class_docstr: A REINFORCE Agent.
  
  Implements:
  
  REINFORCE algorithm from
  
  "Simple statistical gradient-following algorithms for connectionist
  reinforcement learning"
  Williams, R.J., 1992.
  https://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf
  
  REINFORCE with state-value baseline, where state-values are estimated with
  function approximation, from
  
  "Reinforcement learning: An introduction" (Sec. 13.4)
  Sutton, R.S. and Barto, A.G., 2018.
  http://incompleteideas.net/book/the-book-2nd.html
  
  The REINFORCE agent can be optionally provided with:
  - value_network: A `tf_agents.network.Network` which parameterizes state-value
    estimation as a neural network. The network will be called with
    call(observation, step_type) and returns a floating point state-values
    tensor.
  - value_estimation_loss_coef: Weight on the value prediction loss.
  
  If value_network and value_estimation_loss_coef are provided, advantages are
  computed as
    `advantages = (discounted accumulated rewards) - (estimated state-values)`
  and the overall learning objective becomes:
    `(total loss) =
      (policy gradient loss) +
      value_estimation_loss_coef * (squared error of estimated state-values)`
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code: def _initialize(self):
      pass
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  from typing import Callable, Optional, Text
  import gin
  import numpy
  import tensorflow
  from tf_agents.agents import data_converter
  from tf_agents.agents import tf_agent
  from tf_agents.networks import network
  from tf_agents.policies import actor_policy
  from tf_agents.policies import greedy_policy
  from tf_agents.trajectories import time_step
  from tf_agents.trajectories import trajectory
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import eager_utils
  from tf_agents.utils import nest_utils
  from tf_agents.utils import value_ops
label_desc: Use image node_id 2 for calling the ReinforceAgent obj's underlying member method code with example usage: obj._initialize() without return types