baselines算法库common/vec_env/vec_env.py模块分析
作者：互联网
common/vec_env/vec_env.py模块内容：
import contextlib
import os
from abc import ABC, abstractmethod
from baselines.common.tile_images import tile_images
class AlreadySteppingError(Exception):
"""
Raised when an asynchronous step is running while
step_async() is called again.
"""
def __init__(self):
msg = 'already running an async step'
Exception.__init__(self, msg)
class NotSteppingError(Exception):
"""
Raised when an asynchronous step is not running but
step_wait() is called.
"""
def __init__(self):
msg = 'not running an async step'
Exception.__init__(self, msg)
class VecEnv(ABC):
"""
An abstract asynchronous, vectorized environment.
Used to batch data from multiple copies of an environment, so that
each observation becomes an batch of observations, and expected action is a batch of actions to
be applied per-environment.
"""
closed = False
viewer = None
metadata = {
'render.modes': ['human', 'rgb_array']
}
def __init__(self, num_envs, observation_space, action_space):
self.num_envs = num_envs
self.observation_space = observation_space
self.action_space = action_space
@abstractmethod
def reset(self):
"""
Reset all the environments and return an array of
observations, or a dict of observation arrays.
If step_async is still doing work, that work will
be cancelled and step_wait() should not be called
until step_async() is invoked again.
"""
pass
@abstractmethod
def step_async(self, actions):
"""
Tell all the environments to start taking a step
with the given actions.
Call step_wait() to get the results of the step.
You should not call this if a step_async run is
already pending.
"""
pass
@abstractmethod
def step_wait(self):
"""
Wait for the step taken with step_async().
Returns (obs, rews, dones, infos):
- obs: an array of observations, or a dict of
arrays of observations.
- rews: an array of rewards
- dones: an array of "episode done" booleans
- infos: a sequence of info objects
"""
pass
def close_extras(self):
"""
Clean up the extra resources, beyond what's in this base class.
Only runs when not self.closed.
"""
pass
def close(self):
if self.closed:
return
if self.viewer is not None:
self.viewer.close()
self.close_extras()
self.closed = True
def step(self, actions):
"""
Step the environments synchronously.
This is available for backwards compatibility.
"""
self.step_async(actions)
return self.step_wait()
def render(self, mode='human'):
imgs = self.get_images()
bigimg = tile_images(imgs)
if mode == 'human':
self.get_viewer().imshow(bigimg)
return self.get_viewer().isopen
elif mode == 'rgb_array':
return bigimg
else:
raise NotImplementedError
def get_images(self):
"""
Return RGB images from each environment
"""
raise NotImplementedError
@property
def unwrapped(self):
if isinstance(self, VecEnvWrapper):
return self.venv.unwrapped
else:
return self
def get_viewer(self):
if self.viewer is None:
from gym.envs.classic_control import rendering
self.viewer = rendering.SimpleImageViewer()
return self.viewer
class VecEnvWrapper(VecEnv):
"""
An environment wrapper that applies to an entire batch
of environments at once.
"""
def __init__(self, venv, observation_space=None, action_space=None):
self.venv = venv
super().__init__(num_envs=venv.num_envs,
observation_space=observation_space or venv.observation_space,
action_space=action_space or venv.action_space)
def step_async(self, actions):
self.venv.step_async(actions)
@abstractmethod
def reset(self):
pass
@abstractmethod
def step_wait(self):
pass
def close(self):
return self.venv.close()
def render(self, mode='human'):
return self.venv.render(mode=mode)
def get_images(self):
return self.venv.get_images()
def __getattr__(self, name):
if name.startswith('_'):
raise AttributeError("attempted to get missing private attribute '{}'".format(name))
return getattr(self.venv, name)
class VecEnvObservationWrapper(VecEnvWrapper):
@abstractmethod
def process(self, obs):
pass
def reset(self):
obs = self.venv.reset()
return self.process(obs)
def step_wait(self):
obs, rews, dones, infos = self.venv.step_wait()
return self.process(obs), rews, dones, infos
class CloudpickleWrapper(object):
"""
Uses cloudpickle to serialize contents (otherwise multiprocessing tries to use pickle)
"""
def __init__(self, x):
self.x = x
def __getstate__(self):
import cloudpickle
return cloudpickle.dumps(self.x)
def __setstate__(self, ob):
import pickle
self.x = pickle.loads(ob)
@contextlib.contextmanager
def clear_mpi_env_vars():
"""
from mpi4py import MPI will call MPI_Init by default. If the child process has MPI environment variables, MPI will think that the child process is an MPI process just like the parent and do bad things such as hang.
This context manager is a hacky way to clear those environment variables temporarily such as when we are starting multiprocessing
Processes.
"""
removed_environment = {}
for k, v in list(os.environ.items()):
for prefix in ['OMPI_', 'PMI_']:
if k.startswith(prefix):
removed_environment[k] = v
del os.environ[k]
try:
yield
finally:
os.environ.update(removed_environment)
类
class AlreadySteppingError(Exception):
class NotSteppingError(Exception):
作为异常类不过多介绍。
class VecEnv(ABC): 作为抽象类是对gym的环境进行进一步的包装，该类的作用就是进行多环境env的并行操作，也就是并行与环境进行交互和采样。
继承并实现该类进行初始化的时候需要设置并行的环境数和环境的状态空间和动作空间。
该类的主要操作为 reset, step, render , 这三个操作的含义和gym的设定相同，不同的是并行操作部分：
step函数中调用 self.step_async(actions) 保证多个环境都可以并行的收到下步的动作，self.step_wait() 可以视作阻塞操作用来同步多进程下多个环境的step同步，并将多个环境返回的：
Returns (obs, rews, dones, infos):
- obs: an array of observations, or a dict of
arrays of observations.
- rews: an array of rewards
- dones: an array of "episode done" booleans
- infos: a sequence of info object
向上返回。
render函数为绘图动作，该函数将多个环境的当前状态的图片进行拼接，在'human'模式下将拼接后的图片进行绘图操作，在'rgb_array'模式下对拼接后的图片的np.array形式数据进行返回。
多环境当前状态图片的拼接参见： https://www.cnblogs.com/devilmaycry812839668/p/16025513.html
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标签：__,venv,return,self,py,step,vec,env,def 来源： https://www.cnblogs.com/devilmaycry812839668/p/16027382.html