mirror of
https://github.com/vale981/ray
synced 2025-03-08 19:41:38 -05:00
118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
from tensorflow.python.eager.context import eager_mode
|
|
import unittest
|
|
|
|
from ray.rllib.utils.schedules import ConstantSchedule, \
|
|
LinearSchedule, ExponentialSchedule, PiecewiseSchedule
|
|
from ray.rllib.utils import check, try_import_tf
|
|
from ray.rllib.utils.from_config import from_config
|
|
|
|
tf = try_import_tf()
|
|
|
|
|
|
class TestSchedules(unittest.TestCase):
|
|
"""
|
|
Tests all time-step dependent Schedule classes.
|
|
"""
|
|
|
|
def test_constant_schedule(self):
|
|
value = 2.3
|
|
ts = [100, 0, 10, 2, 3, 4, 99, 56, 10000, 23, 234, 56]
|
|
|
|
config = {"value": value}
|
|
|
|
for fw in ["tf", "torch", None]:
|
|
constant = from_config(ConstantSchedule, config, framework=fw)
|
|
for t in ts:
|
|
out = constant(t)
|
|
check(out, value)
|
|
|
|
# Test eager as well.
|
|
with eager_mode():
|
|
constant = from_config(ConstantSchedule, config, framework="tf")
|
|
for t in ts:
|
|
out = constant(t)
|
|
check(out, value)
|
|
|
|
def test_linear_schedule(self):
|
|
ts = [0, 50, 10, 100, 90, 2, 1, 99, 23]
|
|
config = {"schedule_timesteps": 100, "initial_p": 2.1, "final_p": 0.6}
|
|
for fw in ["tf", "torch", None]:
|
|
linear = from_config(LinearSchedule, config, framework=fw)
|
|
for t in ts:
|
|
out = linear(t)
|
|
check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
|
|
|
|
# Test eager as well.
|
|
with eager_mode():
|
|
linear = from_config(LinearSchedule, config, framework="tf")
|
|
for t in ts:
|
|
out = linear(t)
|
|
check(out, 2.1 - (t / 100) * (2.1 - 0.6), decimals=4)
|
|
|
|
def test_polynomial_schedule(self):
|
|
ts = [0, 5, 10, 100, 90, 2, 1, 99, 23]
|
|
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 ["tf", "torch", None]:
|
|
config["framework"] = fw
|
|
polynomial = from_config(config)
|
|
for t in ts:
|
|
out = polynomial(t)
|
|
check(out, 0.5 + (2.0 - 0.5) * (1.0 - t / 100)**2, decimals=4)
|
|
|
|
# Test eager as well.
|
|
with eager_mode():
|
|
config["framework"] = "tf"
|
|
polynomial = from_config(config)
|
|
for t in ts:
|
|
out = polynomial(t)
|
|
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)
|
|
for fw in ["tf", "torch", None]:
|
|
config["framework"] = fw
|
|
exponential = from_config(ExponentialSchedule, config)
|
|
for t in ts:
|
|
out = exponential(t)
|
|
check(out, 2.0 * 0.99**(t / 100), decimals=4)
|
|
|
|
# Test eager as well.
|
|
with eager_mode():
|
|
config["framework"] = "tf"
|
|
exponential = from_config(ExponentialSchedule, config)
|
|
for t in ts:
|
|
out = exponential(t)
|
|
check(out, 2.0 * 0.99**(t / 100), decimals=4)
|
|
|
|
def test_piecewise_schedule(self):
|
|
ts = [0, 5, 10, 100, 90, 2, 1, 99, 27]
|
|
expected = [50.0, 60.0, 70.0, 14.5, 14.5, 54.0, 52.0, 14.5, 140.0]
|
|
config = dict(
|
|
endpoints=[(0, 50.0), (25, 100.0), (30, 200.0)],
|
|
outside_value=14.5)
|
|
for fw in ["tf", "torch", None]:
|
|
config["framework"] = fw
|
|
piecewise = from_config(PiecewiseSchedule, config)
|
|
for t, e in zip(ts, expected):
|
|
out = piecewise(t)
|
|
check(out, e, decimals=4)
|
|
|
|
# Test eager as well.
|
|
with eager_mode():
|
|
config["framework"] = "tf"
|
|
piecewise = from_config(PiecewiseSchedule, config)
|
|
for t, e in zip(ts, expected):
|
|
out = piecewise(t)
|
|
check(out, e, decimals=4)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
import unittest
|
|
unittest.main(verbosity=1)
|