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#!/usr/bin/env python
# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pytest
from lerobot.configs.types import FeatureType, NormalizationMode, PolicyFeature
from lerobot.policies.sac.configuration_sac import (
ActorLearnerConfig,
ActorNetworkConfig,
ConcurrencyConfig,
CriticNetworkConfig,
PolicyConfig,
SACConfig,
)
from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
def test_sac_config_default_initialization():
config = SACConfig()
assert config.normalization_mapping == {
"VISUAL": NormalizationMode.MEAN_STD,
"STATE": NormalizationMode.MIN_MAX,
"ENV": NormalizationMode.MIN_MAX,
"ACTION": NormalizationMode.MIN_MAX,
}
assert config.dataset_stats == {
OBS_IMAGE: {
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225],
},
OBS_STATE: {
"min": [0.0, 0.0],
"max": [1.0, 1.0],
},
ACTION: {
"min": [0.0, 0.0, 0.0],
"max": [1.0, 1.0, 1.0],
},
}
# Basic parameters
assert config.device == "cpu"
assert config.storage_device == "cpu"
assert config.discount == 0.99
assert config.temperature_init == 1.0
assert config.num_critics == 2
# Architecture specifics
assert config.vision_encoder_name is None
assert config.freeze_vision_encoder is True
assert config.image_encoder_hidden_dim == 32
assert config.shared_encoder is True
assert config.num_discrete_actions is None
assert config.image_embedding_pooling_dim == 8
# Training parameters
assert config.online_steps == 1000000
assert config.online_buffer_capacity == 100000
assert config.offline_buffer_capacity == 100000
assert config.async_prefetch is False
assert config.online_step_before_learning == 100
assert config.policy_update_freq == 1
# SAC algorithm parameters
assert config.num_subsample_critics is None
assert config.critic_lr == 3e-4
assert config.actor_lr == 3e-4
assert config.temperature_lr == 3e-4
assert config.critic_target_update_weight == 0.005
assert config.utd_ratio == 1
assert config.state_encoder_hidden_dim == 256
assert config.latent_dim == 256
assert config.target_entropy is None
assert config.use_backup_entropy is True
assert config.grad_clip_norm == 40.0
# Dataset stats defaults
expected_dataset_stats = {
OBS_IMAGE: {
"mean": [0.485, 0.456, 0.406],
"std": [0.229, 0.224, 0.225],
},
OBS_STATE: {
"min": [0.0, 0.0],
"max": [1.0, 1.0],
},
ACTION: {
"min": [0.0, 0.0, 0.0],
"max": [1.0, 1.0, 1.0],
},
}
assert config.dataset_stats == expected_dataset_stats
# Critic network configuration
assert config.critic_network_kwargs.hidden_dims == [256, 256]
assert config.critic_network_kwargs.activate_final is True
assert config.critic_network_kwargs.final_activation is None
# Actor network configuration
assert config.actor_network_kwargs.hidden_dims == [256, 256]
assert config.actor_network_kwargs.activate_final is True
# Policy configuration
assert config.policy_kwargs.use_tanh_squash is True
assert config.policy_kwargs.std_min == 1e-5
assert config.policy_kwargs.std_max == 10.0
assert config.policy_kwargs.init_final == 0.05
# Discrete critic network configuration
assert config.discrete_critic_network_kwargs.hidden_dims == [256, 256]
assert config.discrete_critic_network_kwargs.activate_final is True
assert config.discrete_critic_network_kwargs.final_activation is None
# Actor learner configuration
assert config.actor_learner_config.learner_host == "127.0.0.1"
assert config.actor_learner_config.learner_port == 50051
assert config.actor_learner_config.policy_parameters_push_frequency == 4
# Concurrency configuration
assert config.concurrency.actor == "threads"
assert config.concurrency.learner == "threads"
assert isinstance(config.actor_network_kwargs, ActorNetworkConfig)
assert isinstance(config.critic_network_kwargs, CriticNetworkConfig)
assert isinstance(config.policy_kwargs, PolicyConfig)
assert isinstance(config.actor_learner_config, ActorLearnerConfig)
assert isinstance(config.concurrency, ConcurrencyConfig)
def test_critic_network_kwargs():
config = CriticNetworkConfig()
assert config.hidden_dims == [256, 256]
assert config.activate_final is True
assert config.final_activation is None
def test_actor_network_kwargs():
config = ActorNetworkConfig()
assert config.hidden_dims == [256, 256]
assert config.activate_final is True
def test_policy_kwargs():
config = PolicyConfig()
assert config.use_tanh_squash is True
assert config.std_min == 1e-5
assert config.std_max == 10.0
assert config.init_final == 0.05
def test_actor_learner_config():
config = ActorLearnerConfig()
assert config.learner_host == "127.0.0.1"
assert config.learner_port == 50051
assert config.policy_parameters_push_frequency == 4
def test_concurrency_config():
config = ConcurrencyConfig()
assert config.actor == "threads"
assert config.learner == "threads"
def test_sac_config_custom_initialization():
config = SACConfig(
device="cpu",
discount=0.95,
temperature_init=0.5,
num_critics=3,
)
assert config.device == "cpu"
assert config.discount == 0.95
assert config.temperature_init == 0.5
assert config.num_critics == 3
def test_validate_features():
config = SACConfig(
input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
)
config.validate_features()
def test_validate_features_missing_observation():
config = SACConfig(
input_features={"wrong_key": PolicyFeature(type=FeatureType.STATE, shape=(10,))},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
)
with pytest.raises(
ValueError, match="You must provide either 'observation.state' or an image observation"
):
config.validate_features()
def test_validate_features_missing_action():
config = SACConfig(
input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(10,))},
output_features={"wrong_key": PolicyFeature(type=FeatureType.ACTION, shape=(3,))},
)
with pytest.raises(ValueError, match="You must provide 'action' in the output features"):
config.validate_features()
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