tensorforce/core/optimizers/solvers/iterative.py
def tf_solve(self, fn_x, x_init, *args):
        """
        Iteratively solves an equation/optimization for $x$ involving an expression $f(x)$.

        Args:
            fn_x: A callable returning an expression $f(x)$ given $x$.
            x_init: Initial solution guess $x_0$.
            *args: Additional solver-specific arguments.

        Returns:
            A solution $x$ to the problem as given by the solver.
        """
        self.fn_x = fn_x

        # Initialization step
        args = self.initialize(x_init, *args)
        # args = util.map_tensors(fn=tf.stop_gradient, tensors=args)

        # Iteration loop with termination condition
        if self.unroll_loop:
            # Unrolled for loop
            for _ in range(self.max_iterations):
                next_step = self.next_step(*args)
                step = (lambda: self.step(*args))
                do_nothing = (lambda: args)
                args = tf.cond(pred=next_step, true_fn=step, false_fn=do_nothing)

        else:
            # TensorFlow while loop
            args = tf.while_loop(cond=self.next_step, body=self.step, loop_vars=args)

        # First argument contains solution
        return args[0]
def tf_solve(self, fn_x, x_init, *args):
        """
        Iteratively solves an equation/optimization for $x$ involving an expression $f(x)$.

        Args:
            fn_x: A callable returning an expression $f(x)$ given $x$.
            x_init: Initial solution guess $x_0$.
            *args: Additional solver-specific arguments.

        Returns:
            A solution $x$ to the problem as given by the solver.
        """
        self.fn_x = fn_x

        # Initialization step
        args = self.initialize(x_init, *args)
        # args = util.map_tensors(fn=tf.stop_gradient, tensors=args)

        # Iteration loop with termination condition
        if self.unroll_loop:
            # Unrolled for loop
            for _ in range(self.max_iterations):
                next_step = self.next_step(*args)
                step = (lambda: self.step(*args))
                do_nothing = (lambda: args)
                args = tf.cond(pred=next_step, true_fn=step, false_fn=do_nothing)

        else:
            # TensorFlow while loop
            args = tf.while_loop(cond=self.next_step, body=self.step, loop_vars=args)

        # First argument contains solution
        return args[0]
def tf_solve ( self , fn_x , x_init , * args ) : self . fn_x = fn_x # Initialization step args = self . initialize ( x_init , * args ) # args = util.map_tensors(fn=tf.stop_gradient, tensors=args) # Iteration loop with termination condition if self . unroll_loop : # Unrolled for loop for _ in range ( self . max_iterations ) : next_step = self . next_step ( * args ) step = ( lambda : self . step ( * args ) ) do_nothing = ( lambda : args ) args = tf . cond ( pred = next_step , true_fn = step , false_fn = do_nothing ) else : # TensorFlow while loop args = tf . while_loop ( cond = self . next_step , body = self . step , loop_vars = args ) # First argument contains solution return args [ 0 ]
Iteratively solves an equation/optimization for $x$ involving an expression $f(x)$.

        Args:
            fn_x: A callable returning an expression $f(x)$ given $x$.
            x_init: Initial solution guess $x_0$.
            *args: Additional solver-specific arguments.

        Returns:
            A solution $x$ to the problem as given by the solver.
Iteratively solves an equation / optimization for $x$ involving an expression $f ( x ) $ .