f6bd12eb18056ea0ca0364b6e458f96ec0ff7585,rllib/utils/schedules/tests/test_schedules.py,TestSchedules,test_polynomial_schedule,#TestSchedules#,40

Before Change

            power=2.0)

        for fw in framework_iterator(frameworks=["tf", "tfe", "torch", None]):
            fw_ = fw if fw != "tfe" else "tf"
            polynomial = from_config(config, framework=fw_)
            for t in ts:
                out = polynomial(t)
                t = min(t, 100)
                check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)

    def test_exponential_schedule(self):
        ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
        config = dict(initial_p=2.0, decay_rate=0.99, schedule_timesteps=100)

After Change

    def test_polynomial_schedule(self):
        ts = [0, 5, 10, 100, 90, 2, 1, 99, 23, 1000]
        expected = [
            0.5 + (2.0 - 0.5) * (1.0 - min(t, 100) / 100)**2 for t in ts]
        config = dict(
            type="ray.rllib.utils.schedules.polynomial_schedule."
            "PolynomialSchedule",
            schedule_timesteps=100,
            initial_p=2.0,
            final_p=0.5,
            power=2.0)

        for fw in framework_iterator(
                frameworks=["tf2", "tf", "tfe", "torch", None]):
            polynomial = from_config(config, framework=fw)
            for t, e in zip(ts, expected):
                out = polynomial(t)
                check(out, e, decimals=4)

            ts_as_tensors = self._get_framework_tensors(ts, fw)
            for t, e in zip(ts_as_tensors, expected):
                out = polynomial(t)
                assert fw != "tf" or isinstance(out, tf.Tensor)
                check(out, e, decimals=4)

    def test_exponential_schedule(self):
        decay_rate = 0.2
        ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
        expected = [2.0 * decay_rate**(t / 100) for t in ts]

Instances