code: """Deep Q learning graph
  
  The functions in this file can are used to create the following functions:
  
  ======= act ========
  
      Function to chose an action given an observation
  
      Parameters
      ----------
      observation: object
          Observation that can be feed into the output of make_obs_ph
      stochastic: bool
          if set to False all the actions are always deterministic (default False)
      update_eps_ph: float
          update epsilon a new value, if negative not update happens
          (default: no update)
  
      Returns
      -------
      Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for
      every element of the batch.
  
  
  ======= act (in case of parameter noise) ========
  
      Function to chose an action given an observation
  
      Parameters
      ----------
      observation: object
          Observation that can be feed into the output of make_obs_ph
      stochastic: bool
          if set to False all the actions are always deterministic (default False)
      update_eps_ph: float
          update epsilon to a new value, if negative no update happens
          (default: no update)
      reset_ph: bool
          reset the perturbed policy by sampling a new perturbation
      update_param_noise_threshold_ph: float
          the desired threshold for the difference between non-perturbed and perturbed policy
      update_param_noise_scale_ph: bool
          whether or not to update the scale of the noise for the next time it is re-perturbed
  
      Returns
      -------
      Tensor of dtype tf.int64 and shape (BATCH_SIZE,) with an action to be performed for
      every element of the batch.
  
  
  ======= train =======
  
      Function that takes a transition (s,a,r,s') and optimizes Bellman equation's error:
  
          td_error = Q(s,a) - (r + gamma * max_a' Q(s', a'))
          loss = huber_loss[td_error]
  
      Parameters
      ----------
      obs_t: object
          a batch of observations
      action: np.array
          actions that were selected upon seeing obs_t.
          dtype must be int32 and shape must be (batch_size,)
      reward: np.array
          immediate reward attained after executing those actions
          dtype must be float32 and shape must be (batch_size,)
      obs_tp1: object
          observations that followed obs_t
      done: np.array
          1 if obs_t was the last observation in the episode and 0 otherwise
          obs_tp1 gets ignored, but must be of the valid shape.
          dtype must be float32 and shape must be (batch_size,)
      weight: np.array
          imporance weights for every element of the batch (gradient is multiplied
          by the importance weight) dtype must be float32 and shape must be (batch_size,)
  
      Returns
      -------
      td_error: np.array
          a list of differences between Q(s,a) and the target in Bellman's equation.
          dtype is float32 and shape is (batch_size,)
  
  ======= update_target ========
  
      copy the parameters from optimized Q function to the target Q function.
      In Q learning we actually optimize the following error:
  
          Q(s,a) - (r + gamma * max_a' Q'(s', a'))
  
      Where Q' is lagging behind Q to stablize the learning. For example for Atari
  
      Q' is set to Q once every 10000 updates training steps.
  
  """
  import tensorflow as tf
  import baselines.common.tf_util as U
  
  
  def scope_vars(scope, trainable_only=False):
      """
      Get variables inside a scope
      The scope can be specified as a string
      Parameters
      ----------
      scope: str or VariableScope
          scope in which the variables reside.
      trainable_only: bool
          whether or not to return only the variables that were marked as trainable.
      Returns
      -------
      vars: [tf.Variable]
          list of variables in `scope`.
      """
      return tf.get_collection(
          tf.GraphKeys.TRAINABLE_VARIABLES if trainable_only else tf.GraphKeys.GLOBAL_VARIABLES,
          scope=scope if isinstance(scope, str) else scope.name
      )
  
  
  def scope_name():
      """Returns the name of current scope as a string, e.g. deepq/q_func"""
      return tf.get_variable_scope().name
  
  
  def absolute_scope_name(relative_scope_name):
      """Appends parent scope name to `relative_scope_name`"""
      return scope_name() + "/" + relative_scope_name
  
  
  def default_param_noise_filter(var):
      if var not in tf.trainable_variables():
          # We never perturb non-trainable vars.
          return False
      if "fully_connected" in var.name:
          # We perturb fully-connected layers.
          return True
  
      # The remaining layers are likely conv or layer norm layers, which we do not wish to
      # perturb (in the former case because they only extract features, in the latter case because
      # we use them for normalization purposes). If you change your network, you will likely want
      # to re-consider which layers to perturb and which to keep untouched.
      return False
  
  
  def build_act(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None):
      """Creates the act function:
  
      Parameters
      ----------
      make_obs_ph: str -> tf.placeholder or TfInput
          a function that take a name and creates a placeholder of input with that name
      q_func: (tf.Variable, int, str, bool) -> tf.Variable
          the model that takes the following inputs:
              observation_in: object
                  the output of observation placeholder
              num_actions: int
                  number of actions
              scope: str
              reuse: bool
                  should be passed to outer variable scope
          and returns a tensor of shape (batch_size, num_actions) with values of every action.
      num_actions: int
          number of actions.
      scope: str or VariableScope
          optional scope for variable_scope.
      reuse: bool or None
          whether or not the variables should be reused. To be able to reuse the scope must be given.
  
