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。
您最多选择25个主题 主题必须以中文或者字母或数字开头,可以包含连字符 (-),并且长度不得超过35个字符
 
 
 
 
 

60 行
1.9 KiB

# This should be the only place that we import tensorflow directly.
# Everywhere else is caught by the banned-modules setting for flake8
from distutils.version import LooseVersion
try:
import tensorflow as tf # noqa I201
# LooseVersion handles things "1.2.3a" or "4.5.6-rc7" fairly sensibly.
_is_tensorflow2 = LooseVersion(tf.__version__) >= LooseVersion("2.0.0")
if _is_tensorflow2:
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
tf_logging = tf.logging
else:
try:
# Newer versions of tf 1.x will complain that tf.logging is deprecated
tf_logging = tf.compat.v1.logging
except AttributeError:
# Fall back to the safe import, even if it might generate a warning or two.
tf_logging = tf.logging
except ImportError:
tf = None
def is_available():
"""
Returns whether Torch is available in this Python environment
"""
return tf is not None
def set_warnings_enabled(is_enabled: bool) -> None:
"""
Enable or disable tensorflow warnings (notably, this disables deprecation warnings.
:param is_enabled:
"""
if is_available():
level = tf_logging.WARN if is_enabled else tf_logging.ERROR
tf_logging.set_verbosity(level)
def generate_session_config() -> "tf.ConfigProto":
"""
Generate a ConfigProto to use for ML-Agents that doesn't consume all of the GPU memory
and allows for soft placement in the case of multi-GPU.
"""
if is_available():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# For multi-GPU training, set allow_soft_placement to True to allow
# placing the operation into an alternative device automatically
# to prevent from exceptions if the device doesn't suppport the operation
# or the device does not exist
config.allow_soft_placement = True
return config
else:
return None