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import pytest
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from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
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from lerobot.policies.sac.configuration_sac import (
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ActorLearnerConfig,
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ActorNetworkConfig,
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ConcurrencyConfig,
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CriticNetworkConfig,
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PolicyConfig,
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SACConfig,
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)
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from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
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def test_sac_config_default_initialization():
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config = SACConfig()
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assert config.normalization_mapping == {
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"VISUAL": NormalizationMode.MEAN_STD,
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"STATE": NormalizationMode.MIN_MAX,
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"ENV": NormalizationMode.MIN_MAX,
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"ACTION": NormalizationMode.MIN_MAX,
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}
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assert config.dataset_stats == {
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OBS_IMAGE: {
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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},
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OBS_STATE: {
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"min": [0.0, 0.0],
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"max": [1.0, 1.0],
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},
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ACTION: {
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"min": [0.0, 0.0, 0.0],
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"max": [1.0, 1.0, 1.0],
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},
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}
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assert config.device == "cpu"
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assert config.storage_device == "cpu"
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assert config.discount == 0.99
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assert config.temperature_init == 1.0
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assert config.num_critics == 2
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assert config.vision_encoder_name is None
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assert config.freeze_vision_encoder is True
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assert config.image_encoder_hidden_dim == 32
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assert config.shared_encoder is True
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assert config.num_discrete_actions is None
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assert config.image_embedding_pooling_dim == 8
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assert config.online_steps == 1000000
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assert config.online_buffer_capacity == 100000
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assert config.offline_buffer_capacity == 100000
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assert config.async_prefetch is False
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assert config.online_step_before_learning == 100
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assert config.policy_update_freq == 1
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assert config.num_subsample_critics is None
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assert config.critic_lr == 3e-4
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assert config.actor_lr == 3e-4
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assert config.temperature_lr == 3e-4
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assert config.critic_target_update_weight == 0.005
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assert config.utd_ratio == 1
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assert config.state_encoder_hidden_dim == 256
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assert config.latent_dim == 256
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assert config.target_entropy is None
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assert config.use_backup_entropy is True
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assert config.grad_clip_norm == 40.0
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expected_dataset_stats = {
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OBS_IMAGE: {
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"mean": [0.485, 0.456, 0.406],
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"std": [0.229, 0.224, 0.225],
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},
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OBS_STATE: {
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"min": [0.0, 0.0],
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"max": [1.0, 1.0],
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},
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ACTION: {
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"min": [0.0, 0.0, 0.0],
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"max": [1.0, 1.0, 1.0],
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},
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}
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assert config.dataset_stats == expected_dataset_stats
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assert config.critic_network_kwargs.hidden_dims == [256, 256]
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assert config.critic_network_kwargs.activate_final is True
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assert config.critic_network_kwargs.final_activation is None
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assert config.actor_network_kwargs.hidden_dims == [256, 256]
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assert config.actor_network_kwargs.activate_final is True
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assert config.policy_kwargs.use_tanh_squash is True
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assert config.policy_kwargs.std_min == 1e-5
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assert config.policy_kwargs.std_max == 10.0
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assert config.policy_kwargs.init_final == 0.05
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assert config.discrete_critic_network_kwargs.hidden_dims == [256, 256]
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assert config.discrete_critic_network_kwargs.activate_final is True
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assert config.discrete_critic_network_kwargs.final_activation is None
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assert config.actor_learner_config.learner_host == "127.0.0.1"
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assert config.actor_learner_config.learner_port == 50051
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assert config.actor_learner_config.policy_parameters_push_frequency == 4
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assert config.concurrency.actor == "threads"
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assert config.concurrency.learner == "threads"
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assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
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assert isinstance(config.critic_network_kwargs, CriticNetworkConfig)
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assert isinstance(config.policy_kwargs, PolicyConfig)
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assert isinstance(config.actor_learner_config, ActorLearnerConfig)
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assert isinstance(config.concurrency, ConcurrencyConfig)
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def test_critic_network_kwargs():
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config = CriticNetworkConfig()
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assert config.hidden_dims == [256, 256]
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assert config.activate_final is True
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assert config.final_activation is None
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def test_actor_network_kwargs():
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config = ActorNetworkConfig()
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assert config.hidden_dims == [256, 256]
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assert config.activate_final is True
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def test_policy_kwargs():
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config = PolicyConfig()
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assert config.use_tanh_squash is True
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assert config.std_min == 1e-5
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assert config.std_max == 10.0
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assert config.init_final == 0.05
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def test_actor_learner_config():
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config = ActorLearnerConfig()
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assert config.learner_host == "127.0.0.1"
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assert config.learner_port == 50051
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assert config.policy_parameters_push_frequency == 4
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def test_concurrency_config():
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config = ConcurrencyConfig()
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assert config.actor == "threads"
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assert config.learner == "threads"
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def test_sac_config_custom_initialization():
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config = SACConfig(
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device="cpu",
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discount=0.95,
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temperature_init=0.5,
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num_critics=3,
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)
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assert config.device == "cpu"
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assert config.discount == 0.95
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assert config.temperature_init == 0.5
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assert config.num_critics == 3
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def test_validate_features():
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config = SACConfig(
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input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))},
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output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
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)
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config.validate_features()
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def test_validate_features_missing_observation():
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config = SACConfig(
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input_features={"wrong_key": PolicyFeature(type=FeatureType.STATE, shape=(10,))},
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output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
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)
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with pytest.raises(
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ValueError, match="You must provide either 'observation.state' or an image observation"
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):
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config.validate_features()
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def test_validate_features_missing_action():
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config = SACConfig(
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input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))},
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output_features={"wrong_key": PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
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)
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with pytest.raises(ValueError, match="You must provide 'action' in the output features"):
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config.validate_features()
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