Resource Allocation Discrete

Discrete Sequential Resource Allocation Problem for n locations with K commodities.

Currently reward is Nash Social Welfare but in the future will integrate more options to determine a fair allocation.

class or_suite.envs.resource_allocation.resource_allocation_discrete.DiscreteResourceAllocationEnvironment(config={'K': 2, 'MAX_VAL': 1000, 'from_data': False, 'init_budget': <function <lambda>>, 'num_rounds': 10, 'type_dist': <function <lambda>>, 'utility_function': <function <lambda>>, 'weight_matrix': array([[1. , 2. ],        [0.3, 9. ],        [1. , 1. ]])})

Custom Environment that follows gym interface.

__init__(config={'K': 2, 'MAX_VAL': 1000, 'from_data': False, 'init_budget': <function <lambda>>, 'num_rounds': 10, 'type_dist': <function <lambda>>, 'utility_function': <function <lambda>>, 'weight_matrix': array([[1. , 2. ],        [0.3, 9. ],        [1. , 1. ]])})

Initializes the Discrete Sequential Resource Allocation Environment.

Parameters

• weight_matrix – Weights predefining the commodity needs for each type, every row is a type vector.

• K – Number of commodities.

• num_rounds – Number of agents (also the length of an episode).

• init_budget – Amount of each commodity the principal begins with.

• type_dist – Function determining the number of people of each type at a location.

• u – Utility function, given an allocation x and a type theta, u(x,theta) is how good the fit is.

close()

Override close in your subclass to perform any necessary cleanup.

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

render(mode='console')

Renders the environment.

The set of supported modes varies per environment. (And some environments do 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

reset()

Important: the observation must be a numpy array Returns: np.array

step(action)

Move one step in the environment.

Parameters

action – A matrix; the chosen action (each row how much to allocate to prev location).

Returns

reward: double; the reward.

newState: int; the new state.

done: 0/1; the flag for end of the episode.

info: dict; any additional information.

Return type

double, int, 0/1, dict