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43043ee4d5
* WIP. * Fixes. * LINT. * WIP. * WIP. * Fixes. * Fixes. * Fixes. * Fixes. * WIP. * Fixes. * Test * Fix. * Fixes and LINT. * Fixes and LINT. * LINT.
40 lines
1.4 KiB
Python
40 lines
1.4 KiB
Python
from ray.rllib.utils.schedules.schedule import Schedule
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from ray.rllib.utils.framework import try_import_tf
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tf1, tf, tfv = try_import_tf()
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class PolynomialSchedule(Schedule):
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def __init__(self,
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schedule_timesteps,
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final_p,
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framework,
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initial_p=1.0,
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power=2.0):
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"""
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Polynomial interpolation between initial_p and final_p over
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schedule_timesteps. After this many time steps always `final_p` is
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returned.
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Agrs:
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schedule_timesteps (int): Number of time steps for which to
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linearly anneal initial_p to final_p
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final_p (float): Final output value.
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initial_p (float): Initial output value.
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framework (Optional[str]): One of "tf", "torch", or None.
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"""
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super().__init__(framework=framework)
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assert schedule_timesteps > 0
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self.schedule_timesteps = schedule_timesteps
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self.final_p = final_p
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self.initial_p = initial_p
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self.power = power
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def _value(self, t):
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"""
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Returns the result of:
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final_p + (initial_p - final_p) * (1 - `t`/t_max) ** power
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"""
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t = min(t, self.schedule_timesteps)
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return self.final_p + (self.initial_p - self.final_p) * (
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1.0 - (t / self.schedule_timesteps))**self.power
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