def test_expected_cumulative_regret_different_priors(self):
num_jobs = 200
num_workers = 2 * num_jobs
prior_1_worker_list = np.linspace(0.1, 0.9, 9)
reward_completed = 1
reward_not_completed = 0
num_rounds = num_jobs
num_simulations = 100
for prior in prior_1_worker_list:
or_jobs_environment_list = [
environment.AllOrJobs(num_jobs, num_workers, prior,
reward_completed, reward_not_completed)
for _ in range(num_simulations)
]
one_chain = policy.OneChain(num_jobs)
simulations = assignment_problem_simulation.MultipleEnvironments()
simulations.simulation(num_rounds, one_chain,
or_jobs_environment_list)
simulations.averaging_simulations()
self.assertEqual(
abs(simulations.get_regret_hist()[-1]) < 1e-10, True)
plt.plot(simulations.get_cumulative_regret())
plt.legend(
["p = " + str(round(prior, 2)) for prior in prior_1_worker_list])
plt.xlabel("round")
plt.title(
"expected cumulative regret of One-Chain algorithm for OR-jobs")
plt.show()
def test_expected_reward_different_priors(self):
num_jobs = 200
num_workers = 2 * num_jobs
prior_1_worker_list = np.linspace(0.1, 0.9, 9)
reward_completed = 1
reward_not_completed = 0
num_rounds = 1
num_simulations = 100
random_policy_reward_list = []
for prior in prior_1_worker_list:
or_jobs_environment_list = [
environment.AllOrJobs(num_jobs, num_workers, prior,
reward_completed, reward_not_completed)
for _ in range(num_simulations)
]
one_chain = policy.OneChain(num_jobs)
simulations = assignment_problem_simulation.MultipleEnvironments()
simulations.simulation(num_rounds, one_chain,
or_jobs_environment_list)
simulations.averaging_simulations()
random_policy_reward_list.append(simulations.get_reward_hist()[0])
math_formula = [
num_jobs * prior * (2 - prior) / num_workers
for prior in prior_1_worker_list
]
plt.plot(prior_1_worker_list, random_policy_reward_list)
plt.plot(prior_1_worker_list, math_formula, '--')
plt.legend(["simulation", "math formula"], loc='lower right')
plt.title("expected reward of random policy")
plt.xlabel("prior of type-1 worker")
plt.show()
def run_experiment(self):
for prior in self.prior_list:
or_jobs_environment_list = [
environment.AllOrJobs(self.num_jobs, self.num_workers, prior,
self.reward_completed,
self.reward_not_completed)
for _ in range(self.num_simulations)
]
two_chain = policy.TwoChain(self.num_jobs)
simulations = assignment_problem_simulation.MultipleEnvironments()
simulations.simulation(self.num_rounds, two_chain,
or_jobs_environment_list)
simulations.averaging_simulations()
# updating
self.instant_regrets.append(simulations.get_regret_hist())
self.cumulative_regrets.append(simulations.get_cumulative_regret())
self.total_regret.append(simulations.get_cumulative_regret()[-1])
def test_expected_regret_fixed_prior(self):
num_jobs = 200
num_workers = 2 * num_jobs
prior_1_worker = 0.5
reward_completed = 1
reward_not_completed = 0
num_rounds = num_jobs
num_simulations = 100
or_jobs_environment_list = [
environment.AllOrJobs(num_jobs, num_workers, prior_1_worker,
reward_completed, reward_not_completed)
for _ in range(num_simulations)
]
one_chain = policy.OneChain(num_jobs)
simulations = assignment_problem_simulation.MultipleEnvironments()
simulations.simulation(num_rounds, one_chain, or_jobs_environment_list)
simulations.averaging_simulations()
self.assertEqual(abs(simulations.get_regret_hist()[-1]) < 1e-10, True)
plt.plot(simulations.get_regret_hist())
plt.show()