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refactor(rl): make algorithm a nested config so all SAC hyperparameters are JSON-addressable

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This commit is contained in:
Khalil Meftah
2026-04-27 13:39:03 +02:00
parent 21c16a27f0
commit 9ce9e01469
9 changed files with 86 additions and 56 deletions
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tests/rl/test_actor_learner.py
+7 -6
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@@ -26,9 +26,9 @@ pytest.importorskip("grpc")
from torch.multiprocessing import Event, Queue
from lerobot.configs.train import TrainRLServerPipelineConfig
from lerobot.configs.types import FeatureType, PolicyFeature
from lerobot.policies.gaussian_actor.configuration_gaussian_actor import GaussianActorConfig
from lerobot.rl.train_rl import TrainRLServerPipelineConfig
from lerobot.utils.constants import ACTION, OBS_STATE, OBS_STR
from lerobot.utils.transition import Transition
from tests.utils import skip_if_package_missing
@@ -314,7 +314,7 @@ def test_learner_algorithm_wiring():
get_weights() output is serializable."""
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.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
from lerobot.transport.utils import state_to_bytes
state_dim = 10
@@ -333,7 +333,7 @@ def test_learner_algorithm_wiring():
policy = GaussianActorPolicy(config=sac_cfg)
policy.train()
algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
assert isinstance(algorithm, SACAlgorithm)
optimizers = algorithm.make_optimizers_and_scheduler()
@@ -400,6 +400,7 @@ def test_initial_and_periodic_weight_push_consistency():
and produce identical structures."""
from lerobot.policies.gaussian_actor.modeling_gaussian_actor import GaussianActorPolicy
from lerobot.rl.algorithms.factory import make_algorithm
from lerobot.rl.algorithms.sac import SACAlgorithmConfig
from lerobot.transport.utils import bytes_to_state_dict, state_to_bytes
state_dim = 10
@@ -416,7 +417,7 @@ def test_initial_and_periodic_weight_push_consistency():
policy = GaussianActorPolicy(config=sac_cfg)
policy.train()
algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
algorithm.make_optimizers_and_scheduler()
# Simulate initial push (same code path the learner now uses)
@@ -437,7 +438,7 @@ def test_actor_side_algorithm_select_action_and_load_weights():
"""Simulate actor: create algorithm without optimizers, select_action, load_weights."""
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.rl.algorithms.sac import SACAlgorithm, SACAlgorithmConfig
state_dim = 10
action_dim = 6
@@ -454,7 +455,7 @@ def test_actor_side_algorithm_select_action_and_load_weights():
# Actor side: no optimizers
policy = GaussianActorPolicy(config=sac_cfg)
policy.eval()
algorithm = make_algorithm(policy=policy, policy_cfg=sac_cfg, algorithm_name="sac")
algorithm = make_algorithm(cfg=SACAlgorithmConfig.from_policy_config(sac_cfg), policy=policy)
assert isinstance(algorithm, SACAlgorithm)
assert algorithm.optimizers == {}