def test_get_horizon():
env = examples.double_reentrant_line_model()
discount_factor = 0.9999
overrides = {
'BigStepPenaltyPolicyParams': {
'boolean_action_flag': True, # This should throw an error.
'convex_solver': 'cvx.CPLEX'
},
'HedgehogHyperParams': {
'activity_rates_policy_class_name': 'BigStepPolicy',
'horizon_mpc_ratio': 0,
'minimum_horizon': 1
}
}
ac_params, wk_params, si_params, po_params, hh_params, _, dp_params, _ \
= get_hedgehog_hyperparams(**overrides)
pf_mip_agent = PureFeedbackMIPHedgehogAgent(
env,
discount_factor,
wk_params,
hh_params,
ac_params,
si_params,
po_params,
demand_planning_params=dp_params)
assert pf_mip_agent.get_horizon() == 1
def test_compare_maxweight_vs_maxweight_scheduling_double_reentrant_line_model(
self):
seed = 42
np.random.seed(seed)
initial_state = 50 * np.ones((5, 1))
env = examples.double_reentrant_line_model(alpha=1,
mu1=4,
mu2=3,
mu3=2,
mu4=3,
mu5=4,
cost_per_buffer=np.ones(
(5, 1)),
initial_state=initial_state,
capacity=np.ones(
(5, 1)) * np.inf,
job_conservation_flag=True,
job_gen_seed=seed,
max_episode_length=None)
smw_agent = smw.SchedulingMaxWeightAgent(env)
mw_agent = mw.MaxWeightAgent(env)
states = np.random.randint(50, size=(400, 5))
for s in states:
z_star_mw = mw_agent.max_weight_policy(s[:, None])
z_star_smw = smw_agent.scheduling_max_weight_policy(s[:, None])
self.assert_both_mw_policies_are_equivalent(
s, env.cost_per_buffer,
env.job_generator.buffer_processing_matrix, z_star_mw,
z_star_smw)
def test_compare_maxweight_vs_maxweight_scheduling(self):
seed = 42
np.random.seed(seed)
initial_state = 50 * np.ones((5, 1))
env = examples.double_reentrant_line_model(initial_state=initial_state,
job_gen_seed=seed)
sch_mw_agent = sch_mw.SchedulingMaxWeightAgent(env)
mw_agent = mw_lp.MaxWeightLpAgent(env)
states = np.random.randint(50, size=(400, 5))
for s in states:
z_star_mw, _ = mw_agent.max_weight_policy(s[:, None])
z_star_sch_mw = sch_mw_agent.scheduling_max_weight_policy(s[:,
None])
self.assert_both_mw_policies_are_equivalent(
s, env.cost_per_buffer,
env.job_generator.buffer_processing_matrix, z_star_mw,
z_star_sch_mw)
def test_get_horizon():
env = examples.double_reentrant_line_model()
discount_factor = 0.9999
overrides = {
'HedgehogHyperParams': {
'horizon_mpc_ratio': 0,
'minimum_horizon': 1
}
}
ac_params, wk_params, si_params, _, hh_params, _, _, dp_params \
= get_hedgehog_hyperparams(**overrides)
pf_mip_agent = PureFeedbackStationaryHedgehogAgent(
env,
discount_factor,
wk_params,
hh_params,
ac_params,
si_params,
demand_planning_params=dp_params)
assert pf_mip_agent.get_horizon() == 1
def test_integration_double_reentrant_line_model(self):
seed = 42
np.random.seed(seed)
initial_state = 50 * np.ones((5, 1))
env = examples.double_reentrant_line_model(
alpha=1,
mu1=4,
mu2=3,
mu3=2,
mu4=3,
mu5=4,
cost_per_buffer=np.array([1, 1, 1, 1, 1])[:, None],
initial_state=initial_state,
capacity=np.ones((5, 1)) * np.inf,
job_conservation_flag=True,
job_gen_seed=seed,
max_episode_length=None)
agent = mw.MaxWeightAgent(env)
simulator = ps.SncSimulator(env, agent, discount_factor=0.95)
data_mw = simulator.run(num_simulation_steps=1000)
assert np.all(data_mw['state'][-1] < initial_state)
def test_set_action_draining_empty_buffer_to_zero_double_reentrant_line_model(
self):
"""
The output of the LP for this example gives nonzero output to action draining buffer 5,
which is empty.
"""
env = examples.double_reentrant_line_model(alpha=0.25,
mu1=1,
mu2=0.75,
mu3=0.5,
mu4=0.75,
mu5=1,
initial_state=5 * np.ones(
(5, 1)))
opt_val = -21.25
z_star = np.array([[1], [0], [1], [1], [1]])
state = np.array([[54], [54], [29], [27], [0]])
diag_cost = np.eye(5)
z_star_zeroed = mw_lp.set_action_draining_empty_buffer_to_zero(
opt_val, z_star, state, env.job_generator.buffer_processing_matrix,
diag_cost)
assert np.all(z_star_zeroed[:-1] == z_star[:-1])
assert z_star_zeroed[4] == 0
def test_create_agent_with_mip_flag_set_to_false():
env = examples.double_reentrant_line_model()
discount_factor = 0.9999
overrides = {
'BigStepPenaltyPolicyParams': {
'boolean_action_flag': False, # This should throw an error.
'convex_solver': 'cvx.CPLEX'
},
'HedgehogHyperParams': {
'activity_rates_policy_class_name': 'BigStepPolicy'
}
}
ac_params, wk_params, si_params, po_params, hh_params, _, dp_params, _ \
= get_hedgehog_hyperparams(**overrides)
with pytest.raises(AssertionError):
_ = PureFeedbackMIPHedgehogAgent(env,
discount_factor,
wk_params,
hh_params,
ac_params,
si_params,
po_params,
demand_planning_params=dp_params)
