class PEPG:
'''Extension of PEPG with bells and whistles.'''
def __init__(
self,
num_params, # number of model parameters
sigma_init=0.10, # initial standard deviation
sigma_alpha=0.20, # learning rate for std
sigma_decay=0.999, # anneal standard deviation
sigma_limit=0.01, # stop annealing if less than
sigma_max_change=0.2, # clips adaptive sigma to 20%
learning_rate=0.01, # learning rate for std
learning_rate_decay=0.9999, # annealing the learning rate
learning_rate_limit=0.01, # stop annealing learning rate
elite_ratio=0, # if >0 then ignore learning_rate
pop_size=256, # population size
average_baseline=True, # set baseline to average
weight_decay=0.01, # weight decay coefficient
rank_fitness=True, # use rank rather than fitness
forget_best=True): # don't keep the hist best sol
self.num_params = num_params
self.sigma_init = sigma_init
self.sigma_alpha = sigma_alpha
self.sigma_decay = sigma_decay
self.sigma_limit = sigma_limit
self.sigma_max_change = sigma_max_change
self.learning_rate = learning_rate
self.learning_rate_decay = learning_rate_decay
self.learning_rate_limit = learning_rate_limit
self.pop_size = pop_size
self.average_baseline = average_baseline
if self.average_baseline:
assert (self.pop_size % 2 == 0), "Population size must be even"
self.pop_size = int(self.pop_size / 2)
else:
assert (self.pop_size & 1), "Population size must be odd"
self.pop_size = int((self.pop_size - 1) / 2)
# option to use greedy es method to select next mu,
# rather than using drift param
self.elite_ratio = elite_ratio
self.elite_pop_size = int(self.pop_size * self.elite_ratio)
self.use_elite = False
if self.elite_pop_size > 0:
self.use_elite = True
self.forget_best = forget_best
self.batch_reward = np.zeros(self.pop_size * 2)
self.mu = np.zeros(self.num_params)
self.sigma = np.ones(self.num_params) * self.sigma_init
self.curr_best_mu = np.zeros(self.num_params)
self.best_mu = np.zeros(self.num_params)
self.best_reward = 0
self.first_interation = True
self.weight_decay = weight_decay
self.rank_fitness = rank_fitness
if self.rank_fitness:
self.forget_best = True # always forget the best one if we rank
# choose optimizer
self.optimizer = Adam(self, learning_rate)
def rms_stdev(self):
sigma = self.sigma
return np.mean(np.sqrt(sigma * sigma))
def ask(self):
'''returns a list of parameters'''
# antithetic sampling
self.epsilon = np.random.randn(self.pop_size, self.num_params)
self.epsilon *= self.sigma.reshape(1, self.num_params)
self.epsilon_full = np.concatenate([self.epsilon, -self.epsilon])
if self.average_baseline:
epsilon = self.epsilon_full
else:
# first population is mu, then positive epsilon,
# then negative epsilon
epsilon = np.concatenate(
[np.zeros((1, self.num_params)), self.epsilon_full])
solutions = self.mu.reshape(1, self.num_params) + epsilon
self.solutions = solutions
return solutions
def tell(self, scores):
# input must be a numpy float array
assert (len(scores) == self.pop_size
), "Inconsistent reward_table size reported."
reward_table = np.array(scores)
if self.rank_fitness:
reward_table = compute_centered_ranks(reward_table)
if self.weight_decay > 0:
l2_decay = compute_weight_decay(self.weight_decay, self.solutions)
reward_table += l2_decay
reward_offset = 1
if self.average_baseline:
b = np.mean(reward_table)
reward_offset = 0
else:
b = reward_table[0] # baseline
reward = reward_table[reward_offset:]
if self.use_elite:
idx = np.argsort(reward)[::-1][0:self.elite_pop_size]
else:
idx = np.argsort(reward)[::-1]
best_reward = reward[idx[0]]
if (best_reward > b or self.average_baseline):
best_mu = self.mu + self.epsilon_full[idx[0]]
best_reward = reward[idx[0]]
else:
best_mu = self.mu
best_reward = b
self.curr_best_reward = best_reward
self.curr_best_mu = best_mu
if self.first_interation:
self.sigma = np.ones(self.num_params) * self.sigma_init
self.first_interation = False
self.best_reward = self.curr_best_reward
self.best_mu = best_mu
else:
if self.forget_best or (self.curr_best_reward > self.best_reward):
self.best_mu = best_mu
self.best_reward = self.curr_best_reward
# short hand
epsilon = self.epsilon
sigma = self.sigma
# update the mean
# move mean to the average of the best idx means
if self.use_elite:
self.mu += self.epsilon_full[idx].mean(axis=0)
else:
rT = (reward[:self.pop_size] - reward[self.pop_size:])
change_mu = np.dot(rT, epsilon)