      Returns
      -------
      act: (tf.Variable, bool, float) -> tf.Variable
          function to select and action given observation.
  `       See the top of the file for details.
      """
      with tf.variable_scope(scope, reuse=reuse):
          observations_ph = make_obs_ph("observation")
          stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
          update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
  
          eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
  
          q_values = q_func(observations_ph.get(), num_actions, scope="q_func")
          deterministic_actions = tf.argmax(q_values, axis=1)
  
          batch_size = tf.shape(observations_ph.get())[0]
          random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
          chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
          stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
  
          output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
          update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
          _act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph],
                           outputs=output_actions,
                           givens={update_eps_ph: -1.0, stochastic_ph: True},
                           updates=[update_eps_expr])
          def act(ob, stochastic=True, update_eps=-1):
              return _act(ob, stochastic, update_eps)
          return act
  
  
  def build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope="deepq", reuse=None, param_noise_filter_func=None):
      """Creates the act function with support for parameter space noise exploration (https://arxiv.org/abs/1706.01905):
  
      Parameters
      ----------
      make_obs_ph: str -> tf.placeholder or TfInput
          a function that take a name and creates a placeholder of input with that name
      q_func: (tf.Variable, int, str, bool) -> tf.Variable
          the model that takes the following inputs:
              observation_in: object
                  the output of observation placeholder
              num_actions: int
                  number of actions
              scope: str
              reuse: bool
                  should be passed to outer variable scope
          and returns a tensor of shape (batch_size, num_actions) with values of every action.
      num_actions: int
          number of actions.
      scope: str or VariableScope
          optional scope for variable_scope.
      reuse: bool or None
          whether or not the variables should be reused. To be able to reuse the scope must be given.
      param_noise_filter_func: tf.Variable -> bool
          function that decides whether or not a variable should be perturbed. Only applicable
          if param_noise is True. If set to None, default_param_noise_filter is used by default.
  
      Returns
      -------
      act: (tf.Variable, bool, float, bool, float, bool) -> tf.Variable
          function to select and action given observation.
  `       See the top of the file for details.
      """
      if param_noise_filter_func is None:
          param_noise_filter_func = default_param_noise_filter
  
      with tf.variable_scope(scope, reuse=reuse):
          observations_ph = make_obs_ph("observation")
          stochastic_ph = tf.placeholder(tf.bool, (), name="stochastic")
          update_eps_ph = tf.placeholder(tf.float32, (), name="update_eps")
          update_param_noise_threshold_ph = tf.placeholder(tf.float32, (), name="update_param_noise_threshold")
          update_param_noise_scale_ph = tf.placeholder(tf.bool, (), name="update_param_noise_scale")
          reset_ph = tf.placeholder(tf.bool, (), name="reset")
  
          eps = tf.get_variable("eps", (), initializer=tf.constant_initializer(0))
          param_noise_scale = tf.get_variable("param_noise_scale", (), initializer=tf.constant_initializer(0.01), trainable=False)
          param_noise_threshold = tf.get_variable("param_noise_threshold", (), initializer=tf.constant_initializer(0.05), trainable=False)
  
          # Unmodified Q.
          q_values = q_func(observations_ph.get(), num_actions, scope="q_func")
  
          # Perturbable Q used for the actual rollout.
          q_values_perturbed = q_func(observations_ph.get(), num_actions, scope="perturbed_q_func")
          # We have to wrap this code into a function due to the way tf.cond() works. See
          # https://stackoverflow.com/questions/37063952/confused-by-the-behavior-of-tf-cond for
          # a more detailed discussion.
          def perturb_vars(original_scope, perturbed_scope):
              all_vars = scope_vars(absolute_scope_name(original_scope))
              all_perturbed_vars = scope_vars(absolute_scope_name(perturbed_scope))
              assert len(all_vars) == len(all_perturbed_vars)
              perturb_ops = []
              for var, perturbed_var in zip(all_vars, all_perturbed_vars):
                  if param_noise_filter_func(perturbed_var):
                      # Perturb this variable.
                      op = tf.assign(perturbed_var, var + tf.random_normal(shape=tf.shape(var), mean=0., stddev=param_noise_scale))
                  else:
                      # Do not perturb, just assign.
                      op = tf.assign(perturbed_var, var)
                  perturb_ops.append(op)
              assert len(perturb_ops) == len(all_vars)
              return tf.group(*perturb_ops)
  
