class_docstr: Line search algorithm which iteratively optimizes the value $f(x)$ for $x$ on the line between
  $x'$ and $x_0$ by optimistically taking the first acceptable $x$ starting from $x_0$ and
  moving towards $x'$.
class_docstr_tok: ["Line","search","algorithm","which","iteratively","optimizes","the","value","$","f","(","x",")","$","for","$","x","$","on","the","line","between","$","x","'","$","and","$","x_0","$","by","optimistically","taking","the","first","acceptable","$","x","$","starting","from","$","x_0","$","and","moving","towards","$","x","'","$","."]
docstr: Termination condition: max number of iterations, or no improvement for last step, or
  improvement less than acceptable ratio, or estimated value not positive.
  
  Args:
      x: Current solution estimate $x_{t-1}$.
      deltas: Current difference $x_t - x_{t-1}$.
      improvement: Current improvement $(f(x') - f(x_t))$.
      last_improvement: Last improvement $(f(x') - f(x_{t-1}))$.
      base_value: Value $f(x')$ at $x = x'$.
  
  Returns:
      True if another iteration should be performed.
docstr_tok: ["Termination","condition",":","max","number","of","iterations",",","or","no","improvement","for","last","step",",","or","improvement","less","than","acceptable","ratio",",","or","estimated","value","not","positive",".","Args",":","x",":","Current","solution","estimate","$","x_","{","t-1","}","$",".","deltas",":","Current","difference","$","x_t","-","x_","{","t-1","}","$",".","improvement",":","Current","improvement","$","(","f","(","x","'",")","-","f","(","x_t",")",")","$",".","last_improvement",":","Last","improvement","$","(","f","(","x","'",")","-","f","(","x_","{","t-1","}",")",")","$",".","base_value",":","Value","$","f","(","x","'",")","$","at","$","x","=","x","'","$",".","Returns",":","True","if","another","iteration","should","be","performed","."]
code: def next_step(
      self,
      *,
      arguments,
      x,
      deltas,
      improvement,
      last_improvement,
      base_value,
  ):
      """
      Termination condition: max number of iterations, or no improvement for last step, or
      improvement less than acceptable ratio, or estimated value not positive.
  
      Args:
          x: Current solution estimate $x_{t-1}$.
          deltas: Current difference $x_t - x_{t-1}$.
          improvement: Current improvement $(f(x') - f(x_t))$.
          last_improvement: Last improvement $(f(x') - f(x_{t-1}))$.
          base_value: Value $f(x')$ at $x = x'$.
  
      Returns:
          True if another iteration should be performed.
      """
      return improvement > last_improvement
  
file_path: tensorforce/tensorforce/core/optimizers/solvers/line_search.py
imports: import tensorflow
  from tensorforce import util
  from tensorforce.core import parameter_modules, SignatureDict, TensorSpec, tf_function, tf_util
  from tensorforce.core.optimizers.solvers import Iterative
label_desc: Use image node_id 7 for calling the LineSearch obj's underlying member method code with example usage: obj.next_step() and returns: unknown