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- from typing import Optional
- from ray.rllib.utils.annotations import OldAPIStack, override
- from ray.rllib.utils.framework import try_import_torch
- from ray.rllib.utils.schedules.schedule import Schedule
- from ray.rllib.utils.typing import TensorType
- torch, _ = try_import_torch()
- @OldAPIStack
- class ExponentialSchedule(Schedule):
- """Exponential decay schedule from `initial_p` to `final_p`.
- Reduces output over `schedule_timesteps`. After this many time steps
- always returns `final_p`.
- """
- def __init__(
- self,
- schedule_timesteps: int,
- framework: Optional[str] = None,
- initial_p: float = 1.0,
- decay_rate: float = 0.1,
- ):
- """Initializes a ExponentialSchedule instance.
- Args:
- schedule_timesteps: Number of time steps for which to
- linearly anneal initial_p to final_p.
- framework: The framework descriptor string, e.g. "tf",
- "torch", or None.
- initial_p: Initial output value.
- decay_rate: 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.
- """
- 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: TensorType) -> TensorType:
- """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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