<gh_stars>1000+
__author__ = "<NAME>"

import numpy as np

from theano import config
from theano import function
from theano import tensor as T

from pylearn2.sandbox.lisa_rl.bandit.environment import Environment
from pylearn2.utils import sharedX
from pylearn2.utils.rng import make_np_rng, make_theano_rng


class GaussianBandit(Environment):
    """
    An n-armed bandit whose rewards are drawn from a different Gaussian
    distribution for each arm.
    The mean and standard deviation of the reward for each arm is drawn
    at initialization time from N(0, <corresponding std arg>).
    (For the standard deviation we use the absolute value of the Gaussian
    sample)

    .. todo::

        WRITEME : parameter list
    """

    def __init__(self, num_arms, mean_std = 1.0, std_std = 1.0):
        self.rng = make_np_rng(None, [2013, 11, 12], which_method="randn")
        self.means = sharedX(self.rng.randn(num_arms) * mean_std)
        self.stds = sharedX(np.abs(self.rng.randn(num_arms) * std_std))
        self.theano_rng = make_theano_rng(None, self.rng.randint(2 ** 16), which_method="normal")

    def get_action_func(self):
        """
        Returns a theano function that takes an action and returns a reward.
        """

        action = T.iscalar()
        reward_mean = self.means[action]
        reward_std = self.stds[action]
        reward = self.theano_rng.normal(avg=reward_mean, std=reward_std,
                dtype=config.floatX, size=reward_mean.shape)
        rval = function([action], reward)
        return rval

__author__ = "<NAME>"

import numpy as np

from theano import config
from theano import function
from theano import tensor as T

from pylearn2.sandbox.lisa_rl.bandit.environment import Environment
from pylearn2.utils import sharedX
from pylearn2.utils.rng import make_np_rng, make_theano_rng


class GaussianBandit(Environment):
    """
    An n-armed bandit whose rewards are drawn from a different Gaussian
    distribution for each arm.
    The mean and standard deviation of the reward for each arm is drawn
    at initialization time from N(0, <corresponding std arg>).
    (For the standard deviation we use the absolute value of the Gaussian
    sample)

    .. todo::

        WRITEME : parameter list
    """

    def __init__(self, num_arms, mean_std = 1.0, std_std = 1.0):
        self.rng = make_np_rng(None, [2013, 11, 12], which_method="randn")
        self.means = sharedX(self.rng.randn(num_arms) * mean_std)
        self.stds = sharedX(np.abs(self.rng.randn(num_arms) * std_std))
        self.theano_rng = make_theano_rng(None, self.rng.randint(2 ** 16), which_method="normal")

    def get_action_func(self):
        """
        Returns a theano function that takes an action and returns a reward.
        """

        action = T.iscalar()
        reward_mean = self.means[action]
        reward_std = self.stds[action]
        reward = self.theano_rng.normal(avg=reward_mean, std=reward_std,
                dtype=config.floatX, size=reward_mean.shape)
        rval = function([action], reward)
        return rval
An n-armed bandit whose rewards are drawn from a different Gaussian     distribution for each arm.     The mean and standard deviation of the reward for each arm is drawn     at initialization time from N(0, <corresponding std arg>).     (For the standard deviation we use the absolute value of the Gaussian     sample)      .. todo::          WRITEME : parameter list Returns a theano function that takes an action and returns a reward.