ES_classes.py

import numpy as np


def compute_ranks(x):
    """
    Returns ranks in [0, len(x))
    Note: This is different from scipy.stats.rankdata, which returns ranks in [1, len(x)].
    (https://github.com/openai/evolution-strategies-starter/blob/master/es_distributed/es.py)
    """
    assert x.ndim == 1
    ranks = np.empty(len(x), dtype=int)
    ranks[x.argsort()] = np.arange(len(x))
    return ranks


def compute_centered_ranks(x):
    """
    https://github.com/openai/evolution-strategies-starter/blob/master/es_distributed/es.py
    """
    y = compute_ranks(x.ravel()).reshape(x.shape).astype(np.float32)
    y /= (x.size - 1)
    y -= .5
    return y


def compute_weight_decay(weight_decay, model_param_list):
    model_param_grid = np.array(model_param_list)
    return - weight_decay * np.mean(model_param_grid * model_param_grid, axis=1)


# adopted from:
# https://github.com/openai/evolution-strategies-starter/blob/master/es_distributed/optimizers.py

class Optimizer(object):
    def __init__(self, pi, epsilon=1e-08):
        self.pi = pi
        self.dim = pi.num_params
        self.epsilon = epsilon
        self.t = 0

    def update(self, globalg):
        self.t += 1
        step = self._compute_step(globalg)
        theta = self.pi.mu
        ratio = np.linalg.norm(step) / (np.linalg.norm(theta) + self.epsilon)
        self.pi.mu = theta + step
        return ratio

    def _compute_step(self, globalg):
        raise NotImplementedError


class BasicSGD(Optimizer):
    def __init__(self, pi, stepsize):
        Optimizer.__init__(self, pi)
        self.stepsize = stepsize

    def _compute_step(self, globalg):
        step = -self.stepsize * globalg
        return step


class SGD(Optimizer):
    def __init__(self, pi, stepsize, momentum=0.9):
        Optimizer.__init__(self, pi)
        self.v = np.zeros(self.dim, dtype=np.float32)
        self.stepsize, self.momentum = stepsize, momentum

    def _compute_step(self, globalg):
        self.v = self.momentum * self.v + (1. - self.momentum) * globalg
        step = -self.stepsize * self.v
        return step


class Adam(Optimizer):
    def __init__(self, pi, stepsize, beta1=0.99, beta2=0.999):
        Optimizer.__init__(self, pi)
        self.stepsize = stepsize
        self.beta1 = beta1
        self.beta2 = beta2
        self.m = np.zeros(self.dim, dtype=np.float32)
        self.v = np.zeros(self.dim, dtype=np.float32)

    def _compute_step(self, globalg):
        a = self.stepsize * np.sqrt(1 - self.beta2 ** self.t) / (1 - self.beta1 ** self.t)
        self.m = self.beta1 * self.m + (1 - self.beta1) * globalg
        self.v = self.beta2 * self.v + (1 - self.beta2) * (globalg * globalg)
        step = -a * self.m / (np.sqrt(self.v) + self.epsilon)
        return step


class CMAES:
    '''CMA-ES wrapper.'''

    def __init__(self, coeffs,  # number of model parameters
                 sigma_init=0.10,  # initial standard deviation
                 popsize=255,  # population size
                 weight_decay=0.0):  # weight decay coefficient
        self.coeffs = coeffs
        # self.num_params = num_params
        self.sigma_init = sigma_init
        self.popsize = popsize
        self.weight_decay = weight_decay
        self.solutions = None

        import cma
        self.es = cma.CMAEvolutionStrategy(self.coeffs,
                                           self.sigma_init,
                                           {'popsize': self.popsize,
                                            })

    def rms_stdev(self):
        sigma = self.es.result[6]
        return np.mean(np.sqrt(sigma * sigma))

    def ask(self):
        '''returns a list of parameters'''
        self.solutions = np.array(self.es.ask())
        return self.solutions

    def tell(self, reward_table_result):
        reward_table = -np.array(reward_table_result)
        if self.weight_decay > 0:
          l2_decay = compute_weight_decay(self.weight_decay, self.solutions)
          reward_table += l2_decay
        self.es.tell(self.solutions, (reward_table).tolist())  # convert minimizer to maximizer.

    def current_param(self):
        return self.es.result[5]  # mean solution, presumably better with noise

    def set_mu(self, mu):
        pass

    def best_param(self):
        return self.es.result[0]  # best evaluated solution

    def result(self):  # return best params so far, along with historically best reward, curr reward, sigma
        r = self.es.result
        return (r[0], -r[1], -r[1], r[6])



class OpenES:
    ''' Basic Version of OpenAI Evolution Strategies.'''

