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import unittest.mock as mock
import pytest
import yaml
import numpy as np
from mlagents.tf_utils import tf
from mlagents.trainers.sac.models import SACModel
from mlagents.trainers.sac.policy import SACPolicy
from mlagents.trainers.sac.trainer import SACTrainer
from mlagents.trainers.agent_processor import AgentManagerQueue
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.tests.mock_brain import make_brain_parameters
from mlagents.trainers.tests.test_trajectory import make_fake_trajectory
@pytest.fixture
def dummy_config():
return yaml.safe_load(
"""
trainer: sac
batch_size: 32
buffer_size: 10240
buffer_init_steps: 0
hidden_units: 32
init_entcoef: 0.1
learning_rate: 3.0e-4
max_steps: 1024
memory_size: 8
normalize: false
num_update: 1
train_interval: 1
num_layers: 1
time_horizon: 64
sequence_length: 16
summary_freq: 1000
tau: 0.005
use_recurrent: false
curiosity_enc_size: 128
demo_path: None
vis_encode_type: simple
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
"""
)
VECTOR_ACTION_SPACE = [2]
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 32
NUM_AGENTS = 12
def create_sac_policy_mock(mock_env, dummy_config, use_rnn, use_discrete, use_visual):
env, mock_brain, _ = mb.setup_mock_env_and_brains(
mock_env,
use_discrete,
use_visual,
num_agents=NUM_AGENTS,
vector_action_space=VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
discrete_action_space=DISCRETE_ACTION_SPACE,
)
trainer_parameters = dummy_config
model_path = env.external_brain_names[0]
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
trainer_parameters["use_recurrent"] = use_rnn
policy = SACPolicy(0, mock_brain, trainer_parameters, False, False)
return env, policy
@mock.patch("mlagents_envs.environment.UnityEnvironment")
def test_sac_cc_policy(mock_env, dummy_config):
# Test evaluate
tf.reset_default_graph()
env, policy = create_sac_policy_mock(
mock_env, dummy_config, use_rnn=False, use_discrete=False, use_visual=False
)
brain_infos = env.reset()
brain_info = brain_infos[env.external_brain_names[0]]
run_out = policy.evaluate(brain_info)
assert run_out["action"].shape == (NUM_AGENTS, VECTOR_ACTION_SPACE[0])
# Test update
update_buffer = mb.simulate_rollout(env, policy, BUFFER_INIT_SAMPLES)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = update_buffer["rewards"]
policy.update(update_buffer, num_sequences=update_buffer.num_experiences)
env.close()
@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
@mock.patch("mlagents_envs.environment.UnityEnvironment")
def test_sac_update_reward_signals(mock_env, dummy_config, discrete):
# Test evaluate
tf.reset_default_graph()
# Add a Curiosity module
dummy_config["reward_signals"]["curiosity"] = {}
dummy_config["reward_signals"]["curiosity"]["strength"] = 1.0
dummy_config["reward_signals"]["curiosity"]["gamma"] = 0.99
dummy_config["reward_signals"]["curiosity"]["encoding_size"] = 128
env, policy = create_sac_policy_mock(
mock_env, dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False
)
# Test update, while removing PPO-specific buffer elements.
update_buffer = mb.simulate_rollout(
env, policy, BUFFER_INIT_SAMPLES, exclude_key_list=["advantages", "actions_pre"]
)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = update_buffer["rewards"]
update_buffer["curiosity_rewards"] = update_buffer["rewards"]
policy.update_reward_signals(
{"curiosity": update_buffer}, num_sequences=update_buffer.num_experiences
)
env.close()
@mock.patch("mlagents_envs.environment.UnityEnvironment")
def test_sac_dc_policy(mock_env, dummy_config):
# Test evaluate
tf.reset_default_graph()
env, policy = create_sac_policy_mock(
mock_env, dummy_config, use_rnn=False, use_discrete=True, use_visual=False
)
brain_infos = env.reset()
brain_info = brain_infos[env.external_brain_names[0]]
run_out = policy.evaluate(brain_info)
assert run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE))
# Test update
update_buffer = mb.simulate_rollout(env, policy, BUFFER_INIT_SAMPLES)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = update_buffer["rewards"]
policy.update(update_buffer, num_sequences=update_buffer.num_experiences)
env.close()
@mock.patch("mlagents_envs.environment.UnityEnvironment")
def test_sac_visual_policy(mock_env, dummy_config):
# Test evaluate
tf.reset_default_graph()
env, policy = create_sac_policy_mock(
mock_env, dummy_config, use_rnn=False, use_discrete=True, use_visual=True
)
brain_infos = env.reset()
brain_info = brain_infos[env.external_brain_names[0]]
run_out = policy.evaluate(brain_info)
assert run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE))
# Test update
update_buffer = mb.simulate_rollout(env, policy, BUFFER_INIT_SAMPLES)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = update_buffer["rewards"]
run_out = policy.update(update_buffer, num_sequences=update_buffer.num_experiences)
assert type(run_out) is dict
@mock.patch("mlagents_envs.environment.UnityEnvironment")
def test_sac_rnn_policy(mock_env, dummy_config):
# Test evaluate
tf.reset_default_graph()
env, policy = create_sac_policy_mock(
mock_env, dummy_config, use_rnn=True, use_discrete=True, use_visual=False
)
brain_infos = env.reset()
