garage.tf.optimizers.first_order_optimizer module

class FirstOrderOptimizer(tf_optimizer_cls=None, tf_optimizer_args=None, max_epochs=1000, tolerance=1e-06, batch_size=32, callback=None, verbose=False, name='FirstOrderOptimizer', **kwargs)

Bases: object

Performs (stochastic) gradient descent, possibly using fancier methods like ADAM etc.

loss(inputs, extra_inputs=None)

optimize(inputs, extra_inputs=None, callback=None)

update_opt(loss, target, inputs, extra_inputs=None, **kwargs)

Parameters:

• loss – Symbolic expression for the loss function.
• target – A parameterized object to optimize over. It should implement methods of the

garage.core.paramerized.Parameterized class. :param leq_constraint: A constraint provided as a tuple (f, epsilon),

of the form f(*inputs) <= epsilon.

Parameters:inputs – A list of symbolic variables as inputs Returns:No return value.