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

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()

[docs]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)