Fixed Threshold Agent
- class or_suite.agents.resource_allocation.fixed_threshold.fixedThresholdAgent(epLen, env_config)[source]
Fixed Guardrail provides lower thresholds on budget distribution calculated by solving the primal-dual paradigm of Eisenberg-Gale Convex Progam
- generate_cvxpy_solver()[source]
Creates a generic solver to solve the offline resource allocation problem.
- get_lower_upper_sol(init_size)[source]
Uses solver to get the lower threshold on budget distribution
- get_expected_endowments(N=1000)[source]
MCM for estimating Expectation of type distribution using N realizations.
- num_types
Number of types
- Type
int
- num_resources
Number of commodities
- Type
int
- budget_remaining
Amount of each commodity the principal begins with.
- Type
int
- scale
Hyperparameter to be used in calculating threshold
- Type
int
- epLen
Number of locations (also the length of an episode).
- Type
int
- data
All data observed so far
- Type
list
- first_allocation_done
Flag that if false, gets upper and lower thresh
- Type
bool
- conf_const
Hyperparameter for confidence bound
- Type
int
- exp_endowments
Matrix containing expected proportion of endowments for location t
- Type
list
- stdev_endowments
Matrix describing variance of exp_endowments
- Type
list
- prob
CVXPY problem object
- Type
cvxpy object
- solver
Function that solves the problem given data
- Type
lambda function
- lower_sol
Matrix of lower threshold
- Type
np.array
- upper_sol
Matrix of upper threshold
- Type
np.array
- __init__(epLen, env_config)[source]
Initialize fixed_threshold agent
- Parameters
epLen – number of steps
env_config – parameters used in initialization of environment
scale – hyperparameter to be used in calculating threshold
- generate_cvxpy_solver()[source]
Creates a generic solver to solve the offline resource allocation problem
- Returns
prob - CVXPY problem object solver - function that solves the problem given data
- get_expected_endowments(N=1000)[source]
Monte Carlo Method for estimating Expectation of type distribution using N realizations Only need to run this once to get expectations for all locations
- Returns
rel_exp_endowments - matrix containing expected proportion of endowments for location t
- pick_action(state, step)[source]
Returns allocation of resources based on calculated upper and lower solutions
- Parameters
state – vector with first K entries denoting remaining budget, and remaining n entires denoting the number of people of each type that appears
step – timestep
- Returns
matrix where each row is a K-dimensional vector denoting how much of each commodity is given to each type