSpace((layers[-1], ))
# store arguments convenient for copy operation
self.args = [layers]
self.kwargs = {
'weights': weights,
'init_weights': init_weights,
'activation': activation,
'dtype': dtype
}
self.state_space = state_space
self.action_space = action_space
self.dtype = dtype
self.layers = layers
self.scope = scope
self.is_set_up = False
if init_weights is None:
self.init_weights = default_init_weights
else:
self.init_weights = init_weights
# Activation function
if activation is None:
activation = (len(layers) - 2) * [tf.sigmoid]
elif (isinstance(activation, list)
and (len(activation) != len(layers) - 2)):
raise ValueError('Activation list has wrong size.')
else:
activation = (len(layers) - 2) * [activation]
self.activation = activation
# Symbols
self.X = tf.placeholder(dtype, shape=[None, layers[0]], name='X')
self.a = tf.placeholder(dtype, shape=[None, layers[-1]], name='a')
if do_setup:
with tf.variable_scope(self.scope):
self.setup()
else:
# Make sure all fields exist
self.W_action = None
self.W_var = None
self.a_pred = None
self.var = None
self.h = None
self.sess = None
def __init__(self,
environment, policy, max_it, avg_reward, window,
kernel, likelihood, fmin, bounds, beta=3.0, threshold=0,
scaling='auto', swarm_size=20, info=None):
"""Initialize Attributes.
Parameters
----------
environment :
Environment to be optimized.
policy :
policy to be optimized.
max_it :
maximal number of iterations before we abort.
avg_reward : integer
average reward at which the optimization will be finished.
window : integer
window for the average reward
kernel : GPy kernel
Kernel used to initialize the gaussian process. If this is a list
multiple kernels will be initialized. The size of this argument
has to agree with the size of the likelihood.
likelihood : GPy likelihood
Likelihood used to initialize kernels. If this is a list, multiple
kernels will be initialized. The size of this argument has to
agree with the size of the likelihood.
fmin : list of floats
Safety threshold for the function value. If multiple safety
constraints are used this can also be a list of floats (the first
one is always the one for the values, can be set to None if not
wanted)
bounds : pair of floats or list of pairs of floats
If a list is given, then each pair represents the lower/upper bound
in each dimension. Otherwise, we assume the same bounds for all
dimensions. This is mostly important for plotting or to restrict
particles to a certain domain.
beta : float or callable
A constant or a function of the time step that scales the
confidence interval of the acquisition function.
threshold : float or list of floats
The algorithm will not try to expand any points that are below this
threshold. This makes the algorithm stop expanding points
eventually. If a list, this represents the stopping criterion for
all the gps. This ignores the scaling factor.
scaling : list of floats or "auto"
A list used to scale the GP uncertainties to compensate for
different input sizes. This should be set to the maximal variance
of each kernel. You should probably set this to "auto" unless your
kernel is non-stationary
swarm_size : int
The number of particles in each of the optimization swarms
info :
Dummy argument that can hold anything usable to identify the
configuration.
"""
add_dependency(safeopt, 'SafeOpt')
add_dependency(GPy, 'GPy')
# store the `SafeOpt` arguments.
gp_opt_par = {
'fmin': fmin,
'bounds': bounds,
'beta': beta,
'threshold': threshold,
'scaling': scaling,
'swarm_size': swarm_size
}
# store the kernel arguments
if not isinstance(kernel, list):
kernel = [kernel]
if not isinstance(likelihood, list):
likelihood = [likelihood]
assert len(likelihood) == len(kernel), (
'kernel and likelihood need to have same length (%d /= %d)'
% (len(likelihood), len(kernel)))
gp_par = (kernel, likelihood)
super(SafeOptSwarm, self).__init__(safeopt.SafeOptSwarm, gp_opt_par,
gp_par, environment, policy, max_it,
avg_reward, window)
def __init__(self,
environment, policy, max_it, avg_reward, window,
kernel, likelihood, parameter_set, fmin,
lipschitz=None, beta=3.0, num_contexts=0, threshold=0,
scaling='auto', info=None):
"""Initialize Attributes.
Parameters
----------
environment :
environmet to be optimized.
policy :
policy to be optimized.
max_it :
maximal number of iterations before we abort.
avg_reward : integer
average reward at which the optimization will be finished.
window : integer
window for the average reward
kernel : GPy kernel
Kernel used to initialize the gaussian process. If this is a list
multiple kernels will be initialized. The size of this argument
has to agree with the size of the likelihood.
likelihood : GPy likelihood
Likelihood used to initialize kernels. If this is a list, multiple
kernels will be initialized. The size of this argument has to
agree with the size of the likelihood.
parameter_set : 2d-array
List of parameters
fmin : list of floats
Safety threshold for the function value. If multiple safety
constraints are used this can also be a list of floats (the first
one is always the one for the values, can be set to None if not
wanted)
lipschitz : list of floats
The Lipschitz constant of the system, if None the GP confidence
intervals are used directly.
beta : float or callable
A constant or a function of the time step that scales the
confidence interval of the acquisition function.
threshold : float or list of floats
The algorithm will not try to expand any points that are below this
threshold. This makes the algorithm stop expanding points
eventually. If a list, this represents the stopping criterion for
all the gps. This ignores the scaling factor.
scaling : list of floats or "auto"
A list used to scale the GP uncertainties to compensate for
different input sizes. This should be set to the maximal variance
of each kernel. You should probably leave this to "auto" unless
your kernel is non-stationary.
info :
Dummy argument that can hold anything usable to identify the
configuration.
"""
add_dependency(safeopt, 'SafeOpt')
add_dependency(GPy, 'GPy')
# store the `SafeOpt` arguments.
gp_opt_par = {
'parameter_set': parameter_set,
'fmin': fmin,
'lipschitz': lipschitz,
'beta': beta,
'num_contexts': num_contexts,
'threshold': threshold,
'scaling': scaling}
# store the kernel arguments
if not isinstance(kernel, list):
kernel = [kernel]
if not isinstance(likelihood, list):
likelihood = [likelihood]
assert len(likelihood) == len(kernel), (
'kernel and likelihood need to have same length (%d /= %d)'
% (len(likelihood), len(kernel)))
gp_par = (kernel, likelihood)
super(SafeOpt, self).__init__(safeopt.SafeOpt, gp_opt_par, gp_par,
environment, policy, max_it, avg_reward,
window)
