ml-agents/ml-agents/tests/trainers/test_bc.py

import unittest.mock as mock
import pytest

import numpy as np
import tensorflow as tf
import yaml

from mlagents.trainers.bc.models import BehavioralCloningModel
from mlagents.trainers.bc.policy import BCPolicy
from mlagents.envs import UnityEnvironment
from tests.mock_communicator import MockCommunicator


@pytest.fixture
def dummy_config():
    return yaml.load(
        '''
            hidden_units: 128
            learning_rate: 3.0e-4
            num_layers: 2
            use_recurrent: false
            sequence_length: 32
            memory_size: 32
            ''')


@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_bc_policy_evaluate(mock_communicator, mock_launcher):
    tf.reset_default_graph()
    with tf.Session() as sess:
        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()
        graph_scope = env.brain_names[0]
        trainer_parameters['graph_scope'] = graph_scope
        policy = BCPolicy(0, env.brains[env.brain_names[0]], trainer_parameters, sess)
        init = tf.global_variables_initializer()
        sess.run(init)
        run_out = policy.evaluate(brain_info)
        assert run_out['action'].shape == (3, 2)

    env.close()


@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
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()


@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_dc_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=True, visual_inputs=0)
            env = UnityEnvironment(' ')
            model = BehavioralCloningModel(env.brains["RealFakeBrain"])
            init = tf.global_variables_initializer()
            sess.run(init)

            run_list = [model.sample_action, model.action_probs]
            feed_dict = {model.batch_size: 2,
                         model.dropout_rate: 1.0,
                         model.sequence_length: 1,
                         model.vector_in: np.array([[1, 2, 3, 1, 2, 3],
                                                    [3, 4, 5, 3, 4, 5]]),
                         model.action_masks: np.ones([2, 2])}
            sess.run(run_list, feed_dict=feed_dict)
            env.close()


@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_visual_dc_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=True, visual_inputs=2)
            env = UnityEnvironment(' ')
            model = BehavioralCloningModel(env.brains["RealFakeBrain"])
            init = tf.global_variables_initializer()
            sess.run(init)

            run_list = [model.sample_action, model.action_probs]
            feed_dict = {model.batch_size: 2,
                         model.dropout_rate: 1.0,
                         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.action_masks: np.ones([2, 2])}
            sess.run(run_list, feed_dict=feed_dict)
            env.close()


@mock.patch('mlagents.envs.UnityEnvironment.executable_launcher')
@mock.patch('mlagents.envs.UnityEnvironment.get_communicator')
def test_visual_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=2)
            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]]),
                         model.visual_in[0]: np.ones([2, 40, 30, 3]),
                         model.visual_in[1]: np.ones([2, 40, 30, 3])}
            sess.run(run_list, feed_dict=feed_dict)
            env.close()


if __name__ == '__main__':
    pytest.main()