concepts.benchmark.algorithm_env.graph_env.GraphEnvBase#

class GraphEnvBase[source]#

Bases: RandomizedEnv

Graph Env Base.

Methods

close()

Override close in your subclass to perform any necessary cleanup.

render([mode])

Renders the environment.

reset(*[, seed, return_info, options])

Resets the environment to an initial state and returns an initial observation.

seed([seed])

Sets the seed for this env's random number generator(s).

step(action)

Run one timestep of the environment's dynamics.

Attributes

action_space

graph

The generated graph.

metadata

np_random

Initializes the np_random field if not done already.

observation_space

reward_range

spec

unwrapped

Completely unwrap this env.
__init__(nr_nodes, p=0.5, directed=False, gen_method='edge', np_random=None, seed=None)[source]#

Initialize the environment.

Parameters:

  • nr_nodes (int) – the number of nodes in the graph.

  • p (float) – parameter for random generation. (Default: 0.5) - (edge method): The probability that an edge doesn’t exist in directed graph. - (dnc method): Control the range of the sample of out-degree. - other methods: Unused.

  • directed (bool) – directed or Undirected graph. Default: False (undirected)

  • gen_method (str) – use which method to randomly generate a graph. - ‘edge’: By sampling the existence of each edge. - ‘dnc’: Sample out-degree (\(m\)) of each node, and link to nearest neighbors in the unit square. - ‘list’: generate a chain-like graph.

  • np_random (RandomState | None)

  • seed (int | None)

__new__(**kwargs)#
close()#

Override close in your subclass to perform any necessary cleanup.

Environments will automatically close() themselves when garbage collected or when the program exits.

abstract render(mode='human')#

Renders the environment.

The set of supported modes varies per environment. (And some third-party environments may not support rendering at all.) By convention, if mode is:

  • human: render to the current display or terminal and return nothing. Usually for human consumption.

  • rgb_array: Return an numpy.ndarray with shape (x, y, 3), representing RGB values for an x-by-y pixel image, suitable for turning into a video.

  • ansi: Return a string (str) or StringIO.StringIO containing a terminal-style text representation. The text can include newlines and ANSI escape sequences (e.g. for colors).

Note

Make sure that your class’s metadata ‘render_modes’ key includes

the list of supported modes. It’s recommended to call super() in implementations to use the functionality of this method.

Parameters:

mode (str) – the mode to render with

Example:

class MyEnv(Env):

metadata = {‘render_modes’: [‘human’, ‘rgb_array’]}

def render(self, mode=’human’):
if mode == ‘rgb_array’:

return np.array(…) # return RGB frame suitable for video

elif mode == ‘human’:

… # pop up a window and render

else:

super(MyEnv, self).render(mode=mode) # just raise an exception

abstract reset(*, seed=None, return_info=False, options=None)#

Resets the environment to an initial state and returns an initial observation.

This method should also reset the environment’s random number generator(s) if seed is an integer or if the environment has not yet initialized a random number generator. If the environment already has a random number generator and reset is called with seed=None, the RNG should not be reset. Moreover, reset should (in the typical use case) be called with an integer seed right after initialization and then never again.

Returns:

the initial observation. info (optional dictionary): a dictionary containing extra information, this is only returned if return_info is set to true

Return type:

observation (object)

Parameters:

  • seed (int | None)

  • return_info (bool)

  • options (dict | None)

seed(seed=None)#

Sets the seed for this env’s random number generator(s).

Note

Some environments use multiple pseudorandom number generators. We want to capture all such seeds used in order to ensure that there aren’t accidental correlations between multiple generators.

Returns:

Returns the list of seeds used in this env’s random

number generators. The first value in the list should be the “main” seed, or the value which a reproducer should pass to ‘seed’. Often, the main seed equals the provided ‘seed’, but this won’t be true if seed=None, for example.

Return type:

list<bigint>

step(action)#

Run one timestep of the environment’s dynamics. When end of episode is reached, you are responsible for calling reset() to reset this environment’s state.

Accepts an action and returns a tuple (observation, reward, done, info).

Parameters:

action (Any) – an action provided by the environment

Returns:

agent’s observation of the current environment reward: amount of reward returned after previous action done: whether the episode has ended, in which case further step() calls will return undefined results info: contains auxiliary diagnostic information (helpful for debugging, and sometimes learning)

Return type:

observation

property action_space#
property graph#

The generated graph.

metadata = {'render_modes': []}#
property np_random: RandomState#

Initializes the np_random field if not done already.

property observation_space#
reward_range = (-inf, inf)#
spec = None#
property unwrapped: Env#

Completely unwrap this env.

Returns:

The base non-wrapped gym.Env instance

Return type:

gym.Env

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