ray.rllib.utils.schedules.piecewise_schedule.PiecewiseSchedule.__init__#

PiecewiseSchedule.__init__(endpoints: List[Tuple[int, float]], framework: Optional[str] = None, interpolation: Callable[[Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor], Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]], Union[numpy.array, jnp.ndarray, tf.Tensor, torch.Tensor]] = <function _linear_interpolation>, outside_value: Optional[float] = None)[source]#

Initializes a PiecewiseSchedule instance.

Parameters

  • endpoints – A list of tuples (t, value) such that the output is an interpolation (given by the interpolation callable) between two values. E.g. t=400 and endpoints=[(0, 20.0),(500, 30.0)] output=20.0 + 0.8 * (30.0 - 20.0) = 28.0 NOTE: All the values for time must be sorted in an increasing order.

  • framework – The framework descriptor string, e.g. “tf”, “torch”, or None.

  • interpolation – A function that takes the left-value, the right-value and an alpha interpolation parameter (0.0=only left value, 1.0=only right value), which is the fraction of distance from left endpoint to right endpoint.

  • outside_value – If t in call to value is outside of all the intervals in endpoints this value is returned. If None then an AssertionError is raised when outside value is requested.