brain_info = brain_infos[env.external_brain_names[0]]
run_out = policy.evaluate(brain_info)
assert run_out["action"].shape == (NUM_AGENTS, len(DISCRETE_ACTION_SPACE))
# Test update
update_buffer = mb.simulate_rollout(env, policy, BUFFER_INIT_SAMPLES)
# Mock out reward signal eval
update_buffer["extrinsic_rewards"] = update_buffer["rewards"]
policy.update(update_buffer, num_sequences=2)
env.close()
def test_sac_model_cc_vector():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = SACModel(
make_brain_parameters(discrete_action=False, visual_inputs=0)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, 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]]),
}
sess.run(run_list, feed_dict=feed_dict)
def test_sac_model_cc_visual():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = SACModel(
make_brain_parameters(discrete_action=False, visual_inputs=2)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, 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], dtype=np.float32),
model.visual_in[1]: np.ones([2, 40, 30, 3], dtype=np.float32),
}
sess.run(run_list, feed_dict=feed_dict)
def test_sac_model_dc_visual():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = SACModel(
make_brain_parameters(discrete_action=True, visual_inputs=2)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, 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], dtype=np.float32),
model.visual_in[1]: np.ones([2, 40, 30, 3], dtype=np.float32),
model.action_masks: np.ones([2, 2], dtype=np.float32),
}
sess.run(run_list, feed_dict=feed_dict)
def test_sac_model_dc_vector():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
model = SACModel(
make_brain_parameters(discrete_action=True, visual_inputs=0)
)
init = tf.global_variables_initializer()
sess.run(init)
run_list = [model.output, 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.action_masks: np.ones([2, 2], dtype=np.float32),
}
sess.run(run_list, feed_dict=feed_dict)
def test_sac_model_dc_vector_rnn():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
memory_size = 128
model = SACModel(
make_brain_parameters(discrete_action=True, visual_inputs=0),
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.prev_action: [[0], [0]],
model.memory_in: np.zeros((1, memory_size), dtype=np.float32),
model.vector_in: np.array([[1, 2, 3, 1, 2, 3], [3, 4, 5, 3, 4, 5]]),
model.action_masks: np.ones([1, 2], dtype=np.float32),
}
sess.run(run_list, feed_dict=feed_dict)
def test_sac_model_cc_vector_rnn():
tf.reset_default_graph()
with tf.Session() as sess:
with tf.variable_scope("FakeGraphScope"):
memory_size = 128
model = SACModel(
make_brain_parameters(discrete_action=False, visual_inputs=0),
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), dtype=np.float32),
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)
def test_sac_save_load_buffer(tmpdir, dummy_config):
env, mock_brain, _ = mb.setup_mock_env_and_brains(
mock.Mock(),
False,
False,
num_agents=NUM_AGENTS,
vector_action_space=VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE,
discrete_action_space=DISCRETE_ACTION_SPACE,
)
trainer_params = dummy_config
trainer_params["summary_path"] = str(tmpdir)
trainer_params["model_path"] = str(tmpdir)
trainer_params["save_replay_buffer"] = True
trainer = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, False, 0, 0)
policy = trainer.create_policy(mock_brain)
trainer.add_policy(mock_brain.brain_name, policy)
trainer.update_buffer = mb.simulate_rollout(
env, trainer.policy, BUFFER_INIT_SAMPLES
)
buffer_len = trainer.update_buffer.num_experiences
trainer.save_model(mock_brain.brain_name)
# Wipe Trainer and try to load
trainer2 = SACTrainer(mock_brain.brain_name, 1, trainer_params, True, True, 0, 0)
policy = trainer2.create_policy(mock_brain)
trainer2.add_policy(mock_brain.brain_name, policy)
assert trainer2.update_buffer.num_experiences == buffer_len
def test_process_trajectory(dummy_config):
brain_params = make_brain_parameters(
discrete_action=False, visual_inputs=0, vec_obs_size=6
)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
trainer = SACTrainer(brain_params, 0, dummy_config, True, False, 0, "0")
policy = trainer.create_policy(brain_params)
trainer.add_policy(brain_params.brain_name, policy)
trajectory_queue = AgentManagerQueue("testbrain")
trainer.subscribe_trajectory_queue(trajectory_queue)
trajectory = make_fake_trajectory(
length=15, max_step_complete=True, vec_obs_size=6, num_vis_obs=0, action_space=2
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that trainer put trajectory in update buffer
assert trainer.update_buffer.num_experiences == 15
# Check that the stats are being collected as episode isn't complete
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent > 0
# Add a terminal trajectory
trajectory = make_fake_trajectory(
length=15,
max_step_complete=False,
vec_obs_size=6,
num_vis_obs=0,
action_space=2,
)
trajectory_queue.put(trajectory)
trainer.advance()
# Check that the stats are reset as episode is finished
for reward in trainer.collected_rewards.values():
for agent in reward.values():
assert agent == 0
assert trainer.stats_reporter.get_stats_summaries("Policy/Extrinsic Reward").num > 0
if __name__ == "__main__":
pytest.main()