Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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import os
import tempfile
import pytest
import mlagents.trainers.tf.tensorflow_to_barracuda as tf2bc
from mlagents.trainers.tests.test_nn_policy import create_policy_mock
from mlagents.trainers.settings import TrainerSettings
from mlagents.tf_utils import tf
from mlagents.model_serialization import SerializationSettings
def test_barracuda_converter():
path_prefix = os.path.dirname(os.path.abspath(__file__))
tmpfile = os.path.join(
tempfile._get_default_tempdir(), next(tempfile._get_candidate_names()) + ".nn"
)
# make sure there are no left-over files
if os.path.isfile(tmpfile):
os.remove(tmpfile)
tf2bc.convert(path_prefix + "/BasicLearning.pb", tmpfile)
# test if file exists after conversion
assert os.path.isfile(tmpfile)
# currently converter produces small output file even if input file is empty
# 100 bytes is high enough to prove that conversion was successful
assert os.path.getsize(tmpfile) > 100
# cleanup
os.remove(tmpfile)
@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
def test_policy_conversion(tmpdir, rnn, visual, discrete):
tf.reset_default_graph()
dummy_config = TrainerSettings()
policy = create_policy_mock(
dummy_config,
use_rnn=rnn,
model_path=os.path.join(tmpdir, "test"),
use_discrete=discrete,
use_visual=visual,
)
settings = SerializationSettings(policy.model_path, "MockBrain")
checkpoint_path = f"{tmpdir}/MockBrain-1"
policy.checkpoint(checkpoint_path, settings)
# These checks taken from test_barracuda_converter
assert os.path.isfile(checkpoint_path + ".nn")
assert os.path.getsize(checkpoint_path + ".nn") > 100