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refactor(policies): rename policies/sac → policies/gaussian_actor

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This commit is contained in:
Khalil Meftah
2026-04-23 19:13:18 +02:00
parent 8065bf15c7
commit 06255996ea
24 changed files with 185 additions and 168 deletions
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tests/rl/test_actor_learner.py
+11 -11
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@@ -28,7 +28,7 @@ from torch.multiprocessing import Event, Queue
from lerobot.configs.train import TrainRLServerPipelineConfig
from lerobot.configs.types import FeatureType, PolicyFeature
from lerobot.policies.sac.configuration_sac import SACConfig
from lerobot.policies.gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
from lerobot.utils.constants import ACTION, OBS_STATE, OBS_STR
from lerobot.utils.transition import Transition
from tests.utils import skip_if_package_missing
@@ -81,7 +81,7 @@ def cfg():
port = find_free_port()
policy_cfg = SACConfig()
policy_cfg = GaussianActorConfig()
policy_cfg.actor_learner_config.learner_host = "127.0.0.1"
policy_cfg.actor_learner_config.learner_port = port
policy_cfg.concurrency.actor = "threads"
@@ -312,7 +312,7 @@ def test_learner_algorithm_wiring():
"""Verify that make_algorithm constructs an SACAlgorithm from config,
make_optimizers_and_scheduler() creates the right optimizers, update() works, and
get_weights() output is serializable."""
from lerobot.policies.sac.modeling_sac import SACPolicy
from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
from lerobot.rl.algorithms.factory import make_algorithm
from lerobot.rl.algorithms.sac import SACAlgorithm
from lerobot.transport.utils import state_to_bytes
@@ -320,7 +320,7 @@ def test_learner_algorithm_wiring():
state_dim = 10
action_dim = 6
sac_cfg = SACConfig(
sac_cfg = GaussianActorConfig(
input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
dataset_stats={
@@ -331,7 +331,7 @@ def test_learner_algorithm_wiring():
)
sac_cfg.validate_features()
policy = SACPolicy(config=sac_cfg)
policy = GaussianActorPolicy(config=sac_cfg)
policy.train()
algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
@@ -399,13 +399,13 @@ def test_learner_algorithm_wiring():
def test_initial_and_periodic_weight_push_consistency():
"""Both initial and periodic weight pushes should use algorithm.get_weights()
and produce identical structures."""
from lerobot.policies.sac.modeling_sac import SACPolicy
from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
from lerobot.rl.algorithms.factory import make_algorithm
from lerobot.transport.utils import bytes_to_state_dict, state_to_bytes
state_dim = 10
action_dim = 6
sac_cfg = SACConfig(
sac_cfg = GaussianActorConfig(
input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
dataset_stats={
@@ -416,7 +416,7 @@ def test_initial_and_periodic_weight_push_consistency():
)
sac_cfg.validate_features()
policy = SACPolicy(config=sac_cfg)
policy = GaussianActorPolicy(config=sac_cfg)
policy.train()
algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
algorithm.make_optimizers_and_scheduler()
@@ -437,13 +437,13 @@ def test_initial_and_periodic_weight_push_consistency():
def test_actor_side_algorithm_select_action_and_load_weights():
"""Simulate actor: create algorithm without optimizers, select_action, load_weights."""
from lerobot.policies.sac.modeling_sac import SACPolicy
from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
from lerobot.rl.algorithms.factory import make_algorithm
from lerobot.rl.algorithms.sac import SACAlgorithm
state_dim = 10
action_dim = 6
sac_cfg = SACConfig(
sac_cfg = GaussianActorConfig(
input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(action_dim,))},
dataset_stats={
@@ -455,7 +455,7 @@ def test_actor_side_algorithm_select_action_and_load_weights():
sac_cfg.validate_features()
# Actor side: no optimizers
policy = SACPolicy(config=sac_cfg)
policy = GaussianActorPolicy(config=sac_cfg)
policy.eval()
algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
assert isinstance(algorithm, SACAlgorithm)