mirror of
https://github.com/vale981/ray
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47 lines
1.3 KiB
Python
47 lines
1.3 KiB
Python
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from abc import ABCMeta, abstractmethod
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from ray.rllib.utils.framework import check_framework
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class Schedule(metaclass=ABCMeta):
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"""
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Schedule classes implement various time-dependent scheduling schemas, such
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as:
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- Constant behavior.
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- Linear decay.
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- Piecewise decay.
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Useful for backend-agnostic rate/weight changes for learning rates,
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exploration epsilons, beta parameters for prioritized replay, loss weights
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decay, etc..
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Each schedule can be called directly with the `t` (absolute time step)
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value and returns the value dependent on the Schedule and the passed time.
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"""
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def __init__(self, framework=None):
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# TODO(sven): replace with .tf_value() / torch_value() methods that
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# can be applied late binding, so no need to set framework during
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# construction.
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self.framework = check_framework(framework)
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@abstractmethod
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def value(self, t):
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"""
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Returns the value based on a time value.
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Args:
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t (int): The time value (e.g. a time step).
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NOTE: This could be a tf.Tensor.
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Returns:
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any: The calculated value depending on the schedule and `t`.
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"""
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raise NotImplementedError
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def __call__(self, t):
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"""
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Simply calls `self.value(t)`.
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"""
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return self.value(t)
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