          # Set up functionality to re-compute `param_noise_scale`. This perturbs yet another copy
          # of the network and measures the effect of that perturbation in action space. If the perturbation
          # is too big, reduce scale of perturbation, otherwise increase.
          q_values_adaptive = q_func(observations_ph.get(), num_actions, scope="adaptive_q_func")
          perturb_for_adaption = perturb_vars(original_scope="q_func", perturbed_scope="adaptive_q_func")
          kl = tf.reduce_sum(tf.nn.softmax(q_values) * (tf.log(tf.nn.softmax(q_values)) - tf.log(tf.nn.softmax(q_values_adaptive))), axis=-1)
          mean_kl = tf.reduce_mean(kl)
          def update_scale():
              with tf.control_dependencies([perturb_for_adaption]):
                  update_scale_expr = tf.cond(mean_kl < param_noise_threshold,
                      lambda: param_noise_scale.assign(param_noise_scale * 1.01),
                      lambda: param_noise_scale.assign(param_noise_scale / 1.01),
                  )
              return update_scale_expr
  
          # Functionality to update the threshold for parameter space noise.
          update_param_noise_threshold_expr = param_noise_threshold.assign(tf.cond(update_param_noise_threshold_ph >= 0,
              lambda: update_param_noise_threshold_ph, lambda: param_noise_threshold))
  
          # Put everything together.
          deterministic_actions = tf.argmax(q_values_perturbed, axis=1)
          batch_size = tf.shape(observations_ph.get())[0]
          random_actions = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=num_actions, dtype=tf.int64)
          chose_random = tf.random_uniform(tf.stack([batch_size]), minval=0, maxval=1, dtype=tf.float32) < eps
          stochastic_actions = tf.where(chose_random, random_actions, deterministic_actions)
  
          output_actions = tf.cond(stochastic_ph, lambda: stochastic_actions, lambda: deterministic_actions)
          update_eps_expr = eps.assign(tf.cond(update_eps_ph >= 0, lambda: update_eps_ph, lambda: eps))
          updates = [
              update_eps_expr,
              tf.cond(reset_ph, lambda: perturb_vars(original_scope="q_func", perturbed_scope="perturbed_q_func"), lambda: tf.group(*[])),
              tf.cond(update_param_noise_scale_ph, lambda: update_scale(), lambda: tf.Variable(0., trainable=False)),
              update_param_noise_threshold_expr,
          ]
          _act = U.function(inputs=[observations_ph, stochastic_ph, update_eps_ph, reset_ph, update_param_noise_threshold_ph, update_param_noise_scale_ph],
                           outputs=output_actions,
                           givens={update_eps_ph: -1.0, stochastic_ph: True, reset_ph: False, update_param_noise_threshold_ph: False, update_param_noise_scale_ph: False},
                           updates=updates)
          def act(ob, reset=False, update_param_noise_threshold=False, update_param_noise_scale=False, stochastic=True, update_eps=-1):
              return _act(ob, stochastic, update_eps, reset, update_param_noise_threshold, update_param_noise_scale)
          return act
  
  
  def build_train(n_step, make_obs_ph, q_func, num_actions, optimizer, grad_norm_clipping=None, gamma=1.0,
      double_q=True, scope="deepq", reuse=None, param_noise=False, param_noise_filter_func=None):
      """Creates the train function:
  
      Parameters
      ----------
      make_obs_ph: str -> tf.placeholder or TfInput
          a function that takes a name and creates a placeholder of input with that name
      q_func: (tf.Variable, int, str, bool) -> tf.Variable
          the model that takes the following inputs:
              observation_in: object
                  the output of observation placeholder
              num_actions: int
                  number of actions
              scope: str
              reuse: bool
                  should be passed to outer variable scope
          and returns a tensor of shape (batch_size, num_actions) with values of every action.
      num_actions: int
          number of actions
      reuse: bool
          whether or not to reuse the graph variables
      optimizer: tf.train.Optimizer
          optimizer to use for the Q-learning objective.
      grad_norm_clipping: float or None
          clip gradient norms to this value. If None no clipping is performed.
      gamma: float
          discount rate.
      double_q: bool
          if true will use Double Q Learning (https://arxiv.org/abs/1509.06461).
          In general it is a good idea to keep it enabled.
      scope: str or VariableScope
          optional scope for variable_scope.
      reuse: bool or None
          whether or not the variables should be reused. To be able to reuse the scope must be given.
      param_noise: bool
          whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
      param_noise_filter_func: tf.Variable -> bool
          function that decides whether or not a variable should be perturbed. Only applicable
          if param_noise is True. If set to None, default_param_noise_filter is used by default.
  
