def test_failure_window(self, mock_time):
config = AgentConfig("", "")
original_config = config.get(AgentConfig.COMMAND_SECTION, AgentConfig.AUTO_RESTART)
config.set(AgentConfig.COMMAND_SECTION, AgentConfig.AUTO_RESTART, '2,1')
## The behavior of side_effect is different when you run tests in command line and when you do it through IDE
## So few extra items are there in the list
mock_time.side_effect = [200, 500, 500]
controller5 = Controller.Controller(config)
try:
self.assertTrue(controller5.shouldAutoRestart())
self.assertTrue(controller5.shouldAutoRestart())
finally:
config.set(AgentConfig.COMMAND_SECTION, AgentConfig.AUTO_RESTART, original_config)
def setUp(self, hostname_method, NetUtil_mock, lockMock, threadMock):
Controller.logger = MagicMock()
lockMock.return_value = MagicMock()
NetUtil_mock.return_value = MagicMock()
hostname_method.return_value = "test_hostname"
config = MagicMock()
config.get.return_value = "something"
config.getResolvedPath.return_value = "something"
self.controller = Controller.Controller(config)
self.controller.netutil.MINIMUM_INTERVAL_BETWEEN_HEARTBEATS = 0.1
self.controller.netutil.HEARTBEAT_NOT_IDDLE_INTERVAL_SEC = 0.1
def test_failure_window2(self):
config = MagicMock()
config.getErrorWindow.return_value = (0, 0)
controller = Controller.Controller(config)
self.assertTrue(controller.shouldAutoRestart())
config.getErrorWindow.return_value = (0, 1)
self.assertTrue(controller.shouldAutoRestart())
config.getErrorWindow.return_value = (1, 0)
self.assertTrue(controller.shouldAutoRestart())
config.getErrorWindow.return_value = (-1, -1)
self.assertTrue(controller.shouldAutoRestart())
config.getErrorWindow.return_value = (1, 1)
self.assertTrue(controller.shouldAutoRestart())
#second failure within a minute
self.assertFalse(controller.shouldAutoRestart())
#do not reset unless window expires
self.assertFalse(controller.shouldAutoRestart())
from agent import Controller
# Load controller
# E10000: (S?/R0/A?0, S1/R1/A80 ,S2/R2/A90, S1/R3/A90)
# E5000: (S4/R0/A80, S4/R1/A70 ,S4/R2/A60, S4/R3/A10)
SEED = 4
REWARD_TYPE = 2
controller = Controller(rand_seed=SEED, rew_type=REWARD_TYPE)
# Training
NUM_EPISODES = 10000
MAX_TIME_STEPS = 150
MODEL_SAVE_NAME = 'reacher'
# controller.train(num_episodes = NUM_EPISODES, max_timesteps = MAX_TIME_STEPS, model_name = MODEL_SAVE_NAME)
# Testing
NUM_TESTS = 10
MODEL_LOAD_NAME = 'reacher_' + str(NUM_EPISODES) + '_' + str(REWARD_TYPE)
controller.test(num_test=NUM_TESTS,
max_timesteps=MAX_TIME_STEPS,
model_name=MODEL_LOAD_NAME)
else: device_id = int(args.device_id) from Environment import Environment env = Environment(task=task,seed=seed) num_actions = env.env.action_space.n # create expereince memory from memory import ExperienceMemory experience_memory = ExperienceMemory(size=batch_size) # create agent from agent import Controller controller = Controller(experience_memory=experience_memory, num_actions=num_actions, batch_size=batch_size, seed=seed, use_multiple_gpu=use_multiple_gpu, device_id=device_id) # create quasi-Newton optimizier from quasi_newton import QUASI_NEWTON qn = QUASI_NEWTON(controller=controller, m=m, search_method=search_method, quasi_newton_matrix=quasi_newton_matrix, seed=seed) # create the trainer from trainer import Trainer atari_trainer = Trainer(env=env, controller=controller,
lr = float(args.lr)
optim = args.optim
max_iter = int(args.max_iter)
from Environment import Environment
env = Environment(task=task)
num_actions = env.env.action_space.n
print('number of actions: ', num_actions)
# create expereince memory
from memory import ExperienceMemory
experience_memory = ExperienceMemory()
# create agent
from agent import Controller
controller = Controller(experience_memory=experience_memory,
num_actions=num_actions,
lr=lr,
batch_size=batch_size,
optim_method=optim,
use_multiple_gpu=False)
# create the trainer
from trainer import Trainer
atari_trainer = Trainer(env=env,
controller=controller,
experience_memory=experience_memory,
max_iter=max_iter)
# run the training loop
atari_trainer.train()