"""This file is used for specifying various schedules that evolve over
time throughout the execution of the algorithm, such as:
- learning rate for the optimizer
- exploration epsilon for the epsilon greedy exploration strategy
- beta parameter for beta parameter in prioritized replay
Each schedule has a function `value(t)` which returns the current value
of the parameter given the timestep t of the optimization procedure.
"""
class Schedule(object):
def value(self, step):
"""
Value of the schedule for a given timestep
:param step: (int) the timestep
:return: (float) the output value for the given timestep
"""
raise NotImplementedError
[docs]class ConstantSchedule(Schedule):
"""
Value remains constant over time.
:param value: (float) Constant value of the schedule
"""
def __init__(self, value):
self._value = value
[docs] def value(self, step):
return self._value
[docs]def linear_interpolation(left, right, alpha):
"""
Linear interpolation between `left` and `right`.
:param left: (float) left boundary
:param right: (float) right boundary
:param alpha: (float) coeff in [0, 1]
:return: (float)
"""
return left + alpha * (right - left)
[docs]class PiecewiseSchedule(Schedule):
"""
Piecewise schedule.
:param endpoints: ([(int, int)])
list of pairs `(time, value)` meaning that schedule should output
`value` when `t==time`. All the values for time must be sorted in
an increasing order. When t is between two times, e.g. `(time_a, value_a)`
and `(time_b, value_b)`, such that `time_a <= t < time_b` then value outputs
`interpolation(value_a, value_b, alpha)` where alpha is a fraction of
time passed between `time_a` and `time_b` for time `t`.
:param interpolation: (lambda (float, float, float): float)
a function that takes value to the left and to the right of t according
to the `endpoints`. Alpha is the fraction of distance from left endpoint to
right endpoint that t has covered. See linear_interpolation for example.
:param outside_value: (float)
if the value is requested outside of all the intervals specified in
`endpoints` this value is returned. If None then AssertionError is
raised when outside value is requested.
"""
def __init__(self, endpoints, interpolation=linear_interpolation, outside_value=None):
idxes = [e[0] for e in endpoints]
assert idxes == sorted(idxes)
self._interpolation = interpolation
self._outside_value = outside_value
self._endpoints = endpoints
[docs] def value(self, step):
for (left_t, left), (right_t, right) in zip(self._endpoints[:-1], self._endpoints[1:]):
if left_t <= step < right_t:
alpha = float(step - left_t) / (right_t - left_t)
return self._interpolation(left, right, alpha)
# t does not belong to any of the pieces, so doom.
assert self._outside_value is not None
return self._outside_value
[docs]class LinearSchedule(Schedule):
"""
Linear interpolation between initial_p and final_p over
schedule_timesteps. After this many timesteps pass final_p is
returned.
:param schedule_timesteps: (int) Number of timesteps for which to linearly anneal initial_p to final_p
:param initial_p: (float) initial output value
:param final_p: (float) final output value
"""
def __init__(self, schedule_timesteps, final_p, initial_p=1.0):
self.schedule_timesteps = schedule_timesteps
self.final_p = final_p
self.initial_p = initial_p
[docs] def value(self, step):
fraction = min(float(step) / self.schedule_timesteps, 1.0)
return self.initial_p + fraction * (self.final_p - self.initial_p)
[docs]def get_schedule_fn(value_schedule):
"""
Transform (if needed) learning rate and clip range
to callable.
:param value_schedule: (callable or float)
:return: (function)
"""
# If the passed schedule is a float
# create a constant function
if isinstance(value_schedule, (float, int)):
# Cast to float to avoid errors
value_schedule = constfn(float(value_schedule))
else:
assert callable(value_schedule)
return value_schedule
[docs]def constfn(val):
"""
Create a function that returns a constant
It is useful for learning rate schedule (to avoid code duplication)
:param val: (float)
:return: (function)
"""
def func(_):
return val
return func
# ================================================================
# Legacy scheduler used by A2C, AKCTR and ACER
# ================================================================
[docs]def constant(_):
"""
Returns a constant value for the Scheduler
:param _: ignored
:return: (float) 1
"""
return 1.
[docs]def linear_schedule(progress):
"""
Returns a linear value for the Scheduler
:param progress: (float) Current progress status (in [0, 1])
:return: (float) 1 - progress
"""
return 1 - progress
[docs]def middle_drop(progress):
"""
Returns a linear value with a drop near the middle to a constant value for the Scheduler
:param progress: (float) Current progress status (in [0, 1])
:return: (float) 1 - progress if (1 - progress) >= 0.75 else 0.075
"""
eps = 0.75
if 1 - progress < eps:
return eps * 0.1
return 1 - progress
[docs]def double_linear_con(progress):
"""
Returns a linear value (x2) with a flattened tail for the Scheduler
:param progress: (float) Current progress status (in [0, 1])
:return: (float) 1 - progress*2 if (1 - progress*2) >= 0.125 else 0.125
"""
progress *= 2
eps = 0.125
if 1 - progress < eps:
return eps
return 1 - progress
[docs]def double_middle_drop(progress):
"""
Returns a linear value with two drops near the middle to a constant value for the Scheduler
:param progress: (float) Current progress status (in [0, 1])
:return: (float) if 0.75 <= 1 - p: 1 - p, if 0.25 <= 1 - p < 0.75: 0.75, if 1 - p < 0.25: 0.125
"""
eps1 = 0.75
eps2 = 0.25
if 1 - progress < eps1:
if 1 - progress < eps2:
return eps2 * 0.5
return eps1 * 0.1
return 1 - progress
SCHEDULES = {
'linear': linear_schedule,
'constant': constant,
'double_linear_con': double_linear_con,
'middle_drop': middle_drop,
'double_middle_drop': double_middle_drop
}
class Scheduler(object):
def __init__(self, initial_value, n_values, schedule):
"""
Update a value every iteration, with a specific curve.
This is a legacy version of schedules, originally defined
in a2c/utils.py. Used by A2C, ACER and ACKTR algorithms.
:param initial_value: (float) initial value
:param n_values: (int) the total number of iterations
:param schedule: (function) the curve you wish to follow for your value
"""
self.step = 0.
self.initial_value = initial_value
self.nvalues = n_values
self.schedule = SCHEDULES[schedule]
def value(self):
"""
Update the Scheduler, and return the current value
:return: (float) the current value
"""
current_value = self.initial_value * self.schedule(self.step / self.nvalues)
self.step += 1.
return current_value
def value_steps(self, steps):
"""
Get a value for a given step
:param steps: (int) The current number of iterations
:return: (float) the value for the current number of iterations
"""
return self.initial_value * self.schedule(steps / self.nvalues)