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42 lines
1.6 KiB
Python
42 lines
1.6 KiB
Python
from ray.rllib.utils.annotations import override
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.schedules.schedule import Schedule
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torch, _ = try_import_torch()
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class ExponentialSchedule(Schedule):
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def __init__(self,
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schedule_timesteps,
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framework,
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initial_p=1.0,
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decay_rate=0.1):
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"""
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Exponential decay schedule from initial_p to 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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initial_p (float): Initial output value.
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decay_rate (float): The percentage of the original value after
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100% of the time has been reached (see formula above).
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>0.0: The smaller the decay-rate, the stronger the decay.
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1.0: No decay at all.
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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.initial_p = initial_p
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self.decay_rate = decay_rate
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@override(Schedule)
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def _value(self, t):
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"""Returns the result of: initial_p * decay_rate ** (`t`/t_max)
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"""
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if self.framework == "torch" and torch and isinstance(t, torch.Tensor):
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t = t.float()
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return self.initial_p * \
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self.decay_rate ** (t / self.schedule_timesteps)
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