mirror of
https://github.com/vale981/ray
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118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
from tensorflow.python.eager.context import eager_mode
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import unittest
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from ray.rllib.utils.schedules import ConstantSchedule, \
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LinearSchedule, ExponentialSchedule, PiecewiseSchedule
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from ray.rllib.utils import check, try_import_tf
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from ray.rllib.utils.from_config import from_config
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tf = try_import_tf()
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class TestSchedules(unittest.TestCase):
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"""
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Tests all time-step dependent Schedule classes.
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"""
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def test_constant_schedule(self):
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value = 2.3
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ts = [100, 0, 10, 2, 3, 4, 99, 56, 10000, 23, 234, 56]
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config = {"value": value}
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for fw in ["tf", "torch", None]:
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constant = from_config(ConstantSchedule, config, framework=fw)
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for t in ts:
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out = constant(t)
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check(out, value)
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# Test eager as well.
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with eager_mode():
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constant = from_config(ConstantSchedule, config, framework="tf")
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for t in ts:
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out = constant(t)
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check(out, value)
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def test_linear_schedule(self):
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ts = [0, 50, 10, 100, 90, 2, 1, 99, 23]
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config = {"schedule_timesteps": 100, "initial_p": 2.1, "final_p": 0.6}
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for fw in ["tf", "torch", None]:
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linear = from_config(LinearSchedule, config, framework=fw)
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for t in ts:
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out = linear(t)
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check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
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# Test eager as well.
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with eager_mode():
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linear = from_config(LinearSchedule, config, framework="tf")
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for t in ts:
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out = linear(t)
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check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
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def test_polynomial_schedule(self):
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ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
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config = dict(
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type="ray.rllib.utils.schedules.polynomial_schedule."
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"PolynomialSchedule",
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schedule_timesteps=100,
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initial_p=2.0,
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final_p=0.5,
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power=2.0)
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for fw in ["tf", "torch", None]:
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config["framework"] = fw
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polynomial = from_config(config)
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for t in ts:
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out = polynomial(t)
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check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
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# Test eager as well.
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with eager_mode():
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config["framework"] = "tf"
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polynomial = from_config(config)
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for t in ts:
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out = polynomial(t)
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check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
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def test_exponential_schedule(self):
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ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
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config = dict(initial_p=2.0, decay_rate=0.99, schedule_timesteps=100)
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for fw in ["tf", "torch", None]:
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config["framework"] = fw
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exponential = from_config(ExponentialSchedule, config)
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for t in ts:
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out = exponential(t)
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check(out, 2.0 * 0.99**(t / 100), decimals=4)
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# Test eager as well.
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with eager_mode():
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config["framework"] = "tf"
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exponential = from_config(ExponentialSchedule, config)
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for t in ts:
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out = exponential(t)
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check(out, 2.0 * 0.99**(t / 100), decimals=4)
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def test_piecewise_schedule(self):
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ts = [0, 5, 10, 100, 90, 2, 1, 99, 27]
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expected = [50.0, 60.0, 70.0, 14.5, 14.5, 54.0, 52.0, 14.5, 140.0]
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config = dict(
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endpoints=[(0, 50.0), (25, 100.0), (30, 200.0)],
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outside_value=14.5)
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for fw in ["tf", "torch", None]:
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config["framework"] = fw
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piecewise = from_config(PiecewiseSchedule, config)
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for t, e in zip(ts, expected):
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out = piecewise(t)
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check(out, e, decimals=4)
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# Test eager as well.
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with eager_mode():
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config["framework"] = "tf"
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piecewise = from_config(PiecewiseSchedule, config)
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for t, e in zip(ts, expected):
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out = piecewise(t)
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check(out, e, decimals=4)
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if __name__ == "__main__":
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import unittest
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unittest.main(verbosity=1)
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