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import unittest.mock as mock
import pytest
import mlagents.trainers.tests.mock_brain as mb
import numpy as np
import tensorflow as tf
import yaml
import os
from mlagents.trainers.ppo.models import PPOModel
from mlagents.trainers.ppo.trainer import discount_rewards
from mlagents.trainers.ppo.policy import PPOPolicy
from mlagents.trainers.demo_loader import make_demo_buffer
from mlagents.envs import UnityEnvironment
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
"""
)
@pytest.fixture
def gail_dummy_config():
return {
"gail": {
"strength": 0.1,
"gamma": 0.9,
"encoding_size": 128,
"demo_path": os.path.dirname(os.path.abspath(__file__)) + "/test.demo",
}
}
@pytest.fixture
def curiosity_dummy_config():
return {"curiosity": {"strength": 0.1, "gamma": 0.9, "encoding_size": 128}}
VECTOR_ACTION_SPACE = [2]
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 20
NUM_AGENTS = 12
def create_ppo_policy_mock(
mock_env, dummy_config, reward_signal_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.brain_names[0]
trainer_parameters["model_path"] = model_path
trainer_parameters["keep_checkpoints"] = 3
trainer_parameters["reward_signals"].update(reward_signal_config)
trainer_parameters["use_recurrent"] = use_rnn
policy = PPOPolicy(0, mock_brain, trainer_parameters, False, False)
return env, policy
def reward_signal_eval(env, policy, reward_signal_name):
brain_infos = env.reset()
brain_info = brain_infos[env.brain_names[0]]
next_brain_info = env.step()[env.brain_names[0]]
# Test evaluate
rsig_result = policy.reward_signals[reward_signal_name].evaluate(
brain_info, next_brain_info
)
assert rsig_result.scaled_reward.shape == (NUM_AGENTS,)
assert rsig_result.unscaled_reward.shape == (NUM_AGENTS,)
def reward_signal_update(env, policy, reward_signal_name):
buffer = mb.simulate_rollout(env, policy, BUFFER_INIT_SAMPLES)
feed_dict = policy.reward_signals[reward_signal_name].prepare_update(
policy.model, buffer.update_buffer.make_mini_batch(0, 10), 2
)
out = policy._execute_model(
feed_dict, policy.reward_signals[reward_signal_name].update_dict
)
assert type(out) is dict
@mock.patch("mlagents.envs.UnityEnvironment")
def test_gail_cc(mock_env, dummy_config, gail_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, gail_dummy_config, False, False, False
)
reward_signal_eval(env, policy, "gail")
reward_signal_update(env, policy, "gail")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_gail_dc_visual(mock_env, dummy_config, gail_dummy_config):
gail_dummy_config["gail"]["demo_path"] = (
os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
)
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, gail_dummy_config, False, True, True
)
reward_signal_eval(env, policy, "gail")
reward_signal_update(env, policy, "gail")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_gail_rnn(mock_env, dummy_config, gail_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, gail_dummy_config, True, False, False
)
reward_signal_eval(env, policy, "gail")
reward_signal_update(env, policy, "gail")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_curiosity_cc(mock_env, dummy_config, curiosity_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, curiosity_dummy_config, False, False, False
)
reward_signal_eval(env, policy, "curiosity")
reward_signal_update(env, policy, "curiosity")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_curiosity_dc(mock_env, dummy_config, curiosity_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, curiosity_dummy_config, False, True, False
)
reward_signal_eval(env, policy, "curiosity")
reward_signal_update(env, policy, "curiosity")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_curiosity_visual(mock_env, dummy_config, curiosity_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, curiosity_dummy_config, False, False, True
)
reward_signal_eval(env, policy, "curiosity")
reward_signal_update(env, policy, "curiosity")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_curiosity_rnn(mock_env, dummy_config, curiosity_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, curiosity_dummy_config, True, False, False
)
reward_signal_eval(env, policy, "curiosity")
reward_signal_update(env, policy, "curiosity")
@mock.patch("mlagents.envs.UnityEnvironment")
def test_extrinsic(mock_env, dummy_config, curiosity_dummy_config):
env, policy = create_ppo_policy_mock(
mock_env, dummy_config, curiosity_dummy_config, False, False, False
)
reward_signal_eval(env, policy, "extrinsic")
reward_signal_update(env, policy, "extrinsic")
if __name__ == "__main__":
pytest.main()