deepMind Lab 可以调用的python API 文档 ，从deepMindlab的代码上直接复制粘贴的，方便今后查看。
Python environment API
The Python module deepmind_lab defines the Lab class. For example usage, there is python/tests/dmlab_module_test.py,python/random_agent.py, and python/random_agent_simple.py.
class deepmind_lab.Lab(level, observations, config={}, renderer='software')
Creates an environment object, loading the game script file level. The environment's observations() method will return the observations specified by name in the list observations.
The config dict specifies additional settings as key-value string pairs. The following options are recognized:
| Option | Description | Default |
|---|---|---|
width |
horizontal resolution of the observation frames | '320' |
height |
vertical resolution of the observation frames | '240' |
fps |
frames per second | '60' |
appendCommand |
Commands for the internal Quake console* | '' |
* See also Lua map API.
Unrecognized options are passed down to the level's init function. In Lua, this is kwargs.opts in api:init.
For example, you can run a modified version of the lt_chasm level with these calls,
import deepmind_lab
observations = ['RGBD']
env = deepmind_lab.Lab('lt_chasm', observations,
config={'width': '640', # screen size, in pixels
'height': '480', # screen size, in pixels
'botCount': '2'}, # lt_chasm option.
renderer='hardware') # select renderer.
env.reset()
Building with --define graphics= sets which graphics implementation is used.
--define graphics=osmesa_or_egl.
If no define is set then the build uses this config_setting at the default.
- If
rendereris set to'software'then osmesa is used for rendering. - If
rendereris set to'hardware'then EGL is used for rendering.
--define graphics=osmesa_or_glx.
- If
rendereris set to'software'then osmesa is used for rendering. - If
rendereris set to'hardware'then GLX is used for rendering.
--define graphics=sdl.
This will render the game to the native window. One of the observation starting with 'RGB' must be in the observations for the game to render correctly.
DeepMind Lab environment objects have the following methods:
reset(episode=-1, seed=None)
Resets the environment to its initialization state. This method needs to be called to start a new episode after the last episode ended (which puts the environment into is_running() == False state).
The optional integer argument episode can be supplied to load the level in a specific episode. If it isn't supplied or negative, episodes are loaded in numerical order.
The optional integer argument seed can be supplied to seed the environment's random number generator. If seed is omitted or None, a random number is used.
num_steps()
Number of frames since the last reset() call
is_running()
Returns True if the environment is in running status, False otherwise.
step(action, num_steps=1)
Advance the environment a number num_steps frames, executing the action defined by action during every frame.
The Numpy array action should have dtype np.intc and should adhere to the specification given in action_spec(), otherwise the behaviour is undefined.
observation_spec()
Returns a list specifying the available observations DeepMind Lab supports, including level specific custom observations.
env = deepmind_lab.Lab('tests/empty_room_test', [])
observation_spec = env.observation_spec()
pprint.pprint(observation_spec)
# Outputs:
[{'dtype': <type 'numpy.uint8'>, 'name': 'RGB_INTERLACED', 'shape': (180, 320, 3)},
{'dtype': <type 'numpy.uint8'>, 'name': 'RGBD_INTERLACED', 'shape': (180, 320, 4)},
{'dtype': <type 'numpy.uint8'>, 'name': 'RGB', 'shape': (3, 180, 320)},
{'dtype': <type 'numpy.uint8'>, 'name': 'RGBD', 'shape': (4, 180, 320)},
{'dtype': <type 'numpy.float64'>, 'name': 'MAP_FRAME_NUMBER', 'shape': (1,)},
{'dtype': <type 'numpy.float64'>, 'name': 'VEL.TRANS', 'shape': (3,)},
{'dtype': <type 'numpy.float64'>, 'name': 'VEL.ROT', 'shape': (3,)},
{'dtype': <type 'str'>, 'name': 'INSTR', 'shape': ()},
{'dtype': <type 'numpy.float64'>, 'name': 'DEBUG.POS.TRANS', 'shape': (3,)},
{'dtype': <type 'numpy.float64'>, 'name': 'DEBUG.POS.ROT', 'shape': (3,)},
{'dtype': <type 'numpy.float64'>, 'name': 'DEBUG.PLAYER_ID', 'shape': (1,)},
# etc...
The observation_spec returns the name, type and shape of the tensor or string that will be returned if that spec name is specified in the observation list.
Example:
{
'name': 'RGB_INTERLACED', ## Name of observation.
'dtype': <type 'numpy.uint8'>, ## Data type array.
'shape': (180, 320, 3) ## Shape of array. (Height, Width, Colors)
}
If the 'dtype' is
events()
Returns a list of events that has occurred since the last call to reset() or step(). Each event is a tuple of a name, and a list of observations.
fps()
An advisory metric that correlates discrete environment steps ("frames") with real (wallclock) time: the number of frames per (real) second.
action_spec()
Returns a dict specifying the shape of the actions expected by step():
env = deepmind_lab.Lab('tests/empty_room_test', [])
action_spec = env.action_spec()
pprint.pprint(action_spec)
# Outputs:
# [{'max': 512, 'min': -512, 'name': 'LOOK_LEFT_RIGHT_PIXELS_PER_FRAME'},
# {'max': 512, 'min': -512, 'name': 'LOOK_DOWN_UP_PIXELS_PER_FRAME'},
# {'max': 1, 'min': -1, 'name': 'STRAFE_LEFT_RIGHT'},
# {'max': 1, 'min': -1, 'name': 'MOVE_BACK_FORWARD'},
# {'max': 1, 'min': 0, 'name': 'FIRE'},
# {'max': 1, 'min': 0, 'name': 'JUMP'},
# {'max': 1, 'min': 0, 'name': 'CROUCH'}]
observations()
Returns a dict, with every observation type passed at initialization as a Numpy array:
env = deepmind_lab.Lab('tests/empty_room_test', ['RGBD'])
env.reset()
obs = env.observations()
obs['RGBD'].dtype
# => dtype('int64')
close()
Closes the environment and releases the underlying Quake III Arena instance. The only method call allowed for closed environments is is_running().