mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
from ray.rllib.utils.schedules.schedule import Schedule
|
|
|
|
|
|
class ExponentialSchedule(Schedule):
|
|
def __init__(self,
|
|
schedule_timesteps,
|
|
framework,
|
|
initial_p=1.0,
|
|
decay_rate=0.1):
|
|
"""
|
|
Exponential decay schedule from initial_p to final_p over
|
|
schedule_timesteps. After this many time steps always `final_p` is
|
|
returned.
|
|
|
|
Agrs:
|
|
schedule_timesteps (int): Number of time steps for which to
|
|
linearly anneal initial_p to final_p
|
|
initial_p (float): Initial output value.
|
|
decay_rate (float): The percentage of the original value after
|
|
100% of the time has been reached (see formula above).
|
|
>0.0: The smaller the decay-rate, the stronger the decay.
|
|
1.0: No decay at all.
|
|
framework (Optional[str]): One of "tf", "torch", or None.
|
|
"""
|
|
super().__init__(framework=framework)
|
|
assert schedule_timesteps > 0
|
|
self.schedule_timesteps = schedule_timesteps
|
|
self.initial_p = initial_p
|
|
self.decay_rate = decay_rate
|
|
|
|
def _value(self, t):
|
|
"""
|
|
Returns the result of:
|
|
initial_p * decay_rate ** (`t`/t_max)
|
|
"""
|
|
return self.initial_p * \
|
|
self.decay_rate ** (t / self.schedule_timesteps)
|