def run_large_experiment():
num_dcs = 10
num_customers = 50
num_commodities = 5
orders_per_day = 10
dcs_per_customer = 3
demand_mean = 200
demand_var = 20
num_steps = 30
num_episodes = 5
runner_random = experiment_runner.create_random_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
runner_dqn = experiment_runner.create_dqn_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
runner_zero = experiment_runner.create_alwayszero_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
runner_bestfit = experiment_runner.create_bestfit_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
#runner_random.run_episodes(num_steps, num_episodes, orders_per_day, experiment_name='dumb_agent')
runner_dqn.run_episodes(num_steps,
num_episodes,
orders_per_day,
experiment_name='dqn_agent')
def run_bestfit():
print("===RUNNING BESTFIT===")
runner_bestfit = experiment_runner.create_bestfit_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
runner_bestfit.run_episodes(num_steps,
num_episodes,
orders_per_day,
experiment_name=f"bestfit_few_warehouses_v2")
print("===DONE BESTFIT===")
def run_bestfit():
print("===RUNNING BESTFIT===")
reproducibility.set_seeds(0)
print("Check this array to ensure reproducibility")
print("Reproducibility BESTFIT", np.random.randint(0, 500, size=(5, 1)))
runner_bestfit = experiment_runner.create_bestfit_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
runner_bestfit.run_episodes(num_steps,
num_episodes,
orders_per_day,
experiment_name=f"bestfit_few_warehouses_v3")
print("===DONE BESTFIT===")
def small_validation_experiments():
num_dcs = 3
num_customers = 2
num_commodities = 4
orders_per_day = 2
dcs_per_customer = 3
demand_mean = 100
demand_var = 20
num_steps = 50
num_episodes = 5
# runner_random = experiment_runner.create_random_experiment_runner(num_dcs, num_customers, dcs_per_customer,
# demand_mean, demand_var, num_commodities,
# orders_per_day, num_steps)
# runner_dqn = experiment_runner.create_dqn_experiment_runner(num_dcs, num_customers, dcs_per_customer, demand_mean,
# demand_var, num_commodities, orders_per_day, num_steps)
# runner_zero = experiment_runner.create_alwayszero_experiment_runner(num_dcs, num_customers, dcs_per_customer,
# demand_mean,
# demand_var, num_commodities, orders_per_day,
# num_steps)
runner_bestfit = experiment_runner.create_bestfit_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
runner_randomvalid = experiment_runner.create_randomvalid_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
# runner = experiment_runner.create_always_first_dc_agent(num_dcs, num_customers,dcs_per_customer,demand_mean,demand_var,num_commodities,orders_per_day,num_steps)
# runner_random.run_episodes(num_steps, num_episodes, orders_per_day, experiment_name='dumb_agent_unittest')
#runner_dqn.run_episodes(num_steps, num_episodes, orders_per_day, experiment_name='dqn_agent_unittest')
# runner_zero.run_episodes(num_steps, num_episodes, orders_per_day, experiment_name='zero_agent_unittest')
#runner_bestfit.run_episodes(num_steps, num_episodes, orders_per_day, experiment_name='bestfit_agent_unittest')
runner_randomvalid.run_episodes(num_steps,
num_episodes,
orders_per_day,
experiment_name='bestfit_agent_unittest')
num_customers,
dcs_per_customer,
demand_mean,
demand_var,
num_commodities,
orders_per_day,
num_steps,
))
# runner_random = experiment_runner.create_random_experiment_runner(
# num_dcs,
# num_customers,
# dcs_per_customer,
# demand_mean,
# demand_var,
# num_commodities,
# orders_per_day,
# num_steps,
# )
runner_bestfit = experiment_runner.create_bestfit_experiment_runner(
num_dcs, num_customers, dcs_per_customer, demand_mean, demand_var,
num_commodities, orders_per_day, num_steps)
# runner_random.run_episodes(
# num_steps, num_episodes, orders_per_day, experiment_name=f"dumb_agent_experiment_{i}"
# )
runner_bestfit.run_episodes(
num_steps,
num_episodes,
orders_per_day,
experiment_name=f"bestfit_agent_experiment_{i}")