tangger/lerobot
1
0
Fork 1
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

chore: replace hard-coded action values with constants throughout all the source code (#2055)

Browse Source 
* chore: replace hard-coded 'action' values with constants throughout all the source code

* chore(tests): replace hard-coded action values with constants throughout all the test code
This commit is contained in:
Steven Palma
2025-09-26 13:33:18 +02:00
committed by GitHub
parent 9627765ce2
commit d2782cf66b
47 changed files with 269 additions and 255 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
tests/policies/test_sac_policy.py
View File

@@ -23,7 +23,7 @@ from torch import Tensor, nn
from lerobot.configs.types import FeatureType, PolicyFeature
from lerobot.policies.sac.configuration_sac import SACConfig
from lerobot.policies.sac.modeling_sac import MLP, SACPolicy
from lerobot.utils.constants import OBS_IMAGE, OBS_STATE
from lerobot.utils.constants import ACTION, OBS_IMAGE, OBS_STATE
from lerobot.utils.random_utils import seeded_context, set_seed
try:
@@ -105,7 +105,7 @@ def create_default_train_batch(
batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
) -> dict[str, Tensor]:
return {
"action": create_dummy_action(batch_size, action_dim),
ACTION: create_dummy_action(batch_size, action_dim),
"reward": torch.randn(batch_size),
"state": create_dummy_state(batch_size, state_dim),
"next_state": create_dummy_state(batch_size, state_dim),
@@ -117,7 +117,7 @@ def create_train_batch_with_visual_input(
batch_size: int = 8, state_dim: int = 10, action_dim: int = 10
) -> dict[str, Tensor]:
return {
"action": create_dummy_action(batch_size, action_dim),
ACTION: create_dummy_action(batch_size, action_dim),
"reward": torch.randn(batch_size),
"state": create_dummy_with_visual_input(batch_size, state_dim),
"next_state": create_dummy_with_visual_input(batch_size, state_dim),
@@ -182,13 +182,13 @@ def create_default_config(
config = SACConfig(
input_features={OBS_STATE: PolicyFeature(type=FeatureType.STATE, shape=(state_dim,))},
output_features={"action": PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))},
output_features={ACTION: PolicyFeature(type=FeatureType.ACTION, shape=(continuous_action_dim,))},
dataset_stats={
OBS_STATE: {
"min": [0.0] * state_dim,
"max": [1.0] * state_dim,
},
"action": {
ACTION: {
"min": [0.0] * continuous_action_dim,
"max": [1.0] * continuous_action_dim,
},