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- from ray.rllib.utils.annotations import override
- from ray.rllib.utils.framework import try_import_torch
- from ray.rllib.utils.schedules.schedule import Schedule
- torch, _ = try_import_torch()
- 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
- @override(Schedule)
- def _value(self, t):
- """Returns the result of: initial_p * decay_rate ** (`t`/t_max)
- """
- if self.framework == "torch" and torch and isinstance(t, torch.Tensor):
- t = t.float()
- return self.initial_p * \
- self.decay_rate ** (t / self.schedule_timesteps)
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