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DeepMind Lab environment documentation: Python

Environment usage in Python

Constructing the environment, doing one step and retrieving one observation:

import deepmind_lab

# Construct and start the environment.
env = deepmind_lab.Lab('seekavoid_arena_01', ['RGB_INTERLACED'])
env.reset()

# Create all-zeros vector for actions.
action = np.zeros([7], dtype=np.intc)

# Advance the environment 4 frames while executing the all-zeros action.
reward = env.step(action, num_steps=4)

# Retrieve the observations of the environment in its new state.
obs = env.observations()  # dict of Numpy arrays
rgb_i = obs['RGB_INTERLACED']
assert rgb_i.shape == (240, 320, 3)

For an example of doing the same thing with the C API, take a look at examples/game_main.c.

Initialised environments can be asked for the list of available observations:

import pprint
lab = deepmind_lab.Lab('seekavoid_arena_01', [])
observation_spec = lab.observation_spec()
pprint.pprint(observation_spec)
# Outputs:
# [{'dtype': <type 'numpy.uint8'>,
#   'name': 'RGB_INTERLACED',
#   'shape': (240, 320, 3)},
#  {'dtype': <type 'numpy.uint8'>,
#   'name': 'RGBD_INTERLACED',
#   'shape': (240, 320, 4)},
#  {'dtype': <type 'numpy.uint8'>, 'name': 'RGB', 'shape': (3, 240, 320)},
#  {'dtype': <type 'numpy.uint8'>, 'name': 'RGBD', 'shape': (4, 240, 320)},
#  {'dtype': <type 'numpy.float64'>, 'name': 'VEL.TRANS', 'shape': (3,)},
#  {'dtype': <type 'numpy.float64'>, 'name': 'VEL.ROT', 'shape': (3,)}]

For full documentation of the Python environment API, see below.

Python environment API

The Python module deepmind_lab defines the Lab class. For example usage, there is python/dmlab_module_test.py and python/random_agent.py.

class deepmind_lab.Lab(level, observations, config={})

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 value
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 the Lua map API ''

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. See above for an example.

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():

lab = deepmind_lab.Lab('tests/demo_map', [])
action_spec = lab.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:

lab = deepmind_lab.Lab('tests/demo_map', ['RGBD'])
lab.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().