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106 行
3.6 KiB
106 行
3.6 KiB
import pytest
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from mlagents.torch_utils import torch
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from mlagents.trainers.sac.optimizer_torch import TorchSACOptimizer
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from mlagents.trainers.policy.torch_policy import TorchPolicy
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from mlagents.trainers.tests import mock_brain as mb
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from mlagents.trainers.settings import NetworkSettings
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from mlagents.trainers.tests.dummy_config import ( # noqa: F401
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sac_dummy_config,
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curiosity_dummy_config,
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)
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@pytest.fixture
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def dummy_config():
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return sac_dummy_config()
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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 = 64
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NUM_AGENTS = 12
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def create_sac_optimizer_mock(dummy_config, use_rnn, use_discrete, use_visual):
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mock_brain = mb.setup_test_behavior_specs(
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use_discrete,
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use_visual,
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vector_action_space=DISCRETE_ACTION_SPACE
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if use_discrete
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else VECTOR_ACTION_SPACE,
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vector_obs_space=VECTOR_OBS_SPACE if not use_visual else 0,
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)
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trainer_settings = dummy_config
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trainer_settings.network_settings.memory = (
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NetworkSettings.MemorySettings(sequence_length=16, memory_size=12)
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if use_rnn
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else None
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)
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policy = TorchPolicy(0, mock_brain, trainer_settings, "test", False)
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optimizer = TorchSACOptimizer(policy, trainer_settings)
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return optimizer
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@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
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@pytest.mark.parametrize("visual", [True, False], ids=["visual", "vector"])
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@pytest.mark.parametrize("rnn", [True, False], ids=["rnn", "no_rnn"])
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def test_sac_optimizer_update(dummy_config, rnn, visual, discrete):
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torch.manual_seed(0)
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# Test evaluate
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optimizer = create_sac_optimizer_mock(
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dummy_config, use_rnn=rnn, use_discrete=discrete, use_visual=visual
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)
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# Test update
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update_buffer = mb.simulate_rollout(
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BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec, memory_size=24
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)
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# Mock out reward signal eval
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update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"]
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return_stats = optimizer.update(
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update_buffer,
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num_sequences=update_buffer.num_experiences // optimizer.policy.sequence_length,
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)
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# Make sure we have the right stats
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required_stats = [
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"Losses/Policy Loss",
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"Losses/Value Loss",
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"Losses/Q1 Loss",
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"Losses/Q2 Loss",
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"Policy/Continuous Entropy Coeff",
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"Policy/Discrete Entropy Coeff",
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"Policy/Learning Rate",
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]
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for stat in required_stats:
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assert stat in return_stats.keys()
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@pytest.mark.parametrize("discrete", [True, False], ids=["discrete", "continuous"])
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def test_sac_update_reward_signals(
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dummy_config, curiosity_dummy_config, discrete # noqa: F811
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):
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# Add a Curiosity module
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dummy_config.reward_signals = curiosity_dummy_config
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optimizer = create_sac_optimizer_mock(
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dummy_config, use_rnn=False, use_discrete=discrete, use_visual=False
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)
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# Test update, while removing PPO-specific buffer elements.
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update_buffer = mb.simulate_rollout(
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BUFFER_INIT_SAMPLES, optimizer.policy.behavior_spec
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)
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# Mock out reward signal eval
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update_buffer["extrinsic_rewards"] = update_buffer["environment_rewards"]
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update_buffer["curiosity_rewards"] = update_buffer["environment_rewards"]
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return_stats = optimizer.update_reward_signals(
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{"curiosity": update_buffer}, num_sequences=update_buffer.num_experiences
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)
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required_stats = ["Losses/Curiosity Forward Loss", "Losses/Curiosity Inverse Loss"]
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for stat in required_stats:
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assert stat in return_stats.keys()
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if __name__ == "__main__":
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pytest.main()
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