import chainer
from chainer import cuda, Function, gradient_check,\
    Variable, optimizers, serializers, utils, iterators,\
    Link, Chain, ChainList

import chainer.functions as F
import chainer.links as L
import chainer.initializers as I

#最適な行動価値関数を近似する
class QNet(Chain):
    def __init__(self):
        super(QNet, self).__init__(
                                        _hidden_layers = ChainList(
                                            L.Linear(None, 64),
                                            L.Linear(None, 64),
                                            L.Linear(None, 32),
                                        ),
                                        _output_layer = L.Linear(None, 2)
                                    )

    #def

    def __call__(self, x):
        h = x
        for layer in self._hidden_layers:
            h = F.relu(layer(h))
        #for
        q = self._output_layer(h)
        return q
    #def
#class

c7addac486707b59ea4cac4b0b29aaba5897e102
<mask token>


class QNet(Chain):
    <mask token>
    <mask token>

<mask token>


class QNet(Chain):
    <mask token>

    def __call__(self, x):
        h = x
        for layer in self._hidden_layers:
            h = F.relu(layer(h))
        q = self._output_layer(h)
        return q

<mask token>


class QNet(Chain):

    def __init__(self):
        super(QNet, self).__init__(_hidden_layers=ChainList(L.Linear(None, 
            64), L.Linear(None, 64), L.Linear(None, 32)), _output_layer=L.
            Linear(None, 2))

    def __call__(self, x):
        h = x
        for layer in self._hidden_layers:
            h = F.relu(layer(h))
        q = self._output_layer(h)
        return q

import chainer
from chainer import cuda, Function, gradient_check, Variable, optimizers, serializers, utils, iterators, Link, Chain, ChainList
import chainer.functions as F
import chainer.links as L
import chainer.initializers as I


class QNet(Chain):

    def __init__(self):
        super(QNet, self).__init__(_hidden_layers=ChainList(L.Linear(None, 
            64), L.Linear(None, 64), L.Linear(None, 32)), _output_layer=L.
            Linear(None, 2))

    def __call__(self, x):
        h = x
        for layer in self._hidden_layers:
            h = F.relu(layer(h))
        q = self._output_layer(h)
        return q

import chainer
from chainer import cuda, Function, gradient_check,\
    Variable, optimizers, serializers, utils, iterators,\
    Link, Chain, ChainList

import chainer.functions as F
import chainer.links as L
import chainer.initializers as I

#最適な行動価値関数を近似する
class QNet(Chain):
    def __init__(self):
        super(QNet, self).__init__(
                                        _hidden_layers = ChainList(
                                            L.Linear(None, 64),
                                            L.Linear(None, 64),
                                            L.Linear(None, 32),
                                        ),
                                        _output_layer = L.Linear(None, 2)
                                    )

    #def

    def __call__(self, x):
        h = x
        for layer in self._hidden_layers:
            h = F.relu(layer(h))
        #for
        q = self._output_layer(h)
        return q
    #def
#class
