def example1(policy_id):
params = Params(
mu=3,
lambda1=4,
lambda2=4,
servers_number=5,
fragments_numbers=[2, 3],
queues_capacities=[3, 3],
)
all_states = get_all_states(params)
states_with_policy = get_policed_states(all_states, params)
policies = get_all_possible_policies(states_with_policy)
# print("All possible policies number: ", len(policies))
selected_policy_vector = None
for idx, p in enumerate(policies):
selected_policy_vector = p
if idx == policy_id:
break
print("Current policy:")
policy_function = Policy(
selected_policy_vector, states_with_policy, params
).get_action_for_state
for state in states_with_policy:
print(f" state = {state}, action = {policy_function(state)}")
bar = ConsoleProgressBar("Progress: ")
model = SplitMergeSystem(params, bar, policy_function)
simulation_time = 10_000
statistics = model.run(simulation_time)
print(statistics)
def example1(policy_id):
params = Params(
mu=3,
lambda1=4,
lambda2=4,
servers_number=5,
fragments_numbers=[2, 3],
queues_capacities=[3, 3],
)
all_states = get_all_states(params)
states_with_policy = get_policed_states(all_states, params)
policies = get_all_possible_policies(states_with_policy)
# print("All possible policies number: ", len(policies))
selected_policy_vector = None
for idx, p in enumerate(policies):
selected_policy_vector = p
if idx == policy_id:
break
print("Current policy:")
policy = Policy(selected_policy_vector, states_with_policy, params)
for state in states_with_policy:
print(
f" state = {state}, action = {policy.get_action_for_state(state)}"
)
calculations = Calculations(params)
calculations.calculate(policy)
performance_measures = calculations.performance_measures
print(performance_measures, "\n")
def example3(queue_id):
params = Params(
mu=3,
lambda1=4,
lambda2=4,
servers_number=6,
fragments_numbers=[3, 2],
queues_capacities=[3, 3],
)
all_states = get_all_states(params)
states_with_policy = get_policed_states(all_states, params)
selected_policy_vector = [queue_id] * len(states_with_policy)
print("Current policy:")
policy = Policy(selected_policy_vector, states_with_policy, params)
for state in states_with_policy:
print(
f" state = {state}, action = {policy.get_action_for_state(state)}"
)
calculations = Calculations(params)
calculations.calculate(policy)
performance_measures = calculations.performance_measures
print(performance_measures, "\n")
def __init__(
self,
model: SplitMergeSystem,
state_with_policy: list,
learning_rate=1,
reward_decay=0.9,
e_greedy=0.4,
progress_bar: ProgressBar = None,
):
self.model = model
self.state_with_policy = state_with_policy
self.actions = model.actions
self.lr = learning_rate
self.gamma = reward_decay
self.epsilon = e_greedy
self._progress_bar = progress_bar
all_states = get_all_states(model.get_params())
self._states_with_policy = get_policed_states(all_states, model.get_params())
self.n_states_with_actions = len(self._states_with_policy)
self._state_id_map = dict()
for id, state in enumerate(self._states_with_policy):
self._state_id_map[str(state)] = id
self._n_actions = 2
self.q_table = np.zeros((self.n_states_with_actions, self._n_actions))
def example1():
params = Params(
mu=3,
lambda1=3,
lambda2=3,
servers_number=6,
fragments_numbers=[2, 3],
queues_capacities=[2, 2],
)
all_states = get_all_states(params)
states_with_policy = get_policed_states(all_states, params)
states_policy = Policy(tuple(), states_with_policy, params)
states_policy.print_adjacent_states()
iterative = IterativeMethod(all_states, states_policy, get_state_reward, params)
iterative.apply(print_iteration_results=True)
print("executed")
def example2():
params = Params(
mu=3,
lambda1=5,
lambda2=5,
servers_number=6,
fragments_numbers=[3, 2],
queues_capacities=[1, 1],
)
all_states = get_all_states(params)
states_with_policy = get_policed_states(all_states, params)
print("All states where policy is possible:")
pprint(states_with_policy)
strategies = get_all_possible_policies(states_with_policy)
states_policy = Policy(tuple(), states_with_policy, params)
states_policy.print_adjacent_states()
storage = PerformanceMeasuresStorage()
print()
for strategy in strategies:
states_policy.policy_vector = strategy
print(strategy)
calculations = Calculations(params)
calculations.calculate(states_policy)
performance_measures = calculations.performance_measures
print(performance_measures, "\n")
storage.append(strategy, performance_measures)
print(storage)
print()
storage.show_difference()
print("executed")
def example1(lambdas, queue_id):
storage = PerformanceMeasuresStorage()
for lambd in lambdas:
params = Params(
mu=3,
lambda1=lambd,
lambda2=lambd,
servers_number=5,
fragments_numbers=[2, 3],
queues_capacities=[3, 3],
)
all_states = get_all_states(params)
states_with_policy = get_policed_states(all_states, params)
selected_policy_vector = [queue_id] * len(states_with_policy)
policy = Policy(selected_policy_vector, states_with_policy, params)
calculations = Calculations(params)
calculations.calculate(policy)
performance_measures = calculations.performance_measures
print(performance_measures, "\n")
storage.append(lambd, performance_measures)
print(storage)
print()
storage.show_difference()
plt.title("Зависимость T от интенсивности входящего потока")
plt.xlabel("lambdas")
plt.ylabel("T")
plt.grid()
plt.plot(lambdas, storage.response_times, "g", linewidth=2, markersize=12)
plt.show()
plt.title("Зависимость T1 и T2 от интенсивности входящего потока")
plt.xlabel("lambdas")
plt.ylabel("T")
plt.grid()
plt.plot(lambdas,
storage.response_times1,
"g",
label="T1",
linewidth=2,
markersize=12)
plt.plot(lambdas,
storage.response_times2,
"b",
label="T2",
linewidth=2,
markersize=12)
plt.legend()
plt.show()
plt.title("Зависимость pf от интенсивности входящего потока")
plt.xlabel("lambdas")
plt.ylabel("pf")
plt.grid()
plt.plot(lambdas,
storage.blocked_all_queues_probability,
"b",
linewidth=2,
markersize=12)
plt.show()
plt.title("Зависимость pf1 и pf2 от интенсивности входящего потока")
plt.xlabel("lambdas")
plt.ylabel("pf")
plt.grid()
plt.plot(
lambdas,
storage.failure_probabilities1,
"g",
label="pf1",
linewidth=2,
markersize=12,
)
plt.plot(
lambdas,
storage.failure_probabilities2,
"b",
label="pf2",
linewidth=2,
markersize=12,
)
plt.legend()
plt.show()
# params = Params(mu=3, lambda1=5, lambda2=5,
# servers_number=6,
# fragments_numbers=[3, 3],
# queues_capacities=[2, 2])
params = Params(
mu=3,
lambda1=3,
lambda2=3,
servers_number=6,
fragments_numbers=[2, 3],
queues_capacities=[2, 2],
)
states = get_all_states(params)
states_with_policy = get_policed_states(states, params)
print("All states where policy is possible:")
pprint(states_with_policy)
states_policy = Policy(tuple(), states_with_policy, params)
states_policy.print_adjacent_states()
model = SplitMergeSystem(params=params, reward_policy=reward_function2)
agent = QLearning(
model,
states_with_policy,
reward_decay=1,
learning_rate=0.3,
e_greedy=0.1,
progress_bar=ConsoleProgressBar("*"),
)