class_docstr: Policy represented by a MLP.
code: def __init__(
      self, obs_size, action_size, action_low, action_high, n_units=100
  ):
      super(Policy, self).__init__()
      self.action_high = action_high
      self.action_low = action_low
      with self.init_scope():
          self.l0 = L.Linear(obs_size, n_units)
          self.l1 = L.Linear(n_units, n_units)
          self.l2 = L.Linear(
              n_units,
              action_size,
              initialW=chainer.initializers.HeNormal(1e-3),
          )
  
imports: from __future__ import division
  import argparse
  import collections
  import copy
  import random
  import warnings
  import gym
  import numpy
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import optimizers
label_desc: Use image node_id 1 to create a new Policy object from inherited base classes: chainer with example: obj = Policy(obs_size, action_size, action_low, action_high, n_units)