from __future__ import print_function
from __future__ import division
from __future__ import unicode_literals
from __future__ import absolute_import
from builtins import * # NOQA
from future import standard_library
standard_library.install_aliases()
from abc import ABCMeta
from abc import abstractmethod
from future.utils import with_metaclass
import numpy as np
[docs]class StepHook(with_metaclass(ABCMeta, object)):
"""Hook function that will be called in training.
This class is for clarifying the interface required for Hook functions.
You don't need to inherit this class to define your own hooks. Any callable
that accepts (env, agent, step) as arguments can be used as a hook.
"""
@abstractmethod
def __call__(self, env, agent, step):
"""Call the hook.
Args:
env: Environment.
agent: Agent.
step: Current timestep.
"""
raise NotImplementedError
[docs]class LinearInterpolationHook(StepHook):
"""Hook that will set a linearly interpolated value.
You can use this hook to decay the learning rate by using a setter function
as follows:
.. code-block:: python
def lr_setter(env, agent, value):
agent.optimizer.lr = value
hook = LinearInterpolationHook(10 ** 6, 1e-3, 0, lr_setter)
Args:
total_steps (int): Number of total steps.
start_value (float): Start value.
stop_value (float): Stop value.
setter (callable): (env, agent, value) -> None
"""
def __init__(self, total_steps, start_value, stop_value, setter):
self.total_steps = total_steps
self.start_value = start_value
self.stop_value = stop_value
self.setter = setter
def __call__(self, env, agent, step):
value = np.interp(step,
[1, self.total_steps],
[self.start_value, self.stop_value])
self.setter(env, agent, value)