      Returns
      -------
      act: (tf.Variable, bool, float) -> tf.Variable
          function to select and action given observation.
  `       See the top of the file for details.
      train: (object, np.array, np.array, object, np.array, np.array) -> np.array
          optimize the error in Bellman's equation.
  `       See the top of the file for details.
      update_target: () -> ()
          copy the parameters from optimized Q function to the target Q function.
  `       See the top of the file for details.
      debug: {str: function}
          a bunch of functions to print debug data like q_values.
      """
      if param_noise:
          act_f = build_act_with_param_noise(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse,
              param_noise_filter_func=param_noise_filter_func)
      else:
          act_f = build_act(make_obs_ph, q_func, num_actions, scope=scope, reuse=reuse)
  
      with tf.variable_scope(scope, reuse=reuse):
          # set up placeholders
          obs_t_input = make_obs_ph("obs_t")
          act_t_ph = tf.placeholder(tf.int32, [None], name="action")
          rew_t_ph = tf.placeholder(tf.float32, shape=(None, n_step), name="reward")
          obs_tp1_input = make_obs_ph("obs_tp1")
          done_mask_ph = tf.placeholder(tf.float32, shape=(None, n_step), name="done")
          importance_weights_ph = tf.placeholder(tf.float32, [None], name="weight")
  
          # q network evaluation
          q_t = q_func(obs_t_input.get(), num_actions, scope="q_func", reuse=True)  # reuse parameters from act
          q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/q_func")
  
          # target q network evalution
          q_tp1 = q_func(obs_tp1_input.get(), num_actions, scope="target_q_func")
          target_q_func_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=tf.get_variable_scope().name + "/target_q_func")
  
          # q scores for actions which we know were selected in the given state.
          q_t_selected = tf.reduce_sum(q_t * tf.one_hot(act_t_ph, num_actions), 1)
  
          # compute estimate of best possible value starting from state at t + 1
          if double_q:
              q_tp1_using_online_net = q_func(obs_tp1_input.get(), num_actions, scope="q_func", reuse=True)
              q_tp1_best_using_online_net = tf.argmax(q_tp1_using_online_net, 1)
              q_tp1_best = tf.reduce_sum(q_tp1 * tf.one_hot(q_tp1_best_using_online_net, num_actions), 1)
          else:
              q_tp1_best = tf.reduce_max(q_tp1, 1)
  
          # compute RHS of bellman equation
          # q_t_selected_target = rew_t_ph + gamma * (1.0 - done_mask_ph) * q_tp1_best
          q_t_selected_target = tf.reduce_sum(tf.multiply([gamma ** (i) for i in range(n_step)] * (1 - done_mask_ph), rew_t_ph), axis=1)\
                   + gamma ** n_step * (1 - done_mask_ph[:, -1]) * q_tp1_best
  
          # compute the error (potentially clipped)
          td_error = q_t_selected - tf.stop_gradient(q_t_selected_target)
          errors = U.huber_loss(td_error)
          weighted_error = tf.reduce_mean(importance_weights_ph * errors)
  
          # compute optimization op (potentially with gradient clipping)
          if grad_norm_clipping is not None:
              gradients = optimizer.compute_gradients(weighted_error, var_list=q_func_vars)
              for i, (grad, var) in enumerate(gradients):
                  if grad is not None:
                      gradients[i] = (tf.clip_by_norm(grad, grad_norm_clipping), var)
              optimize_expr = optimizer.apply_gradients(gradients)
          else:
              optimize_expr = optimizer.minimize(weighted_error, var_list=q_func_vars)
  
          # update_target_fn will be called periodically to copy Q network to target Q network
          update_target_expr = []
          for var, var_target in zip(sorted(q_func_vars, key=lambda v: v.name),
                                     sorted(target_q_func_vars, key=lambda v: v.name)):
              update_target_expr.append(var_target.assign(var))
          update_target_expr = tf.group(*update_target_expr)
  
          # Create callable functions
          train = U.function(
              inputs=[
                  obs_t_input,
                  act_t_ph,
                  rew_t_ph,
                  obs_tp1_input,
                  done_mask_ph,
                  importance_weights_ph
              ],
              outputs=td_error,
              updates=[optimize_expr]
          )
          update_target = U.function([], [], updates=[update_target_expr])
  
          q_values = U.function([obs_t_input], q_t)
  
          return act_f, train, update_target, {'q_values': q_values}
  
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