def test_model(self):
env = MockEnv(3)
env.max_steps = 18
keras_model = conv1_model(env)
model = KerasModel(env, model=keras_model, batch_size=1)
model.epochs = 30
obs1 = np.array([[2, 0, 0], [0, 0, 0], [0, 0, 1]])
expected1 = 13
# Get the current value for obs1 predicted by the model and update the value for action EAST
updated_state_values = model.state_values(obs1)
updated_state_values[EAST] = expected1
# Update the model, calculate and check mean square error
model.update_action_value(obs1, EAST, expected1)
predicted = model.state_values(obs1)
mean_square_error1 = ((predicted - updated_state_values)**2).mean()
# Second round with the same training data
model.update_action_value(obs1, EAST, expected1)
predicted = model.state_values(obs1)
mean_square_error2 = ((predicted - updated_state_values)**2).mean()
# Check that the error decreased
self.assertGreater(mean_square_error1, mean_square_error2)
def __init__(self):
super().__init__()
self.env = CleanBotEnv(4)
self.model = KerasModel(self.env, model=conv1_model(self.env), batch_size=64)
self.training_policy = EpsilonGreedyPolicy(self.model, 0.1)
self.testing_policy = GreedyPolicy(self.model)
self.method = Sarsa(self.env, self.model, self.training_policy)
self.training_policy.exploration = 0.1
self.env.max_steps = 32
self.method.alpha = 0.01
self.model.epochs = 60
def test_smoke(self):
np.random.seed(643674)
env = CleanBotEnv(3)
keras_model = conv1_model(env)
model = KerasModel(env, model=keras_model, batch_size=7)
policy = EpsilonGreedyPolicy(model, 0.1)
mc = AlphaMC(env, model, policy)
policy.exploration = 0.1
episode_count = 1
# for i in range(episode_count):
mc.run_episode()
def __init__(self, epochs, alpha, batch_size):
super().__init__()
self.env = CleanBotEnv(4)
self.model = KerasModel(self.env,
model=conv1_model(self.env),
batch_size=batch_size)
self.training_policy = EpsilonGreedyPolicy(self.model, 0.1)
self.testing_policy = GreedyPolicy(self.model)
self.method = AlphaMC(self.env, self.model, self.training_policy)
self.name = f"{type(self).__name__}-{self.batch_size:03}-{self.model.epochs:03}-{self.method.alpha:.2f}"
self.training_policy.exploration = 0.1
self.env.max_steps = 32
self.method.alpha = alpha
self.model.epochs = epochs
self.batch_size = batch_size