    def __init__(self, num_params,  # number of model parameters
                 sigma_init=0.1,  # initial standard deviation
                 sigma_decay=0.999,  # anneal standard deviation
                 sigma_limit=0.01,  # stop annealing if less than this
                 learning_rate=0.01,  # learning rate for standard deviation
                 learning_rate_decay=0.9999,  # annealing the learning rate
                 learning_rate_limit=0.001,  # stop annealing learning rate
                 popsize=256,  # population size
                 antithetic=True,  # whether to use antithetic sampling
                 weight_decay=0.00,  # weight decay coefficient
                 rank_fitness=True,  # use rank rather than fitness numbers
                 forget_best=True):  # forget historical best

        self.num_params = num_params
        self.sigma_decay = sigma_decay
        self.sigma = sigma_init
        self.sigma_init = sigma_init
        self.sigma_limit = sigma_limit
        self.learning_rate = learning_rate
        self.learning_rate_decay = learning_rate_decay
        self.learning_rate_limit = learning_rate_limit
        self.popsize = popsize
        self.antithetic = antithetic
        if self.antithetic:
            assert (self.popsize % 2 == 0), "Population size must be even"
            self.half_popsize = int(self.popsize / 2)

        self.reward = np.zeros(self.popsize)
        self.mu = np.zeros(self.num_params)
        self.best_mu = np.zeros(self.num_params)
        self.best_reward = 0
        self.first_interation = True
        self.forget_best = forget_best
        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
        if self.antithetic:
            self.epsilon_half = np.random.randn(self.half_popsize, self.num_params)
            self.epsilon = np.concatenate([self.epsilon_half, - self.epsilon_half])
        else:
            self.epsilon = np.random.randn(self.popsize, self.num_params)

        self.solutions = self.mu.reshape(1, self.num_params) + self.epsilon * self.sigma

        return self.solutions

    def tell(self, reward_table_result):
        # input must be a numpy float array
        assert (len(reward_table_result) == self.popsize), "Inconsistent reward_table size reported."
        self.reward = np.array(reward_table_result)
        reward = np.array(reward_table_result)

        if self.rank_fitness:
            reward = compute_centered_ranks(reward)

        if self.weight_decay > 0:
            l2_decay = compute_weight_decay(self.weight_decay, self.solutions)
            reward += l2_decay

        idx = np.argsort(reward)[::-1]

        best_reward = self.reward[idx[0]]
        best_mu = self.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

        # main bit:
        # standardize the rewards to have a gaussian distribution

        std = np.std(reward)

        normalized_reward = (reward - np.mean(reward)) / std  # np.std(reward)

        change_mu = 1. / (self.popsize * self.sigma) * np.dot(self.epsilon.T, normalized_reward)

        # self.mu += self.learning_rate * change_mu

        self.optimizer.stepsize = self.learning_rate
        update_ratio = 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 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 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 standard deviation
                 sigma_decay=0.999,  # anneal standard deviation
                 sigma_limit=0.01,  # stop annealing if less than this
                 sigma_max_change=0.2,  # clips adaptive sigma to 20%
                 learning_rate=0.01,  # learning rate for standard deviation
                 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
                 popsize=256,  # population size
                 average_baseline=True,  # set baseline to average of batch
                 weight_decay=0.01,  # weight decay coefficient
                 rank_fitness=True,  # use rank rather than fitness numbers
                 forget_best=True):  # don't keep the historical best solution

        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.popsize = popsize
        self.average_baseline = average_baseline
        if self.average_baseline:
            assert (self.popsize % 2 == 0), "Population size must be even"
            self.batch_size = int(self.popsize / 2)
        else:
            assert (self.popsize & 1), "Population size must be odd"
            self.batch_size = int((self.popsize - 1) / 2)

        # option to use greedy es method to select next mu, rather than using drift param
        self.elite_ratio = elite_ratio
        self.elite_popsize = int(self.popsize * self.elite_ratio)
        self.use_elite = False
        if self.elite_popsize > 0:
            self.use_elite = True

        self.forget_best = forget_best
        self.batch_reward = np.zeros(self.batch_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.batch_size, self.num_params) * 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, reward_table_result):
        # input must be a numpy float array
        assert (len(reward_table_result) == self.popsize), "Inconsistent reward_table size reported."

        reward_table = np.array(reward_table_result)

        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_popsize]
        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.batch_size] - reward[self.batch_size:])
            change_mu = np.dot(rT, epsilon)
            self.optimizer.stepsize = self.learning_rate
            update_ratio = self.optimizer.update(-change_mu)  # adam, rmsprop, momentum, etc.
            # 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)) / sigma.reshape(1, self.num_params))
            reward_avg = (reward[:self.batch_size] + reward[self.batch_size:]) / 2.0
            rS = reward_avg - b
            delta_sigma = (np.dot(rS, S)) / (2 * self.batch_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)