def test_ppo_model_cc_vector_rnn(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
memory_size = 128
model = PPOModel(env.brains["RealFakeBrain"],
use_recurrent=True,
m_size=memory_size)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output, model.all_log_probs, model.value, model.entropy,
model.learning_rate, model.memory_out
]
feed_dict = {
model.batch_size: 1,
model.sequence_length: 2,
model.memory_in: np.zeros((1, memory_size)),
model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.epsilon: np.array([[0, 1]])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def test_ppo_model_cc_visual(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=2)
env = UnityEnvironment(' ')
model = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output, model.log_probs, model.value, model.entropy,
model.learning_rate
]
feed_dict = {
model.batch_size: 2,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]]),
model.visual_in[0]: np.ones([2, 40, 30, 3]),
model.visual_in[1]: np.ones([2, 40, 30, 3]),
model.epsilon: np.array([[0, 1], [2, 3]])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def test_ppo_model_dc_vector_curio(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=0)
env = UnityEnvironment(' ')
model = PPOModel(env.brains["RealFakeBrain"], use_curiosity=True)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output, model.all_log_probs, model.value, model.entropy,
model.learning_rate, model.intrinsic_reward
]
feed_dict = {
model.batch_size:
2,
model.sequence_length:
1,
model.vector_in:
np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.next_vector_in:
np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.action_holder: [[0], [0]],
model.action_masks:
np.ones([2, 2])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
def test_step(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain = env.brains['RealFakeBrain']
brain_info = env.reset()
brain_info = env.step([0] * brain.vector_action_space_size[0] * len(brain_info['RealFakeBrain'].agents))
with pytest.raises(UnityActionException):
env.step([0])
brain_info = env.step([-1] * brain.vector_action_space_size[0] * len(brain_info['RealFakeBrain'].agents))
with pytest.raises(UnityActionException):
env.step([0] * brain.vector_action_space_size[0] * len(brain_info['RealFakeBrain'].agents))
env.close()
assert env.global_done
assert isinstance(brain_info, dict)
assert isinstance(brain_info['RealFakeBrain'], BrainInfo)
assert isinstance(brain_info['RealFakeBrain'].visual_observations, list)
assert isinstance(brain_info['RealFakeBrain'].vector_observations, np.ndarray)
assert len(brain_info['RealFakeBrain'].visual_observations) == brain.number_visual_observations
assert len(brain_info['RealFakeBrain'].vector_observations) == \
len(brain_info['RealFakeBrain'].agents)
assert len(brain_info['RealFakeBrain'].vector_observations[0]) == \
brain.vector_observation_space_size * brain.num_stacked_vector_observations
print("\n\n\n\n\n\n\n" + str(brain_info['RealFakeBrain'].local_done))
assert not brain_info['RealFakeBrain'].local_done[0]
assert brain_info['RealFakeBrain'].local_done[2]
def test_initialization(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
with pytest.raises(UnityActionException):
env.step([0])
assert env.brain_names[0] == 'RealFakeBrain'
env.close()
def test_close(mock_communicator, mock_launcher):
comm = MockCommunicator(
discrete_action=False, visual_inputs=0)
mock_communicator.return_value = comm
env = UnityEnvironment(' ')
assert env._loaded
env.close()
assert not env._loaded
assert comm.has_been_closed
def test_ppo_policy_evaluate(mock_communicator, mock_launcher, dummy_config):
tf.reset_default_graph()
mock_communicator.return_value = MockCommunicator(discrete_action=False,
visual_inputs=0)
env = UnityEnvironment(' ')
brain_infos = env.reset()
brain_info = brain_infos[env.brain_names[0]]
trainer_parameters = dummy_config
model_path = env.brain_names[0]
trainer_parameters['model_path'] = model_path
trainer_parameters['keep_checkpoints'] = 3
policy = PPOPolicy(0, env.brains[env.brain_names[0]], trainer_parameters,
False, False)
run_out = policy.evaluate(brain_info)
assert run_out['action'].shape == (3, 2)
env.close()
def test_reset(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
brain = env.brains['RealFakeBrain']
brain_info = env.reset()
env.close()
assert not env.global_done
assert isinstance(brain_info, dict)
assert isinstance(brain_info['RealFakeBrain'], BrainInfo)
assert isinstance(brain_info['RealFakeBrain'].visual_observations, list)
assert isinstance(brain_info['RealFakeBrain'].vector_observations, np.ndarray)
assert len(brain_info['RealFakeBrain'].visual_observations) == brain.number_visual_observations
assert len(brain_info['RealFakeBrain'].vector_observations) == \
len(brain_info['RealFakeBrain'].agents)
assert len(brain_info['RealFakeBrain'].vector_observations[0]) == \
brain.vector_observation_space_size * brain.num_stacked_vector_observations
def test_cc_bc_model(mock_communicator, mock_launcher):
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
mock_communicator.return_value = MockCommunicator(
discrete_action=False, visual_inputs=0)
env = UnityEnvironment(' ')
model = BehavioralCloningModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.sample_action, model.policy]
feed_dict = {
model.batch_size: 2,
model.sequence_length: 1,
model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
[3, 4, 5, 3, 4, 5]])
}
sess.run(run_list, feed_dict=feed_dict)
env.close()