self.optimizer.stepsize = self.learning_rate
# adam, rmsprop, momentum, etc.
update_ratio = self.optimizer.update(-change_mu)
# self.mu += (change_mu * self.learning_rate) # normal SGD method
# adaptive sigma
# normalization
if (self.sigma_alpha > 0):
stdev_reward = 1.0
if not self.rank_fitness:
stdev_reward = reward.std()
S = epsilon * epsilon - (sigma * sigma).reshape(1, self.num_params)
S /= sigma.reshape(1, self.num_params)
reward_avg = (reward[:self.pop_size] +
reward[self.pop_size:]) / 2.0
rS = reward_avg - b
delta_sigma = (np.dot(rS, S)) / \
(2 * self.pop_size * stdev_reward)
# adjust sigma according to the adaptive sigma calculation
# for stability, don't let sigma move more than 10% of orig value
change_sigma = self.sigma_alpha * delta_sigma
change_sigma = np.minimum(change_sigma,
self.sigma_max_change * self.sigma)
change_sigma = np.maximum(change_sigma,
-self.sigma_max_change * self.sigma)
self.sigma += change_sigma
if (self.sigma_decay < 1):
self.sigma[self.sigma > self.sigma_limit] *= self.sigma_decay
if (self.learning_rate_decay < 1
and self.learning_rate > self.learning_rate_limit):
self.learning_rate *= self.learning_rate_decay
def current_param(self):
return self.curr_best_mu
def set_mu(self, mu):
self.mu = np.array(mu)
def best_param(self):
return self.best_mu
def result(self):
# return best params so far, along with historically
# best reward, curr reward, sigma
return (self.best_mu, self.best_reward, self.curr_best_reward,
self.sigma)
class OpenES:
"""
Basic Version of OpenAI Evolution Strategies
"""
def __init__(
self,
num_params, # number of model parameters
mu_init=None, # initial mean
sigma_init=1, # initial standard deviation
sigma_decay=0.999, # anneal standard deviation
sigma_limit=0.01, # stop annealing if less than
learning_rate=0.01, # learning rate for std
learning_rate_decay=0.9999, # annealing the learning rate
learning_rate_limit=0.001, # stop annealing learning rate
pop_size=256, # population size
antithetic=False, # whether to use anti sampling
weight_decay=0.01, # weight decay coefficient
rank_fitness=True, # use rank rather than fitness
forget_best=True): # forget historical best
# misc
self.num_params = num_params
self.first_interation = True
# distribution parameters
if mu_init is None:
self.mu = np.zeros(self.num_params)
else:
self.mu = np.array(mu_init)
self.sigma_decay = sigma_decay
self.sigma = sigma_init
self.sigma_init = sigma_init
self.sigma_limit = sigma_limit
# optimizarion stuff
self.learning_rate = learning_rate
self.learning_rate_decay = learning_rate_decay
self.learning_rate_limit = learning_rate_limit
self.optimizer = Adam(self, learning_rate)
# sampling stuff
self.pop_size = pop_size
self.antithetic = antithetic
if self.antithetic:
assert (self.pop_size % 2 == 0), "Population size must be even"
self.forget_best = forget_best
self.weight_decay = weight_decay
self.rank_fitness = rank_fitness
if self.rank_fitness:
self.forget_best = True
def ask(self, pop_size):
"""
Returns a list of candidates parameterss
"""
if self.antithetic:
epsilon_half = np.random.randn(self.pop_size // 2, self.num_params)
epsilon = np.concatenate([epsilon_half, -epsilon_half])
else:
epsilon = np.random.randn(pop_size, self.num_params)
return self.mu.reshape(1, self.num_params) + epsilon * self.sigma
def tell(self, solutions, scores):
"""
Updates the distribution
"""
assert (len(scores) == self.pop_size
), "Inconsistent reward_table size reported."
