Median Agent

class or_suite.agents.ambulance.median.medianAgent(epLen)

Agent that implements a median-like heuristic algorithm for the metric environment. The data on previous call locations is sorted by location and split into k quantiles, where k is the number of ambulances. The midpoint of each quantile is chosen as the location for one of the ambulances.

reset()

clears data and call_locs which contain data on what has occurred so far in the environment

update_config()

(UNIMPLEMENTED)

pick_action(state, step)

locations are chosen by finding the the midpoints of each of k quantiles of the arrival data sorted by location, where k is the number of ambulances

epLen

(int) number of time steps to run the experiment for

data

(float list list) a list of all the states of the environment observed so far

call_locs

(float list) the locations of all calls observed so far

__init__(epLen)

Parameters

epLen – (int) number of time steps to run the experiment for

greedy(state, timestep, epsilon=0)

This algorithm sorts all previous arrivals, and splits them into k quantiles, where k is the number of ambulances. It then finds the median of each quantile, and positions an ambulance there.

update_obs(obs, action, reward, newObs, timestep, info)

Adds newObs, the most recently observed state, to data adds the most recent call arrival, found in info[‘arrival’] to call_locs.

update_policy(k)

Update internal policy based upon records.

Not used, because a greedy algorithm does not have a policy.