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test_unitytrainers.py
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import yaml
import unittest.mock as mock
import pytest
from unitytrainers.trainer_controller import TrainerController
from unitytrainers.buffer import Buffer
from unitytrainers.models import *
from unitytrainers.ppo.trainer import PPOTrainer
from unitytrainers.bc.trainer import BehavioralCloningTrainer
from unityagents import UnityEnvironmentException
from .mock_communicator import MockCommunicator
dummy_start = '''{
"AcademyName": "RealFakeAcademy",
"resetParameters": {},
"brainNames": ["RealFakeBrain"],
"externalBrainNames": ["RealFakeBrain"],
"logPath":"RealFakePath",
"apiNumber":"API-3",
"brainParameters": [{
"vectorObservationSize": 3,
"numStackedVectorObservations" : 2,
"vectorActionSize": 2,
"memorySize": 0,
"cameraResolutions": [],
"vectorActionDescriptions": ["",""],
"vectorActionSpaceType": 1,
"vectorObservationSpaceType": 1
}]
}'''.encode()
dummy_config = yaml.load('''
default:
trainer: ppo
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
use_curiosity: false
curiosity_strength: 0.0
curiosity_enc_size: 1
''')
dummy_bc_config = yaml.load('''
default:
trainer: imitation
brain_to_imitate: ExpertBrain
batches_per_epoch: 16
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
use_curiosity: false
curiosity_strength: 0.0
curiosity_enc_size: 1
''')
dummy_bad_config = yaml.load('''
default:
trainer: incorrect_trainer
brain_to_imitate: ExpertBrain
batches_per_epoch: 16
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
gamma: 0.99
hidden_units: 128
lambd: 0.95
learning_rate: 3.0e-4
max_steps: 5.0e4
normalize: true
num_epoch: 5
num_layers: 2
time_horizon: 64
sequence_length: 64
summary_freq: 1000
use_recurrent: false
memory_size: 8
''')
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_initialization(mock_communicator, mock_launcher):
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=1)
tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
1, 1, 1, '', "tests/test_unitytrainers.py", False)
assert(tc.env.brain_names[0] == 'RealFakeBrain')
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_load_config(mock_communicator, mock_launcher):
open_name = 'unitytrainers.trainer_controller' + '.open'
with mock.patch('yaml.load') as mock_load:
with mock.patch(open_name, create=True) as _:
mock_load.return_value = dummy_config
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=1)
mock_load.return_value = dummy_config
tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
1, 1, 1, '','', False)
config = tc._load_config()
assert(len(config) == 1)
assert(config['default']['trainer'] == "ppo")
@mock.patch('unityagents.UnityEnvironment.executable_launcher')
@mock.patch('unityagents.UnityEnvironment.get_communicator')
def test_initialize_trainers(mock_communicator, mock_launcher):
open_name = 'unitytrainers.trainer_controller' + '.open'
with mock.patch('yaml.load') as mock_load:
with mock.patch(open_name, create=True) as _:
mock_communicator.return_value = MockCommunicator(
discrete_action=True, visual_inputs=1)
tc = TrainerController(' ', ' ', 1, None, True, True, False, 1,
1, 1, 1, '', "tests/test_unitytrainers.py", False)
# Test for PPO trainer
mock_load.return_value = dummy_config
config = tc._load_config()
tf.reset_default_graph()
with tf.Session() as sess:
tc._initialize_trainers(config, sess)
assert(len(tc.trainers) == 1)
assert(isinstance(tc.trainers['RealFakeBrain'], PPOTrainer))
# Test for Behavior Cloning Trainer
mock_load.return_value = dummy_bc_config
config = tc._load_config()
tf.reset_default_graph()
with tf.Session() as sess:
tc._initialize_trainers(config, sess)
assert(isinstance(tc.trainers['RealFakeBrain'], BehavioralCloningTrainer))
# Test for proper exception when trainer name is incorrect
mock_load.return_value = dummy_bad_config
config = tc._load_config()
tf.reset_default_graph()
with tf.Session() as sess:
with pytest.raises(UnityEnvironmentException):
tc._initialize_trainers(config, sess)
def assert_array(a, b):
assert a.shape == b.shape
la = list(a.flatten())
lb = list(b.flatten())
for i in range(len(la)):
assert la[i] == lb[i]
def test_buffer():
b = Buffer()
for fake_agent_id in range(4):
for step in range(9):
b[fake_agent_id]['vector_observation'].append(
[100 * fake_agent_id + 10 * step + 1,
100 * fake_agent_id + 10 * step + 2,
100 * fake_agent_id + 10 * step + 3]
)
b[fake_agent_id]['action'].append([100 * fake_agent_id + 10 * step + 4,
100 * fake_agent_id + 10 * step + 5])
a = b[1]['vector_observation'].get_batch(batch_size=2, training_length=1, sequential=True)
assert_array(a, np.array([[171, 172, 173], [181, 182, 183]]))
a = b[2]['vector_observation'].get_batch(batch_size=2, training_length=3, sequential=True)
assert_array(a, np.array([
[[231, 232, 233], [241, 242, 243], [251, 252, 253]],
[[261, 262, 263], [271, 272, 273], [281, 282, 283]]
]))
a = b[2]['vector_observation'].get_batch(batch_size=2, training_length=3, sequential=False)
assert_array(a, np.array([
[[251, 252, 253], [261, 262, 263], [271, 272, 273]],
[[261, 262, 263], [271, 272, 273], [281, 282, 283]]
]))
b[4].reset_agent()
assert len(b[4]) == 0
b.append_update_buffer(3,
batch_size=None, training_length=2)
b.append_update_buffer(2,
batch_size=None, training_length=2)
assert len(b.update_buffer['action']) == 10
assert np.array(b.update_buffer['action']).shape == (10, 2, 2)
if __name__ == '__main__':
pytest.main()