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import pytest
from mlagents.torch_utils import torch
from mlagents.trainers.buffer import BufferKey, RewardSignalUtil
from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.torch_entities.networks import SimpleActor
from mlagents.trainers.tests import mock_brain as mb
from mlagents.trainers.settings import NetworkSettings
from mlagents.trainers.tests.dummy_config import ( # noqa: F401
sac_dummy_config,
curiosity_dummy_config,
)
@pytest.fixture
def dummy_config():
return sac_dummy_config()
VECTOR_ACTION_SPACE = 2
VECTOR_OBS_SPACE = 8
DISCRETE_ACTION_SPACE = [3, 3, 3, 2]
BUFFER_INIT_SAMPLES = 64
NUM_AGENTS = 12
def create_sac_optimizer_mock(dummy_config, use_rnn, use_discrete, use_visual):
mock_brain = mb.setup_test_behavior_specs(
use_discrete,
use_visual,
vector_action_space=DISCRETE_ACTION_SPACE
if use_discrete
else VECTOR_ACTION_SPACE,
vector_obs_space=VECTOR_OBS_SPACE if not use_visual else 0,
)
trainer_settings = dummy_config
trainer_settings.network_settings.memory = (
NetworkSettings.MemorySettings(sequence_length=16, memory_size=12)
if use_rnn
else None
)
actor_kwargs = {
"conditional_sigma": False,
"tanh_squash": False,
}
policy = TorchPolicy(
0, mock_brain, trainer_settings.network_settings, SimpleActor, actor_kwargs
)
optimizer = TorchSACOptimizer(policy, trainer_settings)
return optimizer
@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
def test_sac_optimizer_update(dummy_config, rnn, visual, discrete):
torch.manual_seed(0)
# Test evaluate
optimizer = create_sac_optimizer_mock(
dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
)
# Test update
update_buffer = mb.simulate_rollout(
BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec, memory_size=12
)
# Mock out reward signal eval
update_buffer[RewardSignalUtil.rewards_key("extrinsic")] = update_buffer[
BufferKey.ENVIRONMENT_REWARDS
]
# Mock out value memories
update_buffer[BufferKey.CRITIC_MEMORY] = update_buffer[BufferKey.MEMORY]
return_stats = optimizer.update(
update_buffer,
num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
)
# Make sure we have the right stats
required_stats = [
"Losses/Policy Loss",
"Losses/Value Loss",
"Losses/Q1 Loss",
"Losses/Q2 Loss",
"Policy/Continuous Entropy Coeff",
"Policy/Discrete Entropy Coeff",
"Policy/Learning Rate",
]
for stat in required_stats:
assert stat in return_stats.keys()
@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
def test_sac_update_reward_signals(
dummy_config, curiosity_dummy_config, discrete # noqa: F811
):
# Add a Curiosity module
dummy_config.reward_signals = curiosity_dummy_config
optimizer = create_sac_optimizer_mock(
dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False
)
# Test update, while removing PPO-specific buffer elements.
update_buffer = mb.simulate_rollout(
BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
)
# Mock out reward signal eval
update_buffer[RewardSignalUtil.rewards_key("extrinsic")] = update_buffer[
BufferKey.ENVIRONMENT_REWARDS
]
update_buffer[RewardSignalUtil.rewards_key("curiosity")] = update_buffer[
BufferKey.ENVIRONMENT_REWARDS
]
return_stats = optimizer.update_reward_signals(update_buffer)
required_stats = ["Losses/Curiosity Forward Loss", "Losses/Curiosity Inverse Loss"]
for stat in required_stats:
assert stat in return_stats.keys()
if __name__ == "__main__":
pytest.main()
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