Source code for ray.rllib.utils.schedules.exponential_schedule

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()

[docs]@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`. """
[docs] 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)