reward = np.array(scores)
if self.rank_fitness:
reward = compute_centered_ranks(reward)
if self.weight_decay > 0:
l2_decay = compute_weight_decay(self.weight_decay, solutions)
reward += l2_decay
# TBD check if ok
epsilon = (solutions -
self.mu.reshape(1, self.num_params)) / self.sigma
# standardize the rewards to have a gaussian distribution
normalized_reward = (reward - np.mean(reward)) / np.std(reward)
change_mu = 1. / (self.pop_size * self.sigma) * \
np.dot(epsilon.T, normalized_reward)
# updating stuff
idx = np.argsort(reward)[::-1]
best_reward = reward[idx[0]]
best_mu = solutions[idx[0]]
self.curr_best_reward = best_reward
self.curr_best_mu = best_mu
if self.first_interation:
self.first_interation = False
self.best_reward = self.curr_best_reward
self.best_mu = best_mu
else:
if self.forget_best or (self.curr_best_reward > self.best_reward):
self.best_mu = best_mu
self.best_reward = self.curr_best_reward
# optimization step
self.optimizer.stepsize = self.learning_rate
self.optimizer.update(-change_mu)
# adjust sigma according to the adaptive sigma calculation
if (self.sigma > self.sigma_limit):
self.sigma *= self.sigma_decay
if (self.learning_rate > self.learning_rate_limit):
self.learning_rate *= self.learning_rate_decay
def get_distrib_params(self):
"""
Returns the parameters of the distrubtion:
the mean and sigma
"""
return self.mu, self.sigma
def result(self):
"""
Returns best params so far, best score, current score
and sigma
"""
return (self.best_mu, self.best_reward, self.curr_best_reward,
self.sigma)
def rms_stdev(self):
sigma = self.sigma
return np.mean(np.sqrt(sigma * sigma))
class Parameters:
def __init__(self, population_size=1, sigma=0, alpha=0, filename=''):
self.population_size = population_size
self.sigma = sigma
self.alpha = alpha
self.optimizer = Adam()
if filename:
npz = np.load(filename)
self.F1 = Param(npz['arr_0'], population_size, sigma)
self.F2 = Param(npz['arr_1'], population_size, sigma)
self.F3 = Param(npz['arr_2'], population_size, sigma)
self.F4 = Param(npz['arr_3'], population_size, sigma)
self.F5 = Param(npz['arr_4'], population_size, sigma)
self.F6 = Param(npz['arr_5'], population_size, sigma)
self.g3 = Param(npz['arr_6'], population_size, sigma)
self.b3 = Param(npz['arr_7'], population_size, sigma)
self.g4 = Param(npz['arr_8'], population_size, sigma)
self.b4 = Param(npz['arr_9'], population_size, sigma)
self.g5 = Param(npz['arr_10'], population_size, sigma)
self.b5 = Param(npz['arr_11'], population_size, sigma)
self.g6 = Param(npz['arr_12'], population_size, sigma)
self.b6 = Param(npz['arr_13'], population_size, sigma)
self.Wx0 = Param(npz['arr_14'], population_size, sigma)
self.bx0 = Param(npz['arr_15'], population_size, sigma)
self.Wx1 = Param(npz['arr_16'], population_size, sigma)
self.bx1 = Param(npz['arr_17'], population_size, sigma)
self.Wx2 = Param(npz['arr_18'], population_size, sigma)
self.bx2 = Param(npz['arr_19'], population_size, sigma)
self.Wv = Param(npz['arr_20'], population_size, sigma)
self.bv = Param(npz['arr_21'], population_size, sigma)
self.lg0 = Param(npz['arr_22'], population_size, sigma)
self.lb0 = Param(npz['arr_23'], population_size, sigma)
self.lg1 = Param(npz['arr_24'], population_size, sigma)
self.lb1 = Param(npz['arr_25'], population_size, sigma)
self.lg2 = Param(npz['arr_26'], population_size, sigma)
self.lb2 = Param(npz['arr_27'], population_size, sigma)
else:
