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- from typing import Callable, List, Optional, Tuple
- from ray.rllib.utils.annotations import override
- from ray.rllib.utils.framework import try_import_tf
- from ray.rllib.utils.schedules.schedule import Schedule
- from ray.rllib.utils.typing import TensorType
- from ray.util.annotations import DeveloperAPI
- tf1, tf, tfv = try_import_tf()
- def _linear_interpolation(left, right, alpha):
- return left + alpha * (right - left)
- @DeveloperAPI
- class PiecewiseSchedule(Schedule):
- """Implements a Piecewise Scheduler."""
- def __init__(
- self,
- endpoints: List[Tuple[int, float]],
- framework: Optional[str] = None,
- interpolation: Callable[
- [TensorType, TensorType, TensorType], TensorType
- ] = _linear_interpolation,
- outside_value: Optional[float] = None,
- ):
- """Initializes a PiecewiseSchedule instance.
- Args:
- 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.
- """
- super().__init__(framework=framework)
- idxes = [e[0] for e in endpoints]
- assert idxes == sorted(idxes)
- self.interpolation = interpolation
- self.outside_value = outside_value
- self.endpoints = [(int(e[0]), float(e[1])) for e in endpoints]
- @override(Schedule)
- def _value(self, t: TensorType) -> TensorType:
- # Find t in our list of endpoints.
- for (l_t, l), (r_t, r) in zip(self.endpoints[:-1], self.endpoints[1:]):
- # When found, return an interpolation (default: linear).
- if l_t <= t < r_t:
- alpha = float(t - l_t) / (r_t - l_t)
- return self.interpolation(l, r, alpha)
- # t does not belong to any of the pieces, return `self.outside_value`.
- assert self.outside_value is not None
- return self.outside_value
- @override(Schedule)
- def _tf_value_op(self, t: TensorType) -> TensorType:
- assert self.outside_value is not None, (
- "tf-version of PiecewiseSchedule requires `outside_value` to be "
- "provided!"
- )
- endpoints = tf.cast(tf.stack([e[0] for e in self.endpoints] + [-1]), tf.int64)
- # Create all possible interpolation results.
- results_list = []
- for (l_t, l), (r_t, r) in zip(self.endpoints[:-1], self.endpoints[1:]):
- alpha = tf.cast(t - l_t, tf.float32) / tf.cast(r_t - l_t, tf.float32)
- results_list.append(self.interpolation(l, r, alpha))
- # If t does not belong to any of the pieces, return `outside_value`.
- results_list.append(self.outside_value)
- results_list = tf.stack(results_list)
- # Return correct results tensor depending on where we find t.
- def _cond(i, x):
- x = tf.cast(x, tf.int64)
- return tf.logical_not(
- tf.logical_or(
- tf.equal(endpoints[i + 1], -1),
- tf.logical_and(endpoints[i] <= x, x < endpoints[i + 1]),
- )
- )
- def _body(i, x):
- return (i + 1, t)
- idx_and_t = tf.while_loop(_cond, _body, [tf.constant(0, dtype=tf.int64), t])
- return results_list[idx_and_t[0]]
|
