def test_tensorflow_action_interpretation():
"""
Tests that tuples of TensorFlow tensors can be handled correctly by the environment.
"""
# Set up the environment parameters.
alpha1, alpha3 = 2, 2
mu1, mu2, mu3, mu4 = 10, 3, 10, 3
cost_per_buffer = np.ones((4, 1))
initial_state = (0, 0, 0, 0)
capacity = np.ones((4, 1)) * np.inf
job_conservation_flag = True
seed = 72
demand_rate = np.array([alpha1, 0, alpha3, 0])[:, None]
buffer_processing_matrix = np.array([[-mu1, 0, 0, 0],
[mu1, -mu2, 0, 0],
[0, 0, -mu3, 0],
[0, 0, mu3, -mu4]])
constituency_matrix = np.array([[1, 0, 0, 1],
[0, 1, 1, 0]])
# Construct environment.
job_generator = ScaledBernoulliServicesPoissonArrivalsGenerator(
demand_rate, buffer_processing_matrix, job_gen_seed=seed)
state_initialiser = stinit.DeterministicCRWStateInitialiser(initial_state)
env = RLControlledRandomWalk(cost_per_buffer, capacity, constituency_matrix, job_generator,
state_initialiser, job_conservation_flag)
# Set up a TensorFlow action tuple with an action for each resource and ensure that the
# environment is able to interpret it and update itself.
action_tuple = (tf.convert_to_tensor([0, 1, 0]), tf.convert_to_tensor([0, 0, 1]))
assert np.all(env.preprocess_action(action_tuple) == np.array([[1], [0], [1], [0]]))
def test_simple_tensorflow_action_interpretation():
"""
Test interpretation of action where there is only one action space. This is done in the single
server queue setting so that there is one resource and therefore one action space.
This tests actions as TensorFlow arrays.
"""
# Set up single server queue.
cost_per_buffer = np.ones((1, 1))
initial_state = (0,)
capacity = np.ones((1, 1)) * np.inf
demand_rate_val = 0.7
job_conservation_flag = True
seed = 72
demand_rate = np.array([demand_rate_val])[:, None]
buffer_processing_matrix = - np.ones((1, 1))
constituency_matrix = np.ones((1, 1))
list_boundary_constraint_matrices = [constituency_matrix]
# Construct environment.
job_generator = ScaledBernoulliServicesPoissonArrivalsGenerator(
demand_rate, buffer_processing_matrix, job_gen_seed=seed)
assert job_generator.routes == {}
state_initialiser = stinit.DeterministicCRWStateInitialiser(initial_state)
env = RLControlledRandomWalk(cost_per_buffer, capacity, constituency_matrix, job_generator,
state_initialiser, job_conservation_flag,
list_boundary_constraint_matrices)
action = tf.convert_to_tensor([0, 1], tf.float32)
assert env.preprocess_action(action) == np.array([1])