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import unittest.mock as mock
import pytest
import numpy as np
import tensorflow as tf
import yaml
from mlagents.trainers.ppo.models import PPOModel
from mlagents.trainers.ppo.trainer import PPOTrainer, discount_rewards
from mlagents.trainers.ppo.policy import PPOPolicy
from mlagents.trainers.rl_trainer import AllRewardsOutput
from mlagents.trainers.components.reward_signals import RewardSignalResult
from mlagents.envs import UnityEnvironment, BrainParameters
from mlagents.envs.mock_communicator import MockCommunicator
@pytest.fixture
def dummy_config():
return yaml.safe_load(
"""
trainer: ppo
batch_size: 32
beta: 5.0e-3
buffer_size: 512
epsilon: 0.2
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
curiosity_strength: 0.0
curiosity_enc_size: 1
reward_signals:
extrinsic:
strength: 1.0
gamma: 0.99
"""
)
@mock.patch("mlagents.envs.UnityEnvironment.executable_launcher")
@mock.patch("mlagents.envs.UnityEnvironment.get_communicator")
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()
@mock.patch("mlagents.envs.UnityEnvironment.executable_launcher")
@mock.patch("mlagents.envs.UnityEnvironment.get_communicator")
def test_ppo_get_value_estimates(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.get_value_estimates(brain_info, 0, done=False)
for key, val in run_out.items():
assert type(key) is str
assert type(val) is float
run_out = policy.get_value_estimates(brain_info, 0, done=True)
for key, val in run_out.items():
assert type(key) is str
assert val == 0.0
# Check if we ignore terminal states properly
policy.reward_signals["extrinsic"].use_terminal_states = False
run_out = policy.get_value_estimates(brain_info, 0, done=True)
for key, val in run_out.items():
assert type(key) is str
assert val != 0.0
env.close()
@mock.patch("mlagents.envs.UnityEnvironment.executable_launcher")
@mock.patch("mlagents.envs.UnityEnvironment.get_communicator")
def test_ppo_model_cc_vector(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 = 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.epsilon: np.array([[0, 1], [2, 3]]),
}
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_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()
@mock.patch("mlagents.envs.UnityEnvironment.executable_launcher")
@mock.patch("mlagents.envs.UnityEnvironment.get_communicator")
def test_ppo_model_dc_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=True, visual_inputs=2
)
env = UnityEnvironment(" ")
model = PPOModel(env.brains["RealFakeBrain"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output,
model.all_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.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_ppo_model_dc_vector(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"])
init = tf.global_variables_initializer()
sess.run(init)
run_list = [
model.output,
model.all_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.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_ppo_model_dc_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=True, 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.prev_action: [[0], [0]],
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.action_masks: np.ones([1, 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_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_rl_functions():
rewards = np.array([0.0, 0.0, 0.0, 1.0])
gamma = 0.9
returns = discount_rewards(rewards, gamma, 0.0)
np.testing.assert_array_almost_equal(returns, np.array([0.729, 0.81, 0.9, 1.0]))
def test_trainer_increment_step():
trainer_params = {
"trainer": "ppo",
"batch_size": 2048,
"beta": 0.005,
"buffer_size": 20480,
"epsilon": 0.2,
"gamma": 0.995,
"hidden_units": 512,
"lambd": 0.95,
"learning_rate": 0.0003,
"max_steps": "2e6",
"memory_size": 256,
"normalize": True,
"num_epoch": 3,
"num_layers": 3,
"time_horizon": 1000,
"sequence_length": 64,
"summary_freq": 3000,
"use_recurrent": False,
"use_curiosity": False,
"curiosity_strength": 0.01,
"curiosity_enc_size": 128,
"summary_path": "./summaries/test_trainer_summary",
"model_path": "./models/test_trainer_models/TestModel",
"keep_checkpoints": 5,
"reward_signals": {"extrinsic": {"strength": 1.0, "gamma": 0.99}},
}
brain_params = BrainParameters("test_brain", 1, 1, [], [2], [], 0)
trainer = PPOTrainer(brain_params, 0, trainer_params, True, False, 0, "0", False)
policy_mock = mock.Mock()
step_count = 10
policy_mock.increment_step = mock.Mock(return_value=step_count)
trainer.policy = policy_mock
trainer.increment_step(5)
policy_mock.increment_step.assert_called_with(5)
assert trainer.step == 10
def test_add_rewards_output(dummy_config):
brain_params = BrainParameters("test_brain", 1, 1, [], [2], [], 0)
dummy_config["summary_path"] = "./summaries/test_trainer_summary"
dummy_config["model_path"] = "./models/test_trainer_models/TestModel"
trainer = PPOTrainer(brain_params, 0, dummy_config, True, False, 0, "0", False)
rewardsout = AllRewardsOutput(
reward_signals={
"extrinsic": RewardSignalResult(
scaled_reward=np.array([1.0, 1.0]), unscaled_reward=np.array([1.0, 1.0])
)
},
environment=np.array([1.0, 1.0]),
)
values = {"extrinsic": np.array([[2.0]])}
agent_id = "123"
idx = 0
# make sure that we're grabbing from the next_idx for rewards. If we're not, the test will fail.
next_idx = 1
trainer.add_rewards_outputs(
rewardsout,
values=values,
agent_id=agent_id,
agent_idx=idx,
agent_next_idx=next_idx,
)
assert trainer.training_buffer[agent_id]["extrinsic_value_estimates"][0] == 2.0
assert trainer.training_buffer[agent_id]["extrinsic_rewards"][0] == 1.0
if __name__ == "__main__":
pytest.main()