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226 行
6.7 KiB
226 行
6.7 KiB
import pytest
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import yaml
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import os
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import mlagents.trainers.tests.mock_brain as mb
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from mlagents.trainers.common.nn_policy import NNPolicy
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from mlagents.trainers.sac.policy import SACPolicy
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def ppo_dummy_config():
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return yaml.safe_load(
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"""
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trainer: ppo
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batch_size: 32
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beta: 5.0e-3
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buffer_size: 512
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epsilon: 0.2
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hidden_units: 128
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lambd: 0.95
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learning_rate: 3.0e-4
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max_steps: 5.0e4
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normalize: true
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num_epoch: 5
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num_layers: 2
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time_horizon: 64
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sequence_length: 64
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summary_freq: 1000
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use_recurrent: false
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memory_size: 8
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reward_signals:
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extrinsic:
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strength: 1.0
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gamma: 0.99
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"""
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)
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def sac_dummy_config():
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return yaml.safe_load(
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"""
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trainer: sac
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batch_size: 128
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buffer_size: 50000
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buffer_init_steps: 0
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hidden_units: 128
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init_entcoef: 1.0
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learning_rate: 3.0e-4
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max_steps: 5.0e4
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memory_size: 256
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normalize: false
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num_update: 1
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train_interval: 1
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num_layers: 2
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time_horizon: 64
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sequence_length: 64
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summary_freq: 1000
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tau: 0.005
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use_recurrent: false
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vis_encode_type: simple
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behavioral_cloning:
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demo_path: ./Project/Assets/ML-Agents/Examples/Pyramids/Demos/ExpertPyramid.demo
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strength: 1.0
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steps: 10000000
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reward_signals:
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extrinsic:
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strength: 1.0
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gamma: 0.99
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"""
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)
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@pytest.fixture
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def gail_dummy_config():
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return {
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"gail": {
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"strength": 0.1,
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"gamma": 0.9,
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"encoding_size": 128,
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"use_vail": True,
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"demo_path": os.path.dirname(os.path.abspath(__file__)) + "/test.demo",
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}
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}
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@pytest.fixture
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def curiosity_dummy_config():
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return {"curiosity": {"strength": 0.1, "gamma": 0.9, "encoding_size": 128}}
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VECTOR_ACTION_SPACE = [2]
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VECTOR_OBS_SPACE = 8
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DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
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BUFFER_INIT_SAMPLES = 20
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BATCH_SIZE = 12
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NUM_AGENTS = 12
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def create_policy_mock(
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trainer_config, reward_signal_config, use_rnn, use_discrete, use_visual
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):
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mock_brain = mb.setup_mock_brain(
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use_discrete,
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use_visual,
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vector_action_space=VECTOR_ACTION_SPACE,
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vector_obs_space=VECTOR_OBS_SPACE,
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discrete_action_space=DISCRETE_ACTION_SPACE,
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)
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trainer_parameters = trainer_config
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model_path = "testpath"
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trainer_parameters["model_path"] = model_path
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trainer_parameters["keep_checkpoints"] = 3
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trainer_parameters["reward_signals"].update(reward_signal_config)
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trainer_parameters["use_recurrent"] = use_rnn
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if trainer_config["trainer"] == "ppo":
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policy = NNPolicy(0, mock_brain, trainer_parameters, False, False)
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else:
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policy = SACPolicy(0, mock_brain, trainer_parameters, False, False)
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return policy
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def reward_signal_eval(policy, reward_signal_name):
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buffer = mb.simulate_rollout(BATCH_SIZE, policy.brain)
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# Test evaluate
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rsig_result = policy.reward_signals[reward_signal_name].evaluate_batch(buffer)
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assert rsig_result.scaled_reward.shape == (BATCH_SIZE,)
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assert rsig_result.unscaled_reward.shape == (BATCH_SIZE,)
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def reward_signal_update(policy, reward_signal_name):
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buffer = mb.simulate_rollout(BUFFER_INIT_SAMPLES, policy.brain)
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feed_dict = policy.reward_signals[reward_signal_name].prepare_update(
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policy, buffer.make_mini_batch(0, 10), 2
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)
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out = policy._execute_model(
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feed_dict, policy.reward_signals[reward_signal_name].update_dict
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)
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assert type(out) is dict
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_gail_cc(trainer_config, gail_dummy_config):
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policy = create_policy_mock(trainer_config, gail_dummy_config, False, False, False)
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reward_signal_eval(policy, "gail")
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reward_signal_update(policy, "gail")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_gail_dc_visual(trainer_config, gail_dummy_config):
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gail_dummy_config["gail"]["demo_path"] = (
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os.path.dirname(os.path.abspath(__file__)) + "/testdcvis.demo"
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)
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policy = create_policy_mock(trainer_config, gail_dummy_config, False, True, True)
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reward_signal_eval(policy, "gail")
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reward_signal_update(policy, "gail")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_gail_rnn(trainer_config, gail_dummy_config):
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policy = create_policy_mock(trainer_config, gail_dummy_config, True, False, False)
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reward_signal_eval(policy, "gail")
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reward_signal_update(policy, "gail")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_curiosity_cc(trainer_config, curiosity_dummy_config):
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policy = create_policy_mock(
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trainer_config, curiosity_dummy_config, False, False, False
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)
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reward_signal_eval(policy, "curiosity")
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reward_signal_update(policy, "curiosity")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_curiosity_dc(trainer_config, curiosity_dummy_config):
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policy = create_policy_mock(
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trainer_config, curiosity_dummy_config, False, True, False
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)
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reward_signal_eval(policy, "curiosity")
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reward_signal_update(policy, "curiosity")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_curiosity_visual(trainer_config, curiosity_dummy_config):
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policy = create_policy_mock(
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trainer_config, curiosity_dummy_config, False, False, True
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)
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reward_signal_eval(policy, "curiosity")
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reward_signal_update(policy, "curiosity")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_curiosity_rnn(trainer_config, curiosity_dummy_config):
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policy = create_policy_mock(
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trainer_config, curiosity_dummy_config, True, False, False
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)
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reward_signal_eval(policy, "curiosity")
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reward_signal_update(policy, "curiosity")
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@pytest.mark.parametrize(
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"trainer_config", [ppo_dummy_config(), sac_dummy_config()], ids=["ppo", "sac"]
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)
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def test_extrinsic(trainer_config, curiosity_dummy_config):
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policy = create_policy_mock(
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trainer_config, curiosity_dummy_config, False, False, False
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)
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reward_signal_eval(policy, "extrinsic")
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reward_signal_update(policy, "extrinsic")
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if __name__ == "__main__":
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pytest.main()
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