# filter weight is whdo
# w = width
# h = height
# d = depth (in channels)
# o = out depth (out channels)?
self.F1 = Param(
tf.random.normal([F_size, F_size, 3, NF1_out],
stddev=m.sqrt(2 / F_size)), population_size,
sigma)
self.F2 = Param(
tf.random.normal([F_size, F_size, NF1_out, NF2_out],
stddev=m.sqrt(2 / F_size)), population_size,
sigma)
self.F3 = Param(
tf.random.normal([F_size, F_size, NF2_out, NF3_out],
stddev=m.sqrt(2 / F_size)), population_size,
sigma)
self.g3 = Param(tf.ones((NF3_out, 1)), population_size, sigma)
self.b3 = Param(tf.zeros((NF3_out, 1)), population_size, sigma)
self.F4 = Param(
tf.random.normal([F_size, F_size, NF3_out, NF4_out],
stddev=m.sqrt(2 / F_size)), population_size,
sigma)
self.g4 = Param(tf.ones((NF4_out, 1)), population_size, sigma)
self.b4 = Param(tf.zeros((NF4_out, 1)), population_size, sigma)
self.F5 = Param(
tf.random.normal([F_size, F_size, NF4_out, NF5_out],
stddev=m.sqrt(2 / F_size)), population_size,
sigma)
self.g5 = Param(tf.ones((NF5_out, 1)), population_size, sigma)
self.b5 = Param(tf.zeros((NF5_out, 1)), population_size, sigma)
self.F6 = Param(
tf.random.normal([F_size, F_size, NF5_out, NF6_out],
stddev=m.sqrt(2 / F_size)), population_size,
sigma)
self.g6 = Param(tf.ones((NF6_out, 1)), population_size, sigma)
self.b6 = Param(tf.zeros((NF6_out, 1)), population_size, sigma)
self.lg0 = Param(tf.ones((H_size, 1)), population_size, sigma)
self.lb0 = Param(tf.zeros((H_size, 1)), population_size, sigma)
self.lg1 = Param(tf.ones((H_size, 1)), population_size, sigma)
self.lb1 = Param(tf.zeros((H_size, 1)), population_size, sigma)
self.lg2 = Param(tf.ones((H_size, 1)), population_size, sigma)
self.lb2 = Param(tf.zeros((H_size, 1)), population_size, sigma)
self.Wx0 = Param(tf.random.normal([H_size * 4, z_size]),
population_size, sigma)
self.bx0 = Param(tf.zeros([H_size * 4, 1]), population_size, sigma)
self.Wx1 = Param(tf.random.normal([H_size * 4, H_size * 2]),
population_size, sigma)
self.bx1 = Param(tf.zeros([H_size * 4, 1]), population_size, sigma)
self.Wx2 = Param(tf.random.normal([H_size * 4, H_size * 2]),
population_size, sigma)
self.bx2 = Param(tf.zeros([H_size * 4, 1]), population_size, sigma)
self.Wv = Param(tf.random.normal([Y_size, H_size]),
population_size, sigma)
self.bv = Param(tf.zeros([Y_size, 1]), population_size, sigma)
def all(self):
return [self.F1, self.F2, self.F3, self.F4, self.F5, self.F6,\
self.g3, self.b3, self.g4, self.b4, self.g5, self.b5, self.g6, self.b6,\
self.Wx0, self.bx0, self.Wx1, self.bx1, self.Wx2, self.bx2,\
self.Wv, self.bv,\
self.lg0,self.lb0,self.lg1,self.lb1,self.lg2,self.lb2]
# return reference to current tensors
def current(self):
return [param.current for param in self.all()]
def set_current_population_member(self, i):
for param in self.all():
param.set_current_population_member(i)
def update_nes(self, reward, reward_mean, reward_std):
reward = (reward - reward_mean) / (reward_std + .00001)
grads = []
means = []
for param in self.all():
grads += [
param.get_grad(reward) * (self.alpha /
(self.population_size * self.sigma))
]
means += [param.mean]
self.optimizer.update(means, grads)
for param in self.all():
param.gen_pop_about_mean(self.sigma)
def mutate(self, param, i):
x = param.population[i]
if random.randint(1, 4) == 1:
jitter = tf.random.normal(x.shape, stddev=self.sigma)
return x + jitter
else:
return x
def mate(self, param, i, j):
if random.randint(1, 4) == 1:
return self.mutate(param, i)
else:
return self.mutate(param, j)
def update_ga(self, rewards):
# sort parameters by rewards
top_reward_indices = rewards.argsort()[-PASS_THROUGH:]
top_reward_indices = top_reward_indices[::-1]
for param in self.all():
# sort population
for i, j in enumerate(top_reward_indices):
param.population[i] = param.population[j]
# generate new population
for k in range(PASS_THROUGH, self.population_size):
param.population[k] = self.mate(param, random.randint(0, 9),
random.randint